{"CACHEDAT":"2026-05-13 09:33:19","TRANSLATEDAT":"2026-05-13 09:33:19","SOURCESIGNATURE":"e60b88bd6d7b3c71f7e12fe57b14f89a9e6878cc2949671a0bb004fa9e4abe88","SLUG":"information-circulation-visibility-7ESlNDSxYa","MARKDOWN":"# Information Visibility & Prominence\n\nInformation Visibility refers to whether — and how prominently — specific information items, topics, sources, or accounts appear to users in digital environments. Information that exists in a platform's index or network does not automatically reach all users, nor reach them in the same way. \n\nVisibility is the outcome of two interacting forces: \n\n* how information is circulated (sharing, redistribution, spread) and \n* how it is surfaced prominently, pushed down, or filtered out.\n\n\n:::warning\n**Distinguishing reliability and visibility is essential for information literacy.**\n\n* **Reliability** depends on who created an item (author) and how it was edited (editorial review) — not on whoever shared it, on the environment it appeared in, or on how prominently it was surfaced.\n* **Visibility** depends on who shares an item, on the mechanisms that surface it, and on the practices through which it is amplified. Visibility is not a measure of reliability.\n\nFor example, a retweeted article: the account that shared it and the platform that surfaced it influenced its visibility — but the article has its own author and editorial history, whose reliability must be assessed separately.\n\n:::\n\n# Information Circulation: Sharing, Forwarding, Linking, Reposting, Quoting\n\nPeople, accounts (including bots), and organisations circulate existing information items by sharing, forwarding, linking, reposting, quoting, or otherwise redistributing them.\n\n→ When an item is passed on with added commentary, interpretation, or reframing, a new information item is created with a new author.\n\n# Source-Driven Information Promotion & Visibility\n\nPractices initiated by sources — those who create, publish, or promote information items. Sources include individual users, content creators, organisations, advertisers, and website operators. Unlike platform-driven mechanisms, these practices are driven by the sources themselves, who decide how to make their content visible. They operate in two modes: by adapting content to the platforms' ranking systems (Reach, Direct Addressing, SEO, SMO, Platform-specific Optimisation), or by paying directly for placement (Paid Placements).\n\n## Account Reach\n\nThe size and structure of a sharer's potential audience.\n\nSeveral factors determine reach:\n\n* #### follower or subscriber count\n* #### verification status \n* #### account standing: age, engagement history, platform reputation\n\nReach affects visibility in two ways:\n\n* *directly*: items shared by high-reach accounts appear in more feeds at the moment of sharing\n* *indirectly*: high-reach accounts generate more engagement signals, which platform algorithms then use to elevate items in ranking (→ Algorithmic Gatekeeping)\n\n→ Reach varies widely: a private account with 100 followers and a public account with one million followers operate at fundamentally different scales of influence on visibility.\n\n## Direct Addressing\n\nMechanisms by which sharers target specific recipients, producing immediate prominence for those users through platform notifications.\n\nExamples:\n\n* #### @mentions\n* #### tagging (in photos, posts, or threads)\n* #### quote-tweets & reply-mentions\n* #### group direct messages\n* #### mailing-list addressing (To, CC)\n\n→ Direct addressing differs from regular sharing: the targeted user receives the item directly via notification, regardless of whether they would otherwise have encountered it through their feed.\n\n→ Direct addressing is a hybrid mechanism. It operates user-side, but functions through platform infrastructure (notification systems). Its effect is immediate prominence for the addressed user.\n\n## Search Engine Optimisation (SEO)\n\nSource-side practices to adapt **websites** — their content, metadata, and link structure — so that they rank more prominently in **general-purpose search engine results** (Google, Bing, DuckDuckGo, etc.).\n\nCommon practices include:\n\n* keyword research and integration in titles, headings, body text\n* metadata optimisation (title tags, meta descriptions, alt text)\n* link building (acquiring inbound links from authoritative sites)\n* site structure and internal linking\n* page speed and mobile-friendliness\n* producing content that matches search intent\n\n→ SEO is the most formalised optimisation discipline because search engine ranking signals are relatively stable and well-documented (Lewandowski et al.). Specialised SEO professionals, agencies, and tools support its practice.\n\n→ SEO does not change how search engines rank pages — it adapts the website to fit existing ranking criteria. Publishers can shape what the algorithm sees, not how it decides.\n\n## Social Media Optimisation (SMO)\n\nSource-side practices to maximise visibility, engagement, and shareability of content on social media platforms.\n\nCommon practices include:\n\n* hashtag strategies (trending or topic-specific tags)\n* posting timing (when target audiences are active)\n* content format choices (short video, carousels, reels)\n* headline and hook design (catching attention quickly)\n* encouraging engagement (questions, polls, calls to action)\n* cross-platform repurposing of content\n\n→ SMO is less formalised than SEO because social media ranking signals are more opaque and platform-specific. Practices shift as algorithms change.\n\n## Platform-Specific Optimisation\n\nOptimisation strategies tailored to the conventions and ranking logics of individual platforms — beyond general SEO or SMO principles.\n\n* TikTok: hooking viewers in the first three seconds, using trending sounds, vertical short-form video\n* Instagram: high-quality visuals, Reels-first strategy, hashtag mixing\n* YouTube: thumbnail design, watch-time optimisation, keyword-rich titles and descriptions\n* LinkedIn: long-form professional posts, native publishing, networked engagement\n* X (Twitter): concise hooks, threads, replying to high-reach accounts\n\n→ Platform-specific optimisation requires understanding each platform's ranking system, audience behaviour, and content format preferences. What works on TikTok rarely works on LinkedIn.\n\n## Paid Placements & Advertising\n\nSource-side practice of paying for visibility — sponsored content placed alongside organic content, typically through advertising.\n\nCommon forms:\n\n* sponsored search results (search engine ads)\n* sponsored posts and promoted content (social media)\n* display ads (banners, videos)\n* influencer partnerships (paid collaborations)\n\n→ Paid placements bypass organic ranking systems: instead of optimising content to rank well, the source pays the platform directly for placement.\n\n→ They are sometimes clearly labelled (\"Sponsored\", \"Ad\"), sometimes only weakly distinguishable from organic results. Labelling standards vary by jurisdiction and platform. \n\n# Platform-Side Information Promotion & Gatekeeping\n\n\n:::warning\nWhat users actually see is rarely the product of a single mechanism. In a search engine, an algorithmically ordered list of organic results is presented alongside paid placements, AI-generated summaries, and sometimes editorial highlights — and the underlying ranking signals can be deliberately influenced through *Search Engine Optimisation*. In a social media feed, algorithmically ranked posts appear next to sponsored content, recommended accounts, and trending overlays. Each component follows its own logic and contributes to a composite visibility outcome.\n\n:::\n\n## Editorial Curation\n\nPlatform-side manual curation: information items deliberately featured by editorial teams or platform operators rather than surfaced through algorithmic ranking.\n\nExamples:\n\n* featured Snippets in search results\n* curated trending sections\n* editor-selected stories in news aggregators\n* platform-promoted hashtags\n* Editor's Picks in app stores\n* featured creators or accounts\n\n→ Editorial highlights sit alongside the algorithmic mechanisms and reflect the platform's own judgements about which content deserves prominent display.\n\n→ Unlike algorithmic gatekeeping (curation and personalisation), editorial gatekeeping involve human editorial choices by the platform itself. Functionally, this is a form of *Editorial Gatekeeping* ) — performed by the platform rather than by traditional publishers.\n\n## Algorithmic Gatekeeping\n\n**Algorithmic Gatekeeping** refers to the role of algorithms in deciding which information items reach which users — the digital counterpart to *Editorial Gatekeeping* (→ Information, Sources & Information Environments → Editorial Review). It involves both **selection** (what is surfaced and ranked highly) and **exclusion** (what is filtered, demoted, or hidden).\n\nAlgorithmic gatekeeping operates across different platform types:\n\n* in **search engines**, algorithms select and order results based on queries\n* in **social media feeds**, algorithms decide which posts appear more prominently\n* in **video platforms**, algorithms suggest what to watch next\n* in **AI-based answer systems**, algorithms generate, summarise, or synthesise responses\n\nAlgorithmic gatekeeping operates in two modes that often work together: general operations applied across all users (*Algorithmic Curation*), and individual tailoring based on tracked user signals (*Algorithmic Personalisation*).\n\n### Algorithmic Curation\n\nGeneral algorithmic operations applied across users — they shape what information is available on the platform, regardless of who the user is.\n\n* **Crawling and Indexing** — *Which information items become available for display?*\n  * search engines crawling the web\n  * content aggregators indexing news sources\n  * app stores cataloguing available apps\n* **Filtering and Moderation** — *Which items are blocked or down-ranked under platform rules?*\n  * spam filters\n  * removal of policy-violating content (hate speech, illegal content, graphic material)\n  * down-ranking of low-quality or misleading material\n* **Quality Scoring** — *Which sources or items are evaluated as more credible or higher-quality?*\n  * search engines penalising low-quality sites\n  * news aggregators ranking by source authority\n  * peer-review-influenced ranking on academic search engines\n* **Trending Detection** — *Which items are surfaced as currently popular?*\n  * trending topics on social platforms\n  * top charts on streaming services\n  * \"What's happening\" and \"Today's headlines\" sections\n  * popular hashtags\n\n→ Algorithmic curation defines the *pool* of information available on the platform. It largely operates the same way for all users.\n\n### Algorithmic Personalisation\n\nAlgorithmic operations that adapt the selection, order, and presentation of information to individual users based on their tracked signals. These signals accumulate over time into user histories that algorithms draw on.\n\n→ Two users on the same platform — even with the same query — typically see substantially different content.\n\n* **Personalised Ranking** — *Which items are ordered higher for this user?*\n  * personalised search results (location, history, profile shape ordering)\n  * social media feed ordering (\"For You\" feeds, \"Top posts\")\n  * engagement-based ranking — optimisation for predicted interaction, dominant on social media\n* **Recommendations** — *Which items are suggested to this user beyond what they actively requested?*\n  * \"Recommended for you\" video lists\n  * suggested accounts, groups, or topics to follow\n  * \"People you may know\"\n  * related articles, similar products, \"Up next\"\n* **Personalised Advertising** — *Which adverts are targeted to this user?*\n  * search ads tailored to past queries\n  * social media sponsored posts based on profile and behaviour\n  * retargeted display ads on websites\n  * influencer partnerships matched to audience interests\n\n→ Two users on the same platform — even with the same query — typically see substantially different content.\n\n→ Personalisation creates a **feedback loop**: what users do affects what they see next, and what they see next can influence what they do.\n\n#### ☑ User Signals Tracked by the Platform for Algorithmic Personalisation\n\nActions a user performs — actively or passively — within an information channel that may be tracked and used by algorithms to personalise the selection and visibility of information items.\n\n→ User actions are not limited to deliberate interactions such as clicking or liking. Many actions are passive or automatic, such as how long a user stays on a page, how far they scroll, or where they are located. Users are often unaware that these actions influence what they encounter next.\n\n| Type | What it is | Examples |\n|------|------------|----------|\n| **Explicit feedback** | Deliberate interactions the user chooses to perform | - searches / search queries<br>- clicks<br>- likes / reactions<br>- comments / replies<br>- shares / reposts / forwards<br>- follows / subscribes<br>- saves / bookmarks<br>- ratings / reviews<br>- purchases / downloads |\n| **Implicit behaviour** | Passive behavioural signals captured during use | - watch time / listen time / dwell time<br>- scroll behaviour (how far, how fast)<br>- hover behaviour<br>- skip behaviour |\n| **Contextual data** | Information about the situation in which the user is accessing the platform | - user's location data while using the application<br>- device type (e.g., phone or laptop)<br>- time of access |\n| **Account and social data** | Information from the user's profile and social connections | - profile information (age, interests, profession, gender)<br>- language settings<br>- linked accounts<br>- contact list / address book |\n\n\n:::info\n* Adomavicius, G., & Tuzhilin, A. (2011). Context-aware recommender systems. In F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor (Eds.), *Recommender Systems Handbook* (pp. 217–253). Springer. <https://doi.org/10.1007/978-0-387-85820-3_7>\n* Kelly, D., & Teevan, J. (2003). Implicit feedback for inferring user preference: A bibliography. *ACM SIGIR Forum, 37*(2), 18–28. <https://doi.org/10.1145/959258.959260>\n* Li, W., Kuo, J.-C., Sheng, M., Zhang, P., & Wu, Q. (2025). Beyond explicit and implicit: How users provide feedback to shape personalized recommendation content. In *Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25)*. Association for Computing Machinery. <https://doi.org/10.1145/3706598.3713241>\n* Narayanan, A. (2023). *Understanding social media recommendation algorithms.* Knight First Amendment Institute, Columbia University. <https://knightcolumbia.org/content/understanding-social-media-recommendation-algorithms>\n\n:::\n\n# Information Amplification\n\nAmplification refers to the systematic boosting of an item's visibility beyond the individual-user level — to produce broad visibility across user accounts, and sometimes across information environments. \n\nWhere *Source-Driven Promotion* (above) covers what a single source itself does to gain visibility, and *Platform-Side Gatekeeping* (above) describes the algorithmic operations through which platforms surface and rank content for individual users, *Amplification* refers to the resulting *boost outcomes at scale* — produced either as the aggregate effect of those platform operations (→ *Algorithmic Amplification*) or through coordinated activity by multiple actors (→ *Coordinated Amplification*).\n\n## Amplification Mechanisms\n\nAmplification operates through two principal mechanisms.\n\n* **Algorithmic Amplification** is *platform-driven*: it is the aggregate effect of *Algorithmic Gatekeeping* — the cumulative outcome of platform curation and personalisation on which items reach which users and how prominently.\n* **Coordinated Amplification** is *actor-driven*: multiple accounts, groups, or campaigns deliberately act in concert to boost the visibility of an item, hashtag, or narrative beyond what individual user activity would produce. The literature classifies it along two dimensions — the **coordination** (transparent or concealed) and the **accounts** (real or fake) — and distinguishes accordingly (Rogers & Righetti, 2025):\n  * **Coordinated Authentic Amplification**: coordination is transparent and accounts are real (e.g. open civic campaigns, advocacy, marketing).\n  * **Coordinated Inauthentic / Artificial Amplification**: coordination is concealed, accounts are fake, or both — manufacturing an appearance of organic support (Meta's *Coordinated Inauthentic Behaviour* / CIB; Gleicher, 2018).\n\nThe two mechanisms frequently combine. Coordinated networks exploit engagement-based ranking to trigger algorithmic boosts; algorithmic ranking, in turn, compounds whatever visibility coordination has already produced.\n\n* \n:::info\n  * Gleicher, N. (2018). *Coordinated Inauthentic Behavior Explained*. Meta Newsroom. <https://about.fb.com/news/2018/12/inside-feed-coordinated-inauthentic-behavior/>\n  * Rogers, R., & Righetti, N. (2025). Coordinated inauthentic behaviour on Facebook? A typology of manufactured attention. <https://doi.org/10.1177/29768624251369784>\n\n  :::\n\n### Algorithmic Amplification\n\nAlgorithmic amplification is the cumulative effect of the gatekeeping mechanisms above (Curation and Personalisation): the systematic shaping of which items, topics, accounts, and formats appear prominently to users — and which are filtered, demoted, or pushed down.\n\nEmpirical research shows that engagement-based ranking systematically amplifies emotionally charged and out-group hostile content, even when users themselves do not prefer such content (Milli et al., 2025). It also compounds existing reach: accounts and items with high prior engagement are rewarded with further visibility, producing highly skewed reach distributions (rich-get-richer effect).\n\nAlgorithmic interventions can have nonlinear effects in the opposite direction as well. A reduction of around 20% in an item's feed prominence can cut its reach by an order of magnitude (Narayanan, 2023).\n\nAlgorithmic amplification is not a neutral reflection of user activity. Its effects are emergent and visible primarily in the aggregate: individual recommendations are imprecise (engagement rates remain below 1% on most platforms), but ranking, recommendation, and demotion systematically shape what circulates across the platform.\n\n\n:::info\n* Milli, S., et al. (2025). Engagement, user satisfaction, and the amplification of divisive content on social media. PNAS Nexus.\n\n\n* Narayanan, A. (2023). Understanding social media recommendation algorithms. Knight First Amendment Institute.\n\n:::\n\n### Coordinated Authentic Amplification\n\nCoordinated Authentic Amplification is the deliberate boosting of an information item, topic, hashtag, account, or narrative through openly disclosed, organised activity by real accounts. The coordinated origin is not concealed: participants act under their real identities or under known group affiliations.\n\nTypical contexts include \n\n* civic campaigns (e.g. NGO petitions, advocacy hashtags), \n* political mobilisation (e.g. party campaigning, get-out-the-vote efforts), \n* marketing and brand campaigns, \n* professional association communications, and \n* cultural movements such as Fridays for Future or #MeToo.\n\n\n:::warning\nWhether the underlying message is well-founded, balanced, or one-sided is a separate question — *authenticity* refers only to the transparency of the coordination, not to the truth-value or fairness of the content. An authentic campaign can amplify accurate information, misleading information, or a one-sided position.\n\n:::\n\n\n:::warning\nAuthentic and inauthentic coordination can produce visibility patterns that look identical from the outside — synchronised sharing, hashtag clustering, rapid uptake. The distinguishing feature is not the visible pattern but whether the coordinated origin is openly disclosed.\n\n:::\n\n### Coordinated Inauthentic / Artificial Amplification\n\nCoordinated Inauthentic / Artificial Amplification is the deliberate boosting of an information item, topic, hashtag, account, or narrative through organised activity in which the coordinated origin is concealed, the participating accounts are fake, or both. The aim is to manufacture an appearance of organic, independent support. Meta's term *Coordinated Inauthentic Behaviour* (CIB) — now incorporated into the EU Digital Services Act — centres on this combination of false identities and adversarial methods to evade detection (Gleicher, 2018; Rogers & Righetti, 2025).\n\nTypical contexts include political influence operations (state-sponsored or party-aligned), astroturfing campaigns (commercial or ideological), targeted disinformation around elections, public health, or geopolitical conflict, and reputation manipulation through fake reviews, ratings, or engagement. The operational means — *bots*, *trolls*, *sockpuppets*, and their coordinated networks (*bot farms*, *troll farms*, *sockpuppet networks*, *click farms*) — are described in detail below.\n\n\n:::warning\n*Inauthenticity* refers to the concealment of the coordinated origin or the use of fake accounts — not to the truth-value of the content being amplified. A coordinated network of fake accounts can amplify accurate information; a single authentic individual can spread fabricated information. Coordinated inauthentic amplification and the spread of false content are distinct phenomena that can occur independently or together.\n\n:::\n\nThe following account types described in this section apply across Digital Information Channels & Platforms where users can create accounts and post or interact publicly — particularly social media, discussion forums and community spaces, video and audio platforms, and review or comment sections. They are less prominent in private communication apps or in environments without user-generated content.They appear both independently and within coordinated networks. They are listed here because of their typical role in amplification dynamics; the explicitly coordinated formations are the Account Networks.\n\n| Term | Definition | Controlled by | Defined by | Typical purpose |\n|------|------------|---------------|------------|-----------------|\n| **Social Bot** | An automated or partly automated account that posts, likes, follows, shares, or replies online. | Software      | **Automation** | To amplify messages, create artificial popularity, spam, influence debate, or spread content at scale. |\n| **Cyborg** | A hybrid account combining human operation with software automation. | Mixed: human and software | **Selective automation** | To combine the scale of automation with the contextual plausibility of human input — for legitimate scheduling/management or for harder-to-detect influence operations. |\n| **Troll** | A person or account that deliberately provokes, disrupts, or inflames online discussion. | Usually a human user; sometimes coordinated groups | **Disruptive / provocative / antagonistic behaviour** | To upset others, derail conversations, provoke reactions, spread hostility, or polarise debate. |\n| **Sockpuppet** | A fake account used by someone to hide their real identity or pretend to be a different person. | A human user, though the account may also use automation | **Deceptive identity** | To create false support, attack others anonymously, evade bans, manipulate debate, or give the impression of independent agreement. |\n\n#### Social Bot\n\nA **social bot** is a bot designed to operate on social media platforms, posting, commenting, sharing, or interacting in ways that simulate human users. Social bots are typically programmed to act at scale and at high speed, far beyond what a human user could manage. Their activity is often repetitive and coordinated across many accounts, which distinguishes it from normal human use.\n\nSocial bots can be used for legitimate purposes — such as customer service, news distribution, or marketing — but they are also widely used to influence public opinion, amplify certain messages, manipulate discussions, or manufacture the appearance of widespread support for specific ideas, products, or causes. In the context of misinformation and disinformation, social bots play a particular role in spreading content rapidly and giving the false impression that many independent voices share the same view.\n\nWhen social bots are deployed in coordinated networks, they form a *Bot Farm*.\n\n\n:::info\n#### Bot\n\nA bot is a computer programme that automatically performs tasks, often repetitive ones. Bots range from simple, harmless tools — such as web crawlers that index pages for search engines, automated testing systems, or chatbots that answer routine customer questions — to malicious programmes designed to spread spam, malware, or disinformation.\n\n:::\n\n#### Cyborg\n\nA **cyborg** is a hybrid account that is partly operated by a human and partly automated by software. A cyborg may have routine posts scheduled or generated by software while a person handles selected interactions, replies, or sensitive content. The balance between automated and human activity varies between accounts.\n\nCyborgs can be used for legitimate purposes — such as content scheduling, brand or institutional account management, or hybrid customer service — but they are also used in influence operations to combine the scale and speed of automation with the contextual plausibility of human input.\n\nCyborgs are more difficult to identify than purely automated bots because part of their behaviour is genuinely human, which means single detection indicators rarely suffice for reliable identification.\n\n#### Troll\n\nA **troll** is a real person who deliberately disrupts online discussions through provocative, aggressive, or hostile behaviour. Trolls typically use personal accounts and target controversial issues, public figures (such as politicians or journalists), or media organisations. Their aim is to upset others, trigger reactions, or escalate conflicts — sometimes in support of a particular agenda, sometimes for entertainment or attention.\n\nWhile trolls often act independently, they may also operate in coordinated groups, sometimes paid by political or commercial actors (see *Troll Farm* under Mechanisms of Amplification).\n\n**Trolling is best understood as a pattern of online behaviour, not a specific kind of account.** The same behaviour can be performed by automated accounts, and ordinary users can engage in trolling on occasion.\n\n#### Sockpuppet\n\nA **sockpuppet** is a fake online identity created and operated by a real person who hides their true identity. Unlike trolls — who often act under a single openly hostile account — a sockpuppet operator typically runs multiple fake accounts in parallel to create the impression that several independent users hold the same opinion, support the same cause, or agree with the operator's own (often separate) main account.\n\nSockpuppets are commonly used to manufacture artificial consensus, support one's own arguments under different names, attack opponents while appearing impartial, evade bans by creating new identities after suspension, or manipulate online reviews, votes, and polls.\n\nSockpuppets differ from social bots in that they are manually operated by humans, which makes their content more contextually plausible and harder to detect through automated means. They differ from trolls in that their primary goal is deception about identity and the manufacturing of apparent consensus, not provocation — although sockpuppet operators can also engage in trolling behaviour through their fake identities.\n\nWhen a person or small group operates a coordinated set of sockpuppets together, they form a *Sockpuppet Network* (see Mechanisms of Amplification).\n\n#### ☑ Differentiating Social Bots, Trolls, and Sockpuppets\n\n| **Detection Dimension** | **Social Bots** | **Trolls** | **Sockpuppets** |\n|---------------------|-------------|--------|-------------|\n| **Profile Characteristics** | - [ ] The account looks newly created <br> - [ ] The profile is incomplete or generic <br> - [ ] The username may look non-personal and sometimes include random numbers | - [ ] The account has typically been active for longer and has a post history <br> - [ ] The profile is complete and seems personal; it may present strong ideological or political self-description <br> - [ ] The username looks personal | - [ ] The profile looks plausible and personal, often with a profile picture and biographical details (sometimes stolen, AI-generated, or copied) <br> - [ ] Account history may be moderate and designed to look authentic over time |\n| **Posting Behaviour** | - [ ] The activity does not match normal human online behaviour <br> - [ ] The accounts post or repost content very frequently <br> - [ ] The accounts post or repost content at all hours, day and night | - [ ] The activity resembles normal human online behaviour <br> - [ ] The account posts or replies at irregular times <br> - [ ] The account becomes more active during controversial discussions | - [ ] Activity patterns resemble normal human use <br> - [ ] Multiple accounts run by the same operator may show similar active hours or rhythms <br> - [ ] Sockpuppets tend to start fewer discussions and write shorter posts than typical users |\n| **Interactions**    | - [ ] The account does not have real conversations <br> - [ ] The accounts mostly like, share, or repost <br> - [ ] Replies are short and automated | - [ ] The account replies directly to other users <br> - [ ] The account engages in debates with the purpose of provoking reactions <br> - [ ] Conversations are extended to create or escalate conflict | - [ ] The account engages in real conversations, often supporting the operator's main account or other sockpuppets <br> - [ ] Replies are contextually appropriate and seem authentic <br> - [ ] Pairs of sockpuppets often interact in the same discussion at similar times |\n| **Content Features** | - [ ] The content is one-sided and repetitive <br> - [ ] The same narratives are posted many times | - [ ] The content is specifically tailored to harm or provoke a target <br> - [ ] The content targets individuals or social groups | - [ ] Content seems genuine and varied across accounts <br> - [ ] The underlying message or stance aligns suspiciously across the network <br> - [ ] More frequent use of personal pronouns such as \"I\" |\n| **Language**        | - [ ] Generic expressions, repetitive phrasing with keywords | - [ ] Varied, emotional, often abusive or offensive language | - [ ] Natural and varied language <br> - [ ] Multiple accounts may share linguistic fingerprints (similar phrasing, vocabulary, punctuation, or error patterns) |\n| **Network & Technical Indicators** | - [ ] Social bots follow other social bots, but the relationship is typically one-way and not reciprocal <br> - [ ] Coordinated behaviour is observable across multiple bot accounts | - [ ] Trolls follow human accounts <br> - [ ] Connections are often reciprocal (they follow their followers and vice versa) <br> - [ ] Trolls typically act independently of each other | - [ ] Multiple accounts engaging with each other in mutually supportive ways <br> - [ ] Connections may be artificially reciprocal between sockpuppets in the same network, or deliberately absent to avoid detection <br> - [ ] Same IP address, device fingerprint, or login pattern \\\\\\*(platform-side detection)\\\\\\* <br> - [ ] More clustered ego-networks than ordinary users <br> - [ ] Correlated activity timing across accounts |\n\n\n\n:::info\n* Ferrara, E. (2023). Social bot detection in the age of ChatGPT: Challenges and opportunities. *First Monday, 28*(6). <https://doi.org/10.5210/fm.v28i6.13185>\n* Kumar, S., Cheng, J., Leskovec, J., & Subrahmanian, V. S. (2017). An army of me: Sockpuppets in online discussion communities. *Proceedings of the 26th International Conference on World Wide Web (WWW '17)*, 857–866. <https://doi.org/10.1145/3038912.3052677>\n* Orabi, M., Mouheb, D., Al Aghbari, Z., & Kamel, I. (2020). Detection of bots in social media: A systematic review. *Information Processing & Management, 57*(4), 102250. <https://doi.org/10.1016/j.ipm.2020.102250>\n* Solorio, T., Hasan, R., & Mizan, M. (2013). A case study of sockpuppet detection in Wikipedia. *Proceedings of the Workshop on Language Analysis in Social Media (LASM) at NAACL-HLT*, 59–68. Association for Computational Linguistics. <https://aclanthology.org/W13-1107/>\n* Tomaiuolo, M., Lombardo, G., Mordonini, M., Cagnoni, S., & Poggi, A. (2020). A survey on troll detection. *Future Internet, 12*(2), <https://doi.org/10.3390/fi12020031>\n* Tsikerdekis, M., & Zeadally, S. (2014). Multiple account identity deception detection in social media using nonverbal behavior. *IEEE Transactions on Information Forensics and Security, 9*(8), 1311–1321. <https://doi.org/10.1109/TIFS.2014.2332820>\n* Uyheng, J., Moffitt, J. D., & Carley, K. M. (2022). The language and targets of online trolling: A psycholinguistic approach for social cybersecurity. *Information Processing & Management, 59*(5), 103012. <https://doi.org/10.1016/j.ipm.2022.103012>\n\n:::\n\n#### Account Networks\n\n#### Bot Farm\n\nA **bot farm** is a network of bots operating simultaneously across multiple devices or servers, deployed by a single operator or organisation for a particular purpose.\n\nBot farms have a range of legitimate uses, including web indexing, automated software testing, data aggregation, and website performance monitoring. However, they are also commonly used for malicious activities such as creating fake engagement, generating large volumes of content, distributing spam, or carrying out cybersecurity attacks. When used to manipulate online discourse, bot farms can create the false impression of widespread support, opposition, or interest in a topic, account, or campaign.\n\n#### Troll Farm\n\nA **troll farm** is an organised group of coordinated, often paid workers who post deliberately provocative, misleading, or false content online — typically through fake accounts. Their aim is usually to manipulate public opinion, spread disinformation, or create social and political unrest. Troll farms have been documented in connection with state-sponsored influence operations as well as commercial reputation manipulation.\n\n#### Sockpuppet Network\n\nA sockpuppet network is a coordinated set of sockpuppet accounts operated by one person or a small group, used to simulate independent voices supporting a shared narrative, campaign, account, or cause. Sockpuppet networks are commonly used in political astroturfing, review and rating manipulation, and coordinated disinformation campaigns. Unlike bot farms, sockpuppet networks rely on manual operation by humans, which makes the content of individual accounts appear more authentic and harder to detect through automated means. Their coordination usually becomes detectable only when multiple accounts can be linked through behavioural patterns, shared technical signals, or mutual engagement.\n\n#### Click Farm\n\nA **click farm** is an operation where large numbers of low-paid workers, automated bots, or both are used to click on ads, follow social media accounts, like posts, leave reviews, or download apps. The goal is to artificially boost online engagement or traffic, making content, accounts, or products appear more popular than they actually are.\n\n## Phenomena\n\n#### ☑ Virality vs. Trending\n\n| Feature | Virality | Trending |\n|---------|----------|----------|\n| **What is being spread** | A single information item: a specific video, post, image, or other piece of content | A topic, hashtag, sound, format, or discussion cluster: not one specific item, but many posts referring to or using the same thing |\n| **Primary drivers** | Users share, repost, or forward the information item to others, who in turn pass it along; this cascading spread can be further amplified by recommendation algorithms | Many users post about, mention, or use the same topic, hashtag, or format within a short time; the platform detects this concentration of activity and highlights it in a dedicated \"Trending\" section (such as a trending topics list, trending hashtag overview, or trending sounds page) |\n| **Time pattern** | Often short and explosive; may recur later | Time-bound; persists as long as activity stays high or the platform keeps surfacing it |\n| **How it can be manipulated** | Coordinated sharing, bot amplification, artificial engagement directed at the specific information item | Coordinated posting campaigns, manufactured fake trends through bot networks, platform decisions to promote, filter, or suppress |\n\n*Both virality and trending can emerge organically or be artificially amplified through coordinated campaigns, bot activity, or platform decisions. Both can also give an advantage to emotionally arousing, morally charged, or divisive content, especially in political or conflict-oriented contexts.*\n\n### Virality\n\nThe pattern by which a specific information item spreads rapidly through sharing, recommendation, and re-circulation across networks, analogous to the way a virus propagates. Virality is shaped by content characteristics, social network structures, platform affordances, timing, and algorithmic amplification. \n\nContent that evokes high-arousal emotions, moral reactions, or out-group animosity is often more likely to be shared, especially in political or conflict-oriented contexts. However, virality is not determined only by the size of the original source: smaller accounts or outlets can also produce highly viral items. \n\nVirality can emerge organically, but it can also be artificially amplified through coordinated sharing, platform manipulation, or bot activity.\n\n### Trending\n\nA platform-assigned status indicating that a topic, hashtag, sound, format, or discussion cluster has received unusually concentrated activity within a short period. \n\nTrending is identified algorithmically and surfaced through platform features such as X / Twitter Trending Topics, trending hashtags, trending sounds, trending challenges, or other platform-specific trend features. Trending depends on platform-specific signals such as post volume, rate of increase, engagement, location, personalisation, and moderation filters. \n\nTopics that generate high engagement — including divisive, emotionally arousing, or morally charged topics — may be more likely to trend, but this depends on the platform's ranking system and moderation rules. \n\nTrending can emerge organically from many independent contributions, but it can also be influenced by coordinated campaigns, bot activity, or platform decisions about what to promote, filter, moderate, or suppress.\n\n\n:::info\n* **Berger, J. (2013).** *Contagious: Why Things Catch On.* New York: Simon & Schuster.\n* **Berger, J., & Milkman, K. L. (2012).** What makes online content viral? *Journal of Marketing Research*, 49(2), 192–205. <https://doi.org/10.1509/jmr.10.0353>\n* **Brady, W. J., McLoughlin, K., Doan, T. N., & Crockett, M. J. (2021).** How social learning amplifies moral outrage expression in online social networks. *Science Advances*, 7(33), eabe5641. <https://doi.org/10.1126/sciadv.abe5641>\n* **Jenkins, H., Ford, S., & Green, J. (2013).** *Spreadable Media: Creating Value and Meaning in a Networked Culture.* New York: NYU Press.\n* **Lee, J., & Umback, J. (2026).** The viral turn: rethinking virality in the creator economy on TikTok. *Continuum*, 1–26. <https://doi.org/10.1080/10304312.2026.2625794>\n* **Maarouf, A., Pröllochs, N., & Feuerriegel, S. (2024).** The virality of hate speech on social media. *Proceedings of the ACM on Human-Computer Interaction*, 8 (CSCW1), 1–22. <https://doi.org/10.1145/3641025>\n* **Rathje, S., Van Bavel, J. J., & van der Linden, S. (2021).** Out-group animosity drives engagement on social media. *Proceedings of the National Academy of Sciences*, 118(26), e2024292118. <https://doi.org/10.1073/pnas.2024292118>\n* **Rathje, S., & Van Bavel, J. J. (2025).** The psychology of virality. *Trends in Cognitive Sciences*, 29(10), 914–927. <https://doi.org/10.1016/j.tics.2025.06.014>\n* **Rogers, E. M. (2003).** *Diffusion of Innovations* (5th ed.). New York: Free Press.\n* **Sangiorgio, E., Cinelli, M., Cerqueti, R., & Quattrociocchi, W. (2024).** Followers do not dictate the virality of news outlets on social media. *PNAS Nexus*, 3(7), pgae257. <https://doi.org/10.1093/pnasnexus/pgae257>\n* **Schlessinger, J., Garimella, K., Jakesch, M., & Eckles, D. (2023).** Effects of Algorithmic Trend Promotion: Evidence from Coordinated Campaigns in Twitter's Trending Topics. *Proceedings of the International AAAI Conference on Web and Social Media (ICWSM)*, 17(1), 777–786. <https://doi.org/10.1609/icwsm.v17i1.22187>\n* **Schöne, J. P., Parkinson, B., & Goldenberg, A. (2021).** Negativity spreads more than positivity on Twitter after both positive and negative political situations. *Affective Science*, 2(4), 379–390. <https://doi.org/10.1007/s42761-021-00057-7>\n\n:::\n\n### Spill-Over Effects & Epistemic Laundering\n\nThe process by which information that gains visibility within one information environment — whether through artificial amplification, trending, or editorial selection — is picked up and further distributed in other information environments or information access sytsems, thereby reaching audiences beyond the original environment. \n\nSpill-over can occur through journalistic reporting, cross-platform sharing, editorial curation, or user-driven redistribution. \n\n→ A research finding shared on a scholarly forum may be discussed on social media and summarised by an AI assistant. \\n→ A topic artificially amplified by bots on a social media platform may be picked up by journalists. \n\n→ Spill-over effects can increase the reach of both reliable and unreliable information, and can make information appear more widely established than it originally was. \n\n#### Epistemic Laundering\n\nSpill-over does not always preserve the apparent status of information. When the receiving channel carries stronger signals of authority or reliability than the channel of origin — academic format, peer review, formal publication — the information itself can be perceived as more reliable simply through having moved. This effect is known as Epistemic Laundering: information gains perceived reliability through its passage across channels, without any actual change to the underlying claims or evidence. It exploits the tendency of recipients to attribute the reliability of the channel in which they encounter information to the information itself. \n\n→ A claim originating in an anonymous blog post may be cited in a preprint, reproduced in an AI-generated answer, and finally cited in a peer-reviewed paper — at each step gaining academic surface and apparent authority, while the underlying claim remains unchanged or unverified.\n\n\n:::success\nA team at the University of Gothenburg, led by a medical researcher, invented a fake skin condition called Bixonimania to test whether AI systems would absorb and repeat medical misinformation. They presented it as a supposed condition linked to blue-light exposure from screens, with symptoms such as sore, itchy eyes and a pinkish hue on the eyelids. They then created deliberately fake academic-looking preprints, planted with obvious warning signs — a fictional author with an AI-generated photo, a non-existent university, and references to Starfleet Academy and the USS Enterprise. Nature reported that the preprints have since been removed from Preprints.org.\n\nWithin weeks, major AI chatbots began reproducing Bixonimania as a real medical condition, in some cases offering users explanatory or health-related advice. In parallel, the fake material was cited in at least one published paper, since retracted, in the Springer Nature journal *Cureus*.\n\nSpill-over: log posts → fake preprint → webcrawlers → AI chatbot answers → academic citation\n\n:::\n\n\n:::info\nStokel-Walker, C. (2026). Scientists invented a fake disease. AI told people it was real. Nature, 652(8110), 559-561. <https://doi.org/10.1038/d41586-026-01100-y>\n\n:::\n\n# Information Narrowing\n\nWhereas *Information Amplification* (above) describes how visibility is broadened across user accounts, *Information Narrowing* describes the inverse: how the range of perspectives reaching an individual user or social group becomes restricted. Two distinct mechanisms produce this narrowing — *Filter Bubble* (algorithmic personalisation) and *Echo Chamber* (user self-selection). The two are often conflated in popular discourse but operate differently.\n\n## Filter Bubble\n\nA **filter bubble** is an isolated information environment created by *Algorithmic Personalisation*, in which a user is increasingly exposed to content that aligns with their inferred preferences and past behaviour, while content that diverges is filtered out — typically without the user's awareness. The term was coined by Eli Pariser (2011) to describe how personalisation algorithms on Google, Facebook, and similar platforms can produce systematic exposure asymmetries based on user signals such as click history, location, and profile data.\n\nThe defining feature of a filter bubble is *unintentionality from the user's side*: the narrowing is generated by the platform's optimisation, not by the user's deliberate choice of sources.\n\n\n:::warning\nEmpirical research has substantially qualified Pariser's original thesis. Studies have found that algorithmic personalisation does shape what users see, but most users still encounter ideologically diverse content — partly because their own social networks include varied views, and partly because algorithms do not isolate as completely as the popular discourse suggests (Bakshy et al., 2015; Flaxman et al., 2016; Bruns, 2019). The filter-bubble effect is real but typically weaker than commonly assumed; pre-internet selective exposure (e.g., choosing newspapers or TV channels) was in many cases stronger.\n\n:::\n\n\n:::info\n* Pariser, E. (2011). *The Filter Bubble: What the Internet Is Hiding from You*. Penguin Press.\n* Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. *Science*, 348(6239), 1130–1132. <https://doi.org/10.1126/science.aaa1160>\n* Flaxman, S., Goel, S., & Rao, J. M. (2016). Filter bubbles, echo chambers, and online news consumption. *Public Opinion Quarterly*, 80(S1), 298–320. <https://doi.org/10.1093/poq/nfw006>\n* Bruns, A. (2019). *Are Filter Bubbles Real?* Polity Press.\n\n:::\n\n## Echo Chamber\n\nAn **echo chamber** is a social information environment in which a user is primarily exposed to opinions, claims, or ideologies that reinforce their existing beliefs, while dissenting views are absent, dismissed, or actively discredited. Cass Sunstein (2017) describes the political consequences: when groups insulate themselves from outside perspectives, internal beliefs intensify and become more extreme over time (group polarisation).\n\nUnlike *Filter Bubble*, which arises from algorithmic personalisation, an echo chamber results primarily from **user self-selection**: choices about whom to follow, which communities to join, which sources to trust, and which voices to dismiss. These choices are partly driven by *Confirmation Bias* — the cognitive tendency to seek out and trust information that aligns with existing beliefs. The reinforcing effect comes from the social structure itself, not from invisible algorithmic filtering.\n\nC. Thi Nguyen (2020) draws a conceptual distinction that matters for intervention:\n\n* An **epistemic bubble** is a social structure in which other relevant voices are simply *absent*. Its inhabitants do not hear opposing perspectives, but they do not actively reject them.\n* An **echo chamber** in the strict sense is a social structure in which other relevant voices are *actively discredited*. Members may hear opposing perspectives but learn to distrust their sources.\n\nAn epistemic bubble can be opened by introducing new information; an echo chamber resists correction even when external evidence is presented, because the sources of that evidence have already been delegitimised.\n\n\n:::warning\nEmpirical work suggests that strong, ideologically isolated echo chambers are less common than popular discourse implies (Cinelli et al., 2021; Guess et al., 2018), but where they exist, they can be highly resistant to correction. Mere agreement within a group is not in itself an echo chamber — the defining feature is the active exclusion or discrediting of outside perspectives.\n\n:::\n\n\n:::info\n* Sunstein, C. R. (2017). *#Republic: Divided Democracy in the Age of Social Media*. Princeton University Press.\n* Nguyen, C. T. (2020). Echo chambers and epistemic bubbles. *Episteme*, 17(2), 141–161. <https://doi.org/10.1017/epi.2018.32>\n* Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021). The echo chamber effect on social media. *PNAS*, 118(9), e2023301118. <https://doi.org/10.1073/pnas.2023301118>\n* Guess, A., Lyons, B., Nyhan, B., & Reifler, J. (2018). *Avoiding the Echo Chamber about Echo Chambers: Why Selective Exposure to Like-minded Political News Is Less Prevalent Than You Think*. Knight Foundation White Paper.\n\n:::","HTML":"<style>.scilmi-outline-content h1,.scilmi-outline-content h2{font-weight:700}.scilmi-outline-content .badge-scilmi{background:#0d6efd;color:#fff;padding:2px 8px;border-radius:99px;}</style><h1>Vidljivost i istaknutost informacija</h1>\n<p>Vidljivost informacija odnosi se na to jesu li — i koliko istaknuto — određeni informacijski elementi, teme, izvori ili računi vidljivi korisnicima u digitalnim okruženjima. Informacije koje postoje u indeksu ili mreži platforme ne dospijevaju automatski do svih korisnika, niti im dospijevaju na isti način.</p>\n<p>Vidljivost je rezultat dviju međusobno djelujućih sila:</p>\n<ul>\n<li>kako se informacije cirkuliraju (dijeljenje, redistribucija, širenje) i\n</li>\n<li>kako se ističe, potiskuje ili filtrira.\n</li>\n</ul>\n<div class=\"scilmi-callout scilmi-callout-warning\">\n<div class=\"scilmi-callout-row\" aria-label=\"Warning\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M12 20C7.58172 20 4 16.4183 4 12C4 7.58172 7.58172 4 12 4C16.4183 4 20 7.58172 20 12C20 16.4183 16.4183 20 12 20ZM12 15C12.5523 15 13 15.4477 13 16C13 16.5523 12.5523 17 12 17C11.4477 17 11 16.5523 11 16C11 15.4477 11.4477 15 12 15ZM12 14C13 14 13 13 13 13L13 10.5L13 8C13 8 13 7 12 7C11 7 11 8 11 8L11 13C11 13 11 14 12 14Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><p><strong>Razlikovanje pouzdanosti i vidljivosti ključno je za informacijsku pismenost.</strong></p>\n<ul>\n<li><strong>Pouzdanost</strong> ovisi o tome tko je stvorio sadržaj (autor) i kako je uređen (urednički pregled) — a ne o tome tko ga je podijelio, u kojem se okruženju pojavio ili koliko je istaknut.\n</li>\n<li><strong>Vidljivost</strong> ovisi o tome tko dijeli sadržaj, o mehanizmima koji ga ističu i o praksama kojima se pojačava. Vidljivost nije mjera pouzdanosti.\n<p>Na primjer, članak koji je retweetan: račun koji ga je podijelio i platforma koja ga je prikazala utjecali su na njegovu vidljivost — ali članak ima vlastitog autora i uredničku povijest, čiju pouzdanost treba procijeniti zasebno.</p>\n</li>\n</ul></div>\n</div>\n</div>\n<h1>Cirkulacija informacija: dijeljenje, prosljeđivanje, povezivanje, ponovno objavljivanje, citiranje</h1>\n<p>Ljudi, računi (uključujući botove) i organizacije kruže postojećim informacijskim jedinicama dijeljenjem, prosljeđivanjem, povezivanjem, ponovnim objavljivanjem, citiranjem ili na drugi način redistribuiranjem.</p>\n<p>→ Kada se stavka prenese uz dodatne komentare, tumačenje ili preoblikovanje, stvara se nova informacijska stavka s novim autorom.</p>\n<h1>Promocija i vidljivost informacija vođena izvorom</h1>\n<p>Prakse koje pokreću izvori — oni koji stvaraju, objavljuju ili promoviraju informacijske jedinice. Izvori uključuju pojedinačne korisnike, kreatore sadržaja, organizacije, oglašivače i operatere web-stranica. Za razliku od mehanizama vođenih platformama, ove prakse vode sami izvori, koji odlučuju kako učiniti svoj sadržaj vidljivim. Oni djeluju na dva načina: prilagođavanjem sadržaja sustavima rangiranja platformi (doseg, izravno obraćanje, SEO, SMO, optimizacija specifična za platformu) ili izravnim plaćanjem za postavljanje (plaćena postavljanja).</p>\n<h2>Doseg računa</h2>\n<p>Veličina i struktura potencijalne publike dijelitelja.</p>\n<p>Na doseg utječu sljedeći čimbenici:</p>\n<ul>\n<li>#### broj pratitelja ili pretplatnika\n</li>\n<li>#### status verifikacije\n</li>\n<li>#### status računa: starost, povijest angažmana, reputacija platforme\n</li>\n</ul>\n<p>Domet utječe na vidljivost na dva načina:</p>\n<ul>\n<li><em>izravno</em>: stavke koje dijele računi s velikim dosegom pojavljuju se u više feedova u trenutku dijeljenja\n</li>\n<li><em>neposredno</em>: računi s velikim dosegom generiraju više signala angažmana, koje platformini algoritmi zatim koriste za podizanje stavki u rangiranju (→ Algoritamsko upravljanje sadržajem)\n</li>\n</ul>\n<p>→ Doseg se znatno razlikuje: privatni račun s 100 pratitelja i javni račun s milijun pratitelja djeluju na temeljno različitim razinama utjecaja na vidljivost.</p>\n<h2>Izravno obraćanje</h2>\n<p>Mehanizmi kojima su podijelitelji usmjereni na određene primatelje, stvarajući tim korisnicima trenutačnu istaknutost putem obavijesti platforme.</p>\n<p>Primjeri:</p>\n<ul>\n<li>#### @spominjanja\n</li>\n<li>#### označavanje (na fotografijama, objavama ili nizovima)\n</li>\n<li>#### citirani tweetovi i odgovori s spominjanjem\n</li>\n<li>#### grupne izravne poruke\n</li>\n<li>#### adresiranje mailing-liste (Za, UCC)\n</li>\n</ul>\n<p>→ Izravno obraćanje razlikuje se od uobičajenog dijeljenja: ciljani korisnik prima objavu izravno putem obavijesti, bez obzira na to bi li na nju inače naišao u svojem feedu.</p>\n<p>→ Izravno obraćanje hibridni je mehanizam. Radi na strani korisnika, ali funkcionira putem infrastrukture platforme (sustavi obavijesti). Njegov učinak je trenutačna istaknutost za obraćenog korisnika.</p>\n<h2>Optimizacija za tražilice (SEO)</h2>\n<p>Prakse na strani izvora za prilagodbu <strong>web-stranica</strong> — njihovog sadržaja, metapodataka i strukture poveznica — kako bi se bolje rangirale u <strong>rezultatima općih tražilica</strong> (Google, Bing, DuckDuckGo itd.).</p>\n<p>Uobičajene prakse uključuju:</p>\n<ul>\n<li>istraživanje ključnih riječi i njihova integracija u naslove, zaglavlja i glavni tekst\n</li>\n<li>optimizacija metapodataka (naslovi, meta opisi, alt tekst)\n</li>\n<li>izgradnja poveznica (pribavljanje dolaznih poveznica s autoritativnih stranica)\n</li>\n<li>struktura stranice i interno povezivanje\n</li>\n<li>brzina stranice i prilagođenost mobilnim uređajima\n</li>\n<li>izrada sadržaja koji odgovara namjeri pretraživanja\n</li>\n</ul>\n<p>→ SEO je najformaliziranija disciplina optimizacije jer su signali za rangiranje u tražilicama relativno stabilni i dobro dokumentirani (Lewandowski i dr.). Specijalizirani SEO stručnjaci, agencije i alati podržavaju njegovu praksu.</p>\n<p>→ SEO ne mijenja način na koji tražilice rangiraju stranice — on prilagođava web-stranicu postojećim kriterijima rangiranja. Izdavači mogu oblikovati ono što algoritam vidi, ali ne i način na koji on donosi odluke.</p>\n<h2>Optimizacija za društvene mreže (SMO)</h2>\n<p>Prakse na strani izvora za maksimiziranje vidljivosti, angažmana i mogućnosti dijeljenja sadržaja na platformama društvenih mreža.</p>\n<p>Uobičajene prakse uključuju:</p>\n<ul>\n<li>strategije hashtagova (popularni ili tematski specifični oznake)\n</li>\n<li>vrijeme objavljivanja (kada je ciljana publika aktivna)\n</li>\n<li>izbor formata sadržaja (kratki video, karuseli, reels)\n</li>\n<li>dizajn naslova i uvodnika (brzo privlačenje pažnje)\n</li>\n<li>poticanje interakcije (pitanja, ankete, pozivi na akciju)\n</li>\n<li>prilagodba sadržaja za više platformi\n</li>\n</ul>\n<p>→ SMO je manje formaliziran od SEO-a jer su signali za rangiranje na društvenim mrežama neprozirniji i specifični za platformu. Prakse se mijenjaju kako se mijenjaju i algoritmi.</p>\n<h2>Optimizacija specifična za platformu</h2>\n<p>Strategije optimizacije prilagođene konvencijama i logikama rangiranja pojedinih platformi — izvan općih principa SEO-a ili SMO-a.</p>\n<ul>\n<li>TikTok: privlačenje gledatelja u prve tri sekunde, korištenje trendovskih zvukova, vertikalni video kratkog formata\n</li>\n<li>Instagram: kvalitetni vizuali, strategija usmjerena na Reels, kombiniranje hashtagova\n</li>\n<li>YouTube: dizajn sličica, optimizacija vremena gledanja, naslovi i opisi bogati ključnim riječima\n</li>\n<li>LinkedIn: duži profesionalni postovi, nativno objavljivanje, umreženi angažman\n</li>\n<li>X (Twitter): sažete uvodne rečenice, niti, odgovaranje računima s velikim dosegom\n</li>\n</ul>\n<p>→ Optimizacija specifična za platformu zahtijeva razumijevanje sustava rangiranja svake platforme, ponašanja publike i preferencija formata sadržaja. Ono što funkcionira na TikToku rijetko funkcionira na LinkedInu.</p>\n<h2>Platni plasmani i oglašavanje</h2>\n<p>Praksa plaćanja za vidljivost s izvorne strane — sponzorirani sadržaj postavljen uz organski sadržaj, obično putem oglašavanja.</p>\n<p>Uobičajeni oblici:</p>\n<ul>\n<li>sponzorirani rezultati pretraživanja (oglasi na tražilicama)\n</li>\n<li>sponzorirane objave i promovirani sadržaj (društveni mediji)\n</li>\n<li>oglasi na prikazu (banneri, videozapisi)\n</li>\n<li>partnerstva s utjecajnim osobama (plaćene suradnje)\n</li>\n</ul>\n<p>→ Plaćeni plasmani zaobilaze organske sustave rangiranja: umjesto optimizacije sadržaja za visok plasman, izvor plaća platformi izravno za postavljanje.</p>\n<p>→ Ponekad su jasno označeni (&quot;Sponzorirano&quot;, &quot;Oglas&quot;), a ponekad se jedva razlikuju od organskih rezultata. Standardima označavanja razlikuju se ovisno o nadležnosti i platformi.</p>\n<h1>Promocija informacija i nadzor sadržaja s strane platforme</h1>\n<div class=\"scilmi-callout scilmi-callout-warning\">\n<div class=\"scilmi-callout-row\" aria-label=\"Warning\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M12 20C7.58172 20 4 16.4183 4 12C4 7.58172 7.58172 4 12 4C16.4183 4 20 7.58172 20 12C20 16.4183 16.4183 20 12 20ZM12 15C12.5523 15 13 15.4477 13 16C13 16.5523 12.5523 17 12 17C11.4477 17 11 16.5523 11 16C11 15.4477 11.4477 15 12 15ZM12 14C13 14 13 13 13 13L13 10.5L13 8C13 8 13 7 12 7C11 7 11 8 11 8L11 13C11 13 11 14 12 14Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><p>Ono što korisnici zapravo vide rijetko je proizvod jednog mehanizma. U tražilici se algoritamski poredan popis organskih rezultata prikazuje uz plaćene oglase, sažetke generirane umjetnom inteligencijom i ponekad uredničke istaknute sadržaje — a temeljni signali za rangiranje mogu se namjerno utjecati optimizacijom za<em> tražilice (SEO)</em>. U feedu društvenih mreža algoritamski rangirane objave pojavljuju se uz sponzorirani sadržaj, preporučene profile i oznake popularnih tema. Svaka komponenta slijedi vlastitu logiku i doprinosi cjelokupnom ishodu vidljivosti.</p></div>\n</div>\n</div>\n<h2>Urednička kuracija</h2>\n<p>Ručna kuracija s platforme: informacije koje su namjerno istaknute od strane uredničkih timova ili operatera platforme, a ne prikazane putem algoritamskog rangiranja.</p>\n<p>Primjeri:</p>\n<ul>\n<li>istaknuti isječci u rezultatima pretraživanja\n</li>\n<li>kurirani odjeljci s trendovima\n</li>\n<li>priče odabrane od urednika u agregatorima vijesti\n</li>\n<li>platformom promovirani hashtagovi\n</li>\n<li>Urednikovi odabiri u trgovinama aplikacija\n</li>\n<li>istaknuti kreatori ili računi\n</li>\n</ul>\n<p>→ Urednički istaknuti sadržaji stoje uz algoritamske mehanizme i odražavaju vlastite prosudbe platforme o tome koji sadržaj zaslužuje istaknuto prikazivanje.</p>\n<p>→ Za razliku od algoritamskog upravljanja sadržajem (kuriranje i personalizacija), uredničko upravljanje uključuje ljudske uredničke odluke same platforme. Funkcionalno, to je oblik <em>uredničkog upravljanja sadržajem</em> ) — koje provodi platforma, a ne tradicionalni izdavači.</p>\n<h2>Algoritamski nadzor</h2>\n<p><strong>Algoritamsko upravljanje sadržajem</strong> odnosi se na ulogu algoritama u odlučivanju koje će informacije stići do kojih korisnika — digitalni pandan <em>uredničkom upravljanju sadržajem</em> (→ Informacije, izvori i informacijski okruženja → Urednička provjera). Obuhvaća i <strong>odabir</strong> (što se prikazuje i visoko rangira) i <strong>isključivanje</strong> (što se filtrira, spušta na nižu poziciju ili skriva).</p>\n<p>Algoritamsko upravljanje sadržajem djeluje na različitim vrstama platformi:</p>\n<ul>\n<li>u <strong>tražilicama</strong>, algoritmi odabiru i rangiraju rezultate na temelju upita\n</li>\n<li>u <strong>feedovima društvenih mreža</strong>, algoritmi odlučuju koji se postovi ističu više\n</li>\n<li>na <strong>video platformama</strong>, algoritmi predlažu što gledati sljedeće\n</li>\n<li>u <strong>sustavima za odgovaranje temeljenim na umjetnoj inteligenciji</strong>, algoritmi generiraju, sažimaju ili sintetiziraju odgovore\n</li>\n</ul>\n<p>Algoritamsko upravljanje sadržajem djeluje u dva načina rada koja često djeluju zajedno: opće operacije primijenjene na sve korisnike (<em>algoritamsko kuriranje</em>) i individualno prilagođavanje na temelju praćenih korisničkih signala (<em>algoritamsko personaliziranje</em>).</p>\n<h3>Algoritamsko kuriranje</h3>\n<p>Opće algoritamske operacije primijenjene na sve korisnike — one oblikuju koje su informacije dostupne na platformi, bez obzira na to tko je korisnik.</p>\n<ul>\n<li><strong>Pregledavanje i indeksiranje</strong>\n— <em>Koji će se informacijski elementi učiniti dostupnima za prikaz?</em>\n<ul>\n<li>pretraživači koji pretražuju web\n</li>\n<li>agregatori sadržaja koji indeksiraju izvore vijesti\n</li>\n<li>trgovine aplikacijama koje katalogiziraju dostupne aplikacije\n</li>\n</ul>\n</li>\n<li><strong>Filtriranje i moderacija</strong>\n— <em>Koji se stavci blokiraju ili spuštaju u rang prema pravilima platforme?</em>\n<ul>\n<li>filteri za neželjenu poštu\n</li>\n<li>uklanjanje sadržaja koji krši pravila (govor mržnje, nezakonit sadržaj, eksplicitan materijal)\n</li>\n<li>spuštanje materijala niske kvalitete ili obmanjujućeg sadržaja\n</li>\n</ul>\n</li>\n<li><strong>Ocjenjivanje kvalitete</strong>\n— <em>Koji se izvori ili stavke ocjenjuju kao vjerodostojniji ili kvalitetniji?</em>\n<ul>\n<li>tražilice kažnjavaju stranice niske kvalitete\n</li>\n<li>rangiranje agregatora vijesti prema autoritetu izvora\n</li>\n<li>rangiranje pod utjecajem recenzija na akademskim tražilicama\n</li>\n</ul>\n</li>\n<li><strong>Otkrivanje trendova</strong>\n— <em>Koji se stavci prikazuju kao trenutno popularni?</em>\n<ul>\n<li>teme u trendu na društvenim platformama\n</li>\n<li>top ljestvice na streaming servisima\n</li>\n<li>sekcije &quot;Što se događa&quot; i &quot;Današnji naslovi&quot;\n</li>\n<li>popularni hashtagovi\n</li>\n</ul>\n</li>\n</ul>\n<p>→ Algoritamsko kuriranje definira <em>skup</em> informacija dostupnih na platformi. U velikoj mjeri radi na isti način za sve korisnike.</p>\n<h3>Algoritamsko personaliziranje</h3>\n<p>Algoritamski postupci koji prilagođavaju odabir, redoslijed i prikaz informacija pojedinim korisnicima na temelju njihovih praćenih signala. Ti se signali s vremenom gomilaju u korisničke povijesti na kojima se algoritmi temelje.</p>\n<p>→ Dva korisnika na istoj platformi — čak i s istim upitom — obično vide znatno različiti sadržaj.</p>\n<ul>\n<li><strong>Personalizirani poredak</strong>\n— <em>Koji se stavci za ovog korisnika rangiraju više?</em>\n<ul>\n<li>personalizirani rezultati pretraživanja (lokacija, povijest, redoslijed prema profilu)\n</li>\n<li>redoslijed feeda na društvenim mrežama (&quot;For You&quot; feedovi, &quot;Top objave&quot;)\n</li>\n<li>rangiranje temeljeno na angažmanu — optimizacija za predviđenu interakciju, dominantno na društvenim mrežama\n</li>\n</ul>\n</li>\n<li><strong>Preporuke</strong>\n— <em>Koji se stavci predlažu ovom korisniku osim onih koje je aktivno zatražio?</em>\n<ul>\n<li>popisi videozapisa &quot;Preporučeno za vas&quot;\n</li>\n<li>predloženi računi, grupe ili teme za praćenje\n</li>\n<li>&quot;Osobe koje možda poznajete&quot;\n</li>\n<li>povezani članci, slični proizvodi, &quot;Sljedeće&quot;\n</li>\n</ul>\n</li>\n<li><strong>Personalizirano oglašavanje</strong>\n— <em>Koji se oglasi ciljaju na ovog korisnika?</em>\n<ul>\n<li>oglasi za pretraživanje prilagođeni prethodnim upitima\n</li>\n<li>sponzorirane objave na društvenim mrežama temeljene na profilu i ponašanju\n</li>\n<li>ponovno ciljani prikazni oglasi na web-stranicama\n</li>\n<li>partnerstva s utjecajnim osobama prilagođena interesima publike\n</li>\n</ul>\n</li>\n</ul>\n<p>→ Dva korisnika na istoj platformi — čak i s istim upitom — obično vide znatno različiti sadržaj.</p>\n<p>→ Personalizacija stvara <strong>povratnu spregu</strong>: ono što korisnici rade utječe na ono što će sljedeće vidjeti, a ono što sljedeće vide može utjecati na ono što rade.</p>\n<h4>☑ Korisnički signali koje platforma prati za algoritamsku personalizaciju</h4>\n<p>Radnje koje korisnik poduzima — aktivno ili pasivno — unutar informacijskog kanala koje se mogu pratiti i koristiti algoritmima za personalizaciju odabira i vidljivosti informacijskih stavki.</p>\n<p>→ Korisničke radnje nisu ograničene na namjerne interakcije poput klika ili lajkova. Mnoge su radnje pasivne ili automatske, poput toga koliko dugo korisnik ostaje na stranici, koliko daleko se pomiče prema dolje ili gdje se nalazi. Korisnici često nisu svjesni da te radnje utječu na ono što će sljedeće vidjeti.</p>\n<table><thead><tr>\n<th>Vrsta</th>\n<th>Što je to</th>\n<th>Primjeri</th>\n</tr></thead><tbody>\n<tr>\n<td><strong>Eksplicitna povratna informacija</strong></td>\n<td>Namjerne interakcije koje korisnik odabire izvršiti</td>\n<td>- pretraživanja / upiti za<br>pretraživanje-<br>klikovi- sviđanja /<br>reakcije- komentari /<br>odgovori- dijeljenja / repostovi /<br>prosljeđivanja- praćenja /<br>pretplate- spremanja /<br>oznake- ocjene /<br>recenzije- kupnje / preuzimanja</td>\n</tr>\n<tr>\n<td><strong>Implicitno ponašanje</strong></td>\n<td>Pasivni signali ponašanja prikupljeni tijekom korištenja</td>\n<td>- vrijeme gledanja / slušanja /<br>boravka- ponašanje pri skrolanju (koliko daleko, koliko brzo)<br>- ponašanje pri<br>preletu mišem- ponašanje pri preskakanju</td>\n</tr>\n<tr>\n<td><strong>Kontekstualni podaci</strong></td>\n<td>Informacije o situaciji u kojoj korisnik pristupa platformi</td>\n<td>- podatke o lokaciji korisnika tijekom korištenja<br>aplikacije- tip uređaja (npr. telefon ili prijenosno računalo)<br>- vrijeme pristupa</td>\n</tr>\n<tr>\n<td><strong>Podaci o računu i društvenim mrežama</strong></td>\n<td>Informacije iz korisničkog profila i društvenih veza</td>\n<td>- informacije o profilu (dob, interesi, zanimanje, spol)<br>- postavke<br>jezika - povezani računi<br>- popis kontakata / adresar</td>\n</tr>\n</tbody></table>\n<div class=\"scilmi-callout scilmi-callout-info\">\n<div class=\"scilmi-callout-row\" aria-label=\"Info\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" d=\"M20 12C20 7.58172 16.4183 4 12 4C7.58172 4 4 7.58172 4 12C4 16.4183 7.58172 20 12 20C16.4183 20 20 16.4183 20 12ZM11 8C11 8.55228 11.4477 9 12 9C12.5523 9 13 8.55228 13 8C13 7.44772 12.5523 7 12 7C11.4477 7 11 7.44772 11 8ZM12 10C13 10 13 11 13 11V16C13 16 13 17 12 17C11 17 11 16 11 16V11C11 11 11 11 10.5 11C10 11 10 10 10 10H12Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><ul>\n<li>\nAdomavicius, G., &amp; Tuzhilin, A. (2011). Kontekstno osviješteni sustavi za preporuke. U F. Ricci, L. Rokach, B. Shapira i P. B. Kantor (ur.), <em>Recommender Systems Handbook</em>\n(str. 217–253). Springer. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1007&#x2f;978-0-387-85820-3_7\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1007/978-0-387-85820-3_7</a>\n</li>\n<li>\nKelly, D., &amp; Teevan, J. (2003). Implicitna povratna informacija za zaključivanje korisničkih preferencija: Bibliografija. <em>ACM SIGIR Forum, 37</em>\n(2), 18–28. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1145&#x2f;959258.959260\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1145/959258.959260</a>\n</li>\n<li>\nLi, W., Kuo, J.-C., Sheng, M., Zhang, P., &amp; Wu, Q. (2025). Iza eksplicitnog i implicitnog: Kako korisnici daju povratne informacije za oblikovanje personaliziranog sadržaja preporuka. U <em>Zborniku radova konferencije CHI 2025 o ljudskim čimbenicima u računalnim sustavima (CHI &#x27;25)</em>\n. Association for Computing Machinery. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1145&#x2f;3706598.3713241\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1145/3706598.3713241</a>\n</li>\n<li>\nNarayanan, A. (2023). <em>Razumijevanje algoritama za preporuke na društvenim mrežama.</em>\nKnight First Amendment Institute, Sveučilište Columbia.<a class=\"notranslate\" translate=\"no\" href=\"https&#x3a;&#x2f;&#x2f;knightcolumbia.org&#x2f;content&#x2f;understanding-social-media-recommendation-algorithms\" target=\"_blank\" rel=\"noopener\">https://knightcolumbia.org/content/understanding-social-media-recommendation-algorithms</a>\n</li>\n</ul></div>\n</div>\n</div>\n<h1>Pojačavanje informacija</h1>\n<p>Amplifikacija se odnosi na sustavno povećavanje vidljivosti stavke izvan razine pojedinog korisnika — kako bi se postigla široka vidljivost na korisničkim računima, a ponekad i u različitim informacijskim okruženjima.</p>\n<p>Dok <em>Promocija vođena izvorom</em> (iznad) obuhvaća ono što sam izvor čini kako bi stekao vidljivost, a <em>Nadzor s platforme</em> (iznad) opisuje algoritamske operacije kojima platforme prikazuju i rangiraju sadržaj za pojedinačne korisnike, <em>pojačavanje</em> se odnosi na rezultirajuće <em>pojačane učinke u velikoj mjeri</em> — proizvedene bilo kao zbirni učinak tih operacija platformi (→ <em>Algoritamsko pojačavanje</em>) ili kroz koordiniranu aktivnost više aktera (→ <em>Koordinirano pojačavanje</em>).</p>\n<h2>Mehanizmi amplifikacije</h2>\n<p>Amplifikacija djeluje kroz dva glavna mehanizma.</p>\n<ul>\n<li><strong>Algoritamsko pojačavanje</strong> je <em>vođeno platformom</em>: to je zbirni učinak <em>algoritamskog nadzora</em> — kumulativni ishod kuriranja i personalizacije platforme o tome koji sadržaji dopiru do kojih korisnika i koliko istaknuto.\n</li>\n<li><strong>Koordinirano pojačavanje</strong> je <em>vođeno od strane aktera</em>: više računa, grupa ili kampanja namjerno djeluje zajednički kako bi povećalo vidljivost stavke, hashtag-a ili narativa iznad onoga što bi aktivnost pojedinačnog korisnika proizvela. Literatura ga klasificira duž dviju dimenzija — <strong>koordinacije</strong> (transparentne ili skrivene) i <strong>računa</strong> (pravi ili lažni) — i u skladu s tim razlikuje (Rogers &amp; Righetti, 2025):\n<ul>\n<li><strong>Koordinirano autentično pojačavanje</strong>: koordinacija je transparentna, a računi su stvarni (npr. otvorene građanske kampanje, zagovaranje, marketing).\n</li>\n<li><strong>Koordinirano neautentično / umjetno pojačavanje</strong>: koordinacija je skrivena, računi su lažni ili oboje — stvara se privid organske podrške (Meta-ovo <em>koordinirano neautentično ponašanje</em> / CIB; Gleicher, 2018).\n</li>\n</ul>\n</li>\n</ul>\n<p>Ta se dva mehanizma često kombiniraju. Koordinirane mreže iskorištavaju rangiranje temeljeno na angažmanu kako bi potaknule algoritamske pojačanja; algoritamsko rangiranje, zauzvrat, pojačava svu vidljivost koju je koordinacija već proizvela.</p>\n<div class=\"scilmi-callout scilmi-callout-info\">\n<div class=\"scilmi-callout-row\" aria-label=\"Info\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" d=\"M20 12C20 7.58172 16.4183 4 12 4C7.58172 4 4 7.58172 4 12C4 16.4183 7.58172 20 12 20C16.4183 20 20 16.4183 20 12ZM11 8C11 8.55228 11.4477 9 12 9C12.5523 9 13 8.55228 13 8C13 7.44772 12.5523 7 12 7C11.4477 7 11 7.44772 11 8ZM12 10C13 10 13 11 13 11V16C13 16 13 17 12 17C11 17 11 16 11 16V11C11 11 11 11 10.5 11C10 11 10 10 10 10H12Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><ul>\n<li>\nGleicher, N. (2018.). <em>Objašnjeno koordinirano neautentično ponašanje</em>\n. Meta Newsroom.<a class=\"notranslate\" translate=\"no\" href=\"https&#x3a;&#x2f;&#x2f;about.fb.com&#x2f;news&#x2f;2018&#x2f;12&#x2f;inside-feed-coordinated-inauthentic-behavior&#x2f;\" target=\"_blank\" rel=\"noopener\">https://about.fb.com/news/2018/12/inside-feed-coordinated-inauthentic-behavior/</a>\n</li>\n<li>\nRogers, R. i Righetti, N. (2025). Koordinirano neautentično ponašanje na Facebooku? Tipologija proizvedene pažnje. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1177&#x2f;29768624251369784\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1177/29768624251369784</a>\n</li>\n</ul></div>\n</div>\n</div>\n<h3>Algoritamska pojačanja</h3>\n<p>Algoritamsko pojačavanje je kumulativni učinak gore navedenih mehanizama nadzora (kuriranje i personalizacija): sustavno oblikovanje toga koji se stavci, teme, računi i formati istaknuto prikazuju korisnicima — a koji se filtriraju, umanjuju ili gurnu dolje.</p>\n<p>Empirijska istraživanja pokazuju da rangiranje temeljeno na angažmanu sustavno pojačava sadržaj emotivno nabijen i neprijateljski prema vanjskim skupinama, čak i kada korisnici sami ne preferiraju takav sadržaj (Milli i dr., 2025). Također pojačava postojeći doseg: računi i stavke s visokim prethodnim angažmanom nagrađuju se dodatnom vidljivošću, stvarajući vrlo neuravnotežene distribucije dosega (efekt &quot;bogati postaju bogatiji&quot;).</p>\n<p>Algoritamski zahvati također mogu imati nelinearne učinke u suprotnom smjeru. Smanjenje vidljivosti stavke u feedu za oko 20 % može smanjiti njezin doseg za red veličine (Narayanan, 2023).</p>\n<p>Algoritamsko pojačavanje nije neutralan odraz aktivnosti korisnika. Njegovi učinci su emergentni i vidljivi prvenstveno u cjelini: pojedinačne preporuke su neprecizne (stope angažmana ostaju ispod 1 % na većini platformi), ali rangiranje, preporuke i spustljanje na nižu poziciju sustavno oblikuju ono što kruži platformom.</p>\n<div class=\"scilmi-callout scilmi-callout-info\">\n<div class=\"scilmi-callout-row\" aria-label=\"Info\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" d=\"M20 12C20 7.58172 16.4183 4 12 4C7.58172 4 4 7.58172 4 12C4 16.4183 7.58172 20 12 20C16.4183 20 20 16.4183 20 12ZM11 8C11 8.55228 11.4477 9 12 9C12.5523 9 13 8.55228 13 8C13 7.44772 12.5523 7 12 7C11.4477 7 11 7.44772 11 8ZM12 10C13 10 13 11 13 11V16C13 16 13 17 12 17C11 17 11 16 11 16V11C11 11 11 11 10.5 11C10 11 10 10 10 10H12Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><ul>\n<li>Milli, S., i dr. (2025.). Angažman, zadovoljstvo korisnika i pojačavanje podijeljenog sadržaja na društvenim mrežama. PNAS Nexus.\n</li>\n<li>Narayanan, A. (2023). Understanding social media recommendation algorithms. Knight First Amendment Institute.\n</li>\n</ul></div>\n</div>\n</div>\n<h3>Koordinirano autentično pojačavanje</h3>\n<p>Koordinirano autentično pojačavanje je namjerno pojačavanje informativnog sadržaja, teme, hashtag-a, računa ili narativa putem otvoreno objavljene, organizirane aktivnosti stvarnih računa. Koordinirano podrijetlo nije prikriveno: sudionici djeluju pod svojim stvarnim identitetima ili pod poznatim grupnim pripadnostima.</p>\n<p>Tipični konteksti uključuju</p>\n<ul>\n<li>građanske kampanje (npr. peticije nevladinih organizacija, zagovarački hashtagovi),\n</li>\n<li>politička mobilizacija (npr. stranačko kampanjsko djelovanje, napori za poticanje izlaska birača na izbore),\n</li>\n<li>marketinške i brend kampanje,\n</li>\n<li>komunikacije profesionalnih udruga i\n</li>\n<li>kulturni pokreti poput Fridays for Future ili #MeToo.\n</li>\n</ul>\n<div class=\"scilmi-callout scilmi-callout-warning\">\n<div class=\"scilmi-callout-row\" aria-label=\"Warning\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M12 20C7.58172 20 4 16.4183 4 12C4 7.58172 7.58172 4 12 4C16.4183 4 20 7.58172 20 12C20 16.4183 16.4183 20 12 20ZM12 15C12.5523 15 13 15.4477 13 16C13 16.5523 12.5523 17 12 17C11.4477 17 11 16.5523 11 16C11 15.4477 11.4477 15 12 15ZM12 14C13 14 13 13 13 13L13 10.5L13 8C13 8 13 7 12 7C11 7 11 8 11 8L11 13C11 13 11 14 12 14Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><p>Je li temeljna poruka utemeljena, uravnotežena ili jednostrana, pitanje je za sebe — <em>autentičnost</em> se odnosi samo na transparentnost koordinacije, a ne na istinitost ili pravednost sadržaja. Autentična kampanja može pojačati točne informacije, obmanjujuće informacije ili jednostranu poziciju.</p></div>\n</div>\n</div>\n<div class=\"scilmi-callout scilmi-callout-warning\">\n<div class=\"scilmi-callout-row\" aria-label=\"Warning\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M12 20C7.58172 20 4 16.4183 4 12C4 7.58172 7.58172 4 12 4C16.4183 4 20 7.58172 20 12C20 16.4183 16.4183 20 12 20ZM12 15C12.5523 15 13 15.4477 13 16C13 16.5523 12.5523 17 12 17C11.4477 17 11 16.5523 11 16C11 15.4477 11.4477 15 12 15ZM12 14C13 14 13 13 13 13L13 10.5L13 8C13 8 13 7 12 7C11 7 11 8 11 8L11 13C11 13 11 14 12 14Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><p>Autentična i neautentična koordinacija mogu proizvesti obrasce vidljivosti koji izvana izgledaju identično — sinkronizirano dijeljenje, grupiranje hashtagova, brzo prihvaćanje. Odlikujuća značajka nije vidljiv obrazac, već je li koordinirano podrijetlo otvoreno otkriveno.</p></div>\n</div>\n</div>\n<h3>Koordinirano neautentično / umjetno pojačavanje</h3>\n<p>Koordinirano neautentično / umjetno pojačavanje je namjerno pojačavanje informativnog sadržaja, teme, hashtagova, računa ili narativa putem organizirane aktivnosti u kojoj je koordinirano podrijetlo prikriveno, sudjeluju lažni računi ili oboje. Cilj je stvoriti dojam organske, neovisne podrške. Metaov pojam <em>koordiniranog neautentičnog ponašanja</em> (CIB) — koji je sada uključen u Zakon o digitalnim uslugama EU — usredotočen je na ovu kombinaciju lažnih identiteta i neprijateljskih metoda za izbjegavanje otkrivanja (Gleicher, 2018; Rogers &amp; Righetti, 2025).</p>\n<p>Tipični konteksti uključuju operacije političkog utjecaja (državne ili povezane s političkim strankama), kampanje astroturfinga (komercijalne ili ideološke), ciljanu dezinformaciju o izborima, javnom zdravlju ili geopolitičkim sukobima te manipulaciju ugledom putem lažnih recenzija, ocjena ili angažmana. Operativna sredstva — <em>botovi</em>, <em>trolovi</em>, <em>sockpuppeti</em> i njihove koordinirane mreže (<em>farme botova</em>, <em>farme trolova</em>, <em>mreže sockpuppeta</em>, <em>farme klikova</em>) — detaljno su opisana u nastavku.</p>\n<div class=\"scilmi-callout scilmi-callout-warning\">\n<div class=\"scilmi-callout-row\" aria-label=\"Warning\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M12 20C7.58172 20 4 16.4183 4 12C4 7.58172 7.58172 4 12 4C16.4183 4 20 7.58172 20 12C20 16.4183 16.4183 20 12 20ZM12 15C12.5523 15 13 15.4477 13 16C13 16.5523 12.5523 17 12 17C11.4477 17 11 16.5523 11 16C11 15.4477 11.4477 15 12 15ZM12 14C13 14 13 13 13 13L13 10.5L13 8C13 8 13 7 12 7C11 7 11 8 11 8L11 13C11 13 11 14 12 14Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><p><em>Neautentičnost</em> se odnosi na prikrivanje koordiniranog podrijetla ili upotrebu lažnih računa — a ne na istinitost sadržaja koji se pojačava. Koordinirana mreža lažnih računa može pojačati točne informacije; jedan autentičan pojedinac može širiti izmišljene informacije. Koordinirano neautentično pojačavanje i širenje lažnog sadržaja različiti su fenomeni koji se mogu dogoditi neovisno ili zajedno.</p></div>\n</div>\n</div>\n<p>Sljedeće vrste računa opisane u ovom odjeljku primjenjuju se na sve digitalne informacijske kanale i platforme na kojima korisnici mogu stvarati račune i javno objavljivati sadržaj ili s njim komunicirati — osobito na društvenim mrežama, forumima za raspravu i prostorima zajednice, video i audio platformama te u odjeljcima za recenzije ili komentare. Manje su istaknute u aplikacijama za privatnu komunikaciju ili u okruženjima bez sadržaja koji stvaraju korisnici. Pojavljuju se i samostalno i unutar koordiniranih mreža. Ovdje su navedene zbog svoje uobičajene uloge u dinamici pojačavanja; eksplicitno koordinirane formacije su Mreže računa.</p>\n<table><thead><tr>\n<th>Pojam</th>\n<th>Definicija</th>\n<th>Pod kontrolom</th>\n<th>Definirano od strane</th>\n<th>Tipična svrha</th>\n</tr></thead><tbody>\n<tr>\n<td><strong>Socijalni bot</strong></td>\n<td>Automatski ili djelomično automatizirani račun koji objavljuje, lajka, prati, dijeli ili odgovara na mreži.</td>\n<td>Softver</td>\n<td><strong>Automatizacija</strong></td>\n<td>Za pojačavanje poruka, stvaranje umjetne popularnosti, spam, utjecanje na rasprave ili širenje sadržaja u velikim razmjerima.</td>\n</tr>\n<tr>\n<td><strong>Kiborg</strong></td>\n<td>Hibridni račun koji kombinira ljudsko upravljanje s automatizacijom softvera.</td>\n<td>Miješano: ljudski i softverski</td>\n<td><strong>Selektivna automatizacija</strong></td>\n<td>Kako bi se kombinirala razina automatizacije s kontekstualnom vjerodostojnošću ljudskog unosa — za legitimno zakazivanje/upravljanje ili za operacije utjecaja koje je teže otkriti.</td>\n</tr>\n<tr>\n<td><strong>Trol</strong></td>\n<td>Osoba ili račun koji namjerno provocira, ometa ili rasplamsava internetsku raspravu.</td>\n<td>Obično ljudski korisnik; ponekad koordinirane grupe</td>\n<td><strong>Poremećajno / provokativno / antagonističko ponašanje</strong></td>\n<td>Da bi uznemirili druge, skrenuli razgovore s teme, izazvali reakcije, širili neprijateljstvo ili polarizirali raspravu.</td>\n</tr>\n<tr>\n<td><strong>Sockpuppet</strong></td>\n<td>Lažni račun koji netko koristi kako bi sakrio svoj pravi identitet ili se pretvarao da je netko drugi.</td>\n<td>Ljudski korisnik, iako račun može koristiti i automatizaciju</td>\n<td><strong>Obmanjujući identitet</strong></td>\n<td>Za stvaranje lažne podrške, anonimni napadi na druge, izbjegavanje zabrana, manipuliranje debatom ili stvaranje dojma neovisnog slaganja.</td>\n</tr>\n</tbody></table>\n<h4>Društveni bot</h4>\n<p><strong>Socijalni bot</strong> je bot dizajniran za rad na platformama društvenih medija, objavljujući, komentirajući, dijeleći ili komunicirajući na načine koji oponašaju ljudske korisnike. Socijalni botovi obično su programirani da djeluju u velikim razmjerima i velikom brzinom, daleko iznad onoga što bi ljudski korisnik mogao postići. Njihova je aktivnost često ponavljajuća i koordinirana na više računa, što je razlikuje od normalne ljudske upotrebe.</p>\n<p>Socijalni botovi mogu se koristiti u legitimne svrhe — kao što su korisnička podrška, distribucija vijesti ili marketing — ali se također široko koriste za utjecanje na javno mnijenje, pojačavanje određenih poruka, manipuliranje raspravama ili stvaranje dojma o širokoj podršci za određene ideje, proizvode ili ciljeve. U kontekstu dezinformacija i misinformationa, socijalni botovi imaju posebnu ulogu u brzoj distribuciji sadržaja i stvaranju lažnog dojma da mnogi neovisni glasovi dijele isto mišljenje.</p>\n<p>Kada se društveni botovi rasporede u koordiniranim mrežama, oni čine <em>farmu</em> <em>botova</em>.</p>\n<div class=\"scilmi-callout scilmi-callout-info\">\n<div class=\"scilmi-callout-row\" aria-label=\"Info\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" d=\"M20 12C20 7.58172 16.4183 4 12 4C7.58172 4 4 7.58172 4 12C4 16.4183 7.58172 20 12 20C16.4183 20 20 16.4183 20 12ZM11 8C11 8.55228 11.4477 9 12 9C12.5523 9 13 8.55228 13 8C13 7.44772 12.5523 7 12 7C11.4477 7 11 7.44772 11 8ZM12 10C13 10 13 11 13 11V16C13 16 13 17 12 17C11 17 11 16 11 16V11C11 11 11 11 10.5 11C10 11 10 10 10 10H12Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><h4>Bot</h4>\n<p>Bot je računalni program koji automatski obavlja zadatke, često ponavljajuće. Boti se kreću u rasponu od jednostavnih, bezopasnih alata — poput web-puzica (eng. web crawlers) koji indeksiraju stranice za tražilice, automatiziranih sustava za testiranje ili chatbotova koji odgovaraju na rutinska pitanja korisnika — do zlonamjernih programa osmišljenih za širenje neželjene pošte (spam), zlonamjernog softvera (malware) ili dezinformacija.</p></div>\n</div>\n</div>\n<h4>Kiborg</h4>\n<p><strong>Kiborg</strong> je hibridni račun koji je djelomično upravljan od strane čovjeka, a djelomično automatiziran softverom. Kiborg može imati rutinske objave zakazane ili generirane softverom, dok se osoba bavi odabranim interakcijama, odgovorima ili osjetljivim sadržajem. Ravnoteža između automatizirane i ljudske aktivnosti razlikuje se od računa do računa.</p>\n<p>Kiborzi se mogu koristiti u legitimne svrhe — kao što su zakazivanje sadržaja, upravljanje računima brenda ili institucije ili hibridna korisnička podrška — ali se također koriste u operacijama utjecaja kako bi se kombinirala razina i brzina automatizacije s kontekstualnom vjerodostojnošću ljudskog doprinosa.</p>\n<p>Kiborgove je teže prepoznati od čisto automatiziranih botova jer je dio njihovog ponašanja doista ljudski, što znači da pojedinačni pokazatelji detekcije rijetko dovolje za pouzdanu identifikaciju.</p>\n<h4>Trol</h4>\n<p><strong>Trol</strong> je stvarna osoba koja namjerno remeti internetske rasprave provokativnim, agresivnim ili neprijateljskim ponašanjem. Trolovi obično koriste osobne račune i ciljaju kontroverzne teme, javne ličnosti (poput političara ili novinara) ili medijske organizacije. Njihov je cilj uznemiriti druge, potaknuti reakcije ili eskalirati sukobe — ponekad u korist određene agende, ponekad radi zabave ili privlačenja pažnje.</p>\n<p>Iako trolovi često djeluju neovisno, mogu raditi i u koordiniranim skupinama, ponekad plaćeni od strane političkih ili komercijalnih aktera (vidi <em>Farma trolova</em> pod Mehanizmi pojačanja).</p>\n<p><strong>Trolling je najbolje razumjeti kao obrazac internetskog ponašanja, a ne kao određenu vrstu računa.</strong> Isto se ponašanje može provoditi putem automatiziranih računa, a i obični korisnici povremeno mogu trolati.</p>\n<h4>Sockpuppet</h4>\n<p><strong>Sockpuppet</strong> je lažni internetski identitet koji stvara i njime upravlja stvarna osoba koja skriva svoj pravi identitet. Za razliku od trola — koji često djeluju pod jednim, otvoreno neprijateljskim računom — upravitelj sockpuppeta obično istovremeno vodi više lažnih računa kako bi stvorio dojam da nekoliko neovisnih korisnika dijeli isto mišljenje, podržava istu stvar ili se slaže s glavnim (često zasebnim) računom upravitelja.</p>\n<p>Lažni računi se često koriste za stvaranje umjetnog konsenzusa, podržavanje vlastitih argumenata pod različitim imenima, napadanje protivnika uz privid nepristranosti, izbjegavanje zabrana stvaranjem novih identiteta nakon suspenzije ili manipuliranje internetskim recenzijama, glasovima i anketama.</p>\n<p>Ljudi u lutkama razlikuju se od društvenih robota po tome što ih ručno upravljaju ljudi, što njihov sadržaj čini kontekstualno vjerojatnijim i teže ga je otkriti automatiziranim sredstvima. Razlikuju se od trolova po tome što im je primarni cilj obmana o identitetu i stvaranje prividnog konsenzusa, a ne provociranje — iako se operateri ljudi u lutkama također mogu baviti trolanjem putem svojih lažnih identiteta.</p>\n<p>Kada osoba ili mala skupina zajedno upravljaju koordiniranim skupom sockpuppet računa, oni formiraju <em>sockpuppet mrežu</em> (vidi Mehanizmi pojačanja).</p>\n<h4>☑ Razlikovanje društvenih robota, trolova i sockpuppeta</h4>\n<table><thead><tr>\n<th><strong>Dimenzija detekcije</strong></th>\n<th><strong>Socijalni botovi</strong></th>\n<th><strong>Trolovi</strong></th>\n<th><strong>Sockpuppets</strong></th>\n</tr></thead><tbody>\n<tr>\n<td><strong>Karakteristike profila</strong></td>\n<td>- [ ] Čini se da je račun nedavno<br>stvoren- [ ] Profil je nepotpun ili<br>generički- [ ] Korisničko ime može izgledati neosobno i ponekad uključivati nasumične brojeve</td>\n<td>- [ ] Račun je obično aktivan duže vrijeme i ima povijest<br>objava- [ ] Profil je potpun i čini se osobnim; može sadržavati snažan ideološki ili politički opis<br>sebe- [ ] Korisničko ime izgleda osobno</td>\n<td>- [ ] Profil izgleda uvjerljivo i osobno, često s profilnom slikom i biografskim detaljima (ponekad ukradenim, generiranim umjetnom inteligencijom ili kopiranim)<br>- [ ] Povijest računa može biti umjerena i osmišljena da s vremenom izgleda autentično</td>\n</tr>\n<tr>\n<td><strong>Ponašanje pri objavljivanju</strong></td>\n<td>- [ ] Aktivnost ne odgovara normalnom ljudskom online<br>ponašanju- [ ] Računi objavljuju ili dijele sadržaj vrlo često-<br>[ ] Računi objavljuju ili dijele sadržaj u svako doba, danju i noću</td>\n<td>- [ ] Aktivnost podsjeća na normalno ljudsko<br>ponašanje na internetu- [ ] Račun objavljuje ili odgovara u nepravilnim<br>vremenima- [ ] Račun postaje aktivniji tijekom kontroverznih rasprava</td>\n<td>- [ ] Šabloni aktivnosti nalikuju normalnoj ljudskoj<br>upotrebi- [ ] Više računa kojima upravlja isti operater može pokazivati slična aktivna razdoblja ili<br>ritmove- [ ] Sockpuppet računi obično započinju manje rasprava i pišu kraće objave od tipičnih korisnika</td>\n</tr>\n<tr>\n<td><strong>Interakcije</strong></td>\n<td>- [ ] Račun ne vodi stvarne<br>razgovore- [ ] Računi uglavnom lajkaju, dijele ili<br>repostaju- [ ] Odgovori su kratki i automatizirani</td>\n<td>- [ ] Račun izravno odgovara drugim<br>korisnicima- [ ] Račun se upušta u rasprave s ciljem izazivanja<br>reakcija- [ ] Razgovori se produžuju kako bi se stvorio ili eskalirao sukob</td>\n<td>- [ ] Račun sudjeluje u stvarnim razgovorima, često podržavajući glavni račun operatera ili druge lutkarske<br>račune- [ ] Odgovori su kontekstualno prikladni i čine se<br>autentičnima- [ ] Parovi lutkarskih računa često komuniciraju u istoj raspravi u slično vrijeme</td>\n</tr>\n<tr>\n<td><strong>Značajke sadržaja</strong></td>\n<td>- [ ] Sadržaj je jednostran i<br>repetitivan- [ ] Iste se naracije objavljuju više puta</td>\n<td>- [ ] Sadržaj je posebno prilagođen da našteti ili provocira ciljanu<br>osobu- [ ] Sadržaj cilja pojedince ili društvene skupine</td>\n<td>- [ ] Sadržaj izgleda autentično i raznoliko na različitim<br>računima- [ ] Osnovna poruka ili stav sumnjivo su usklađeni unutar<br>mreže- [ ] Češća upotreba osobnih zamjenica kao što je &quot;ja&quot;</td>\n</tr>\n<tr>\n<td><strong>Jezik</strong></td>\n<td>- [ ] Općeniti izrazi, ponavljajuća formulacija s ključnim riječima</td>\n<td>- [ ] Raznolik, emotivan, često uvredljiv ili pogrdan jezik</td>\n<td>- [ ] Prirodan i raznolik<br>jezik- [ ] Više računa može dijeliti lingvističke otiske prsta (slično formuliranje, rječnik, interpunkcija ili obrasci pogrešaka)</td>\n</tr>\n<tr>\n<td><strong>Mrežni i tehnički pokazatelji</strong></td>\n<td>- [ ] Socijalni botovi prate druge socijalne botove, ali je odnos obično jednosmjeran i<br>neuzajamni- [ ] Koordinirano ponašanje uočljivo je na više računa botova</td>\n<td>- [ ] Trolovi prate račune stvarnih<br>korisnika- [ ] Povezivanja su često uzajamna (prate svoje pratitelje i obrnuto)<br>- [ ] Trolovi obično djeluju neovisno jedni o drugima</td>\n<td>- [ ] Više računa međusobno se povezuje na uzajamno poticajan način-<br>[ ] Povezanja mogu biti umjetno uzajamna između lažnih računa u istoj mreži ili namjerno izostavljena kako bi se izbjeglo<br>otkrivanje- [ ] Ista IP adresa, otisak uređaja ili obrazac prijave \\\\*(otkrivanje s platforme)\\\\*-<br>[ ] Ego-mreže su više grupirane nego kod običnih<br>korisnika- [ ] Vremenski usklađena aktivnost na različitim računima</td>\n</tr>\n</tbody></table>\n<div class=\"scilmi-callout scilmi-callout-info\">\n<div class=\"scilmi-callout-row\" aria-label=\"Info\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" d=\"M20 12C20 7.58172 16.4183 4 12 4C7.58172 4 4 7.58172 4 12C4 16.4183 7.58172 20 12 20C16.4183 20 20 16.4183 20 12ZM11 8C11 8.55228 11.4477 9 12 9C12.5523 9 13 8.55228 13 8C13 7.44772 12.5523 7 12 7C11.4477 7 11 7.44772 11 8ZM12 10C13 10 13 11 13 11V16C13 16 13 17 12 17C11 17 11 16 11 16V11C11 11 11 11 10.5 11C10 11 10 10 10 10H12Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><ul>\n<li>\nFerrara, E. (2023). Otkrivanje društvenih botova u doba ChatGPT-a: Izazovi i prilike. <em>First Monday, 28</em>\n(6). <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.5210&#x2f;fm.v28i6.13185\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.5210/fm.v28i6.13185</a>\n</li>\n<li>\nKumar, S., Cheng, J., Leskovec, J., &amp; Subrahmanian, V. S. (2017). An army of me: Sockpuppets in online discussion communities. <em>Proceedings of the 26th International Conference on World Wide Web (WWW &#x27;17)</em>\n, 857–866. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1145&#x2f;3038912.3052677\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1145/3038912.3052677</a>\n</li>\n<li>\nOrabi, M., Mouheb, D., Al Aghbari, Z., &amp; Kamel, I. (2020). Otkrivanje botova na društvenim mrežama: sustavni pregled. <em>Information Processing &amp; Management, 57</em>\n(4), 102250. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1016&#x2f;j.ipm.2020.102250\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1016/j.ipm.2020.102250</a>\n</li>\n<li>\nSolorio, T., Hasan, R., &amp; Mizan, M. (2013). Studija slučaja otkrivanja sockpuppeta na Wikipediji. <em>Zbornik radova radionice o analizi jezika u društvenim medijima (LASM) na konferenciji NAACL-HLT</em>\n, 59–68. Udruženje za računalnu lingvistiku.<a class=\"notranslate\" translate=\"no\" href=\"https&#x3a;&#x2f;&#x2f;aclanthology.org&#x2f;W13-1107&#x2f;\" target=\"_blank\" rel=\"noopener\">https://aclanthology.org/W13-1107/</a>\n</li>\n<li>\nTomaiuolo, M., Lombardo, G., Mordonini, M., Cagnoni, S., &amp; Poggi, A. (2020). Pregled otkrivanja trolova. <em>Future Internet, 12</em>\n(2), <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.3390&#x2f;fi12020031\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.3390/fi12020031</a>\n</li>\n<li>\nTsikerdekis, M., &amp; Zeadally, S. (2014). Otkrivanje prijevare s više računa u društvenim mrežama pomoću neverbalnog ponašanja. <em>IEEE Transactions on Information Forensics and Security, 9</em>\n(8), 1311–1321. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1109&#x2f;TIFS.2014.2332820\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1109/TIFS.2014.2332820</a>\n</li>\n<li>\nUyheng, J., Moffitt, J. D., &amp; Carley, K. M. (2022). Jezik i ciljevi internetskog trolanja: psiholingvistički pristup za društvenu kibernetičku sigurnost. <em>Information Processing &amp; Management, 59</em>\n(5), 103012. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1016&#x2f;j.ipm.2022.103012\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1016/j.ipm.2022.103012</a>\n</li>\n</ul></div>\n</div>\n</div>\n<h4>Mreže računa</h4>\n<h4>Farma botova</h4>\n<p><strong>Farmu</strong> <strong>botova</strong> čini mreža botova koji istovremeno rade na više uređaja ili poslužitelja, a raspoređuje je jedan operater ili organizacija u određenu svrhu.</p>\n<p>Bot farme imaju niz legitimnih primjena, uključujući indeksiranje weba, automatizirano testiranje softvera, agregaciju podataka i praćenje performansi web-stranica. Međutim, također se često koriste za zlonamjerne aktivnosti kao što su stvaranje lažne angažiranosti, generiranje velikih količina sadržaja, distribucija spama ili izvođenje kibernetičkih napada. Kada se koriste za manipulaciju internetskim diskursom, bot farme mogu stvoriti lažan dojam široke podrške, protivljenja ili interesa za neku temu, račun ili kampanju.</p>\n<h4>Farma trolova</h4>\n<p><strong>Farma</strong> <strong>trolova</strong> je organizirana skupina koordiniranih, često plaćenih radnika koji objavljuju namjerno provokativan, obmanjujući ili lažan sadržaj na internetu — obično putem lažnih računa. Njihov je cilj obično manipulirati javnim mnijenjem, širiti dezinformacije ili stvarati društvene i političke nemire. Farme trolova dokumentirane su u vezi s operacijama utjecaja koje sponzorira država, kao i s komercijalnom manipulacijom ugledom.</p>\n<h4>Mreža lažnih računa</h4>\n<p>Mreža lažnih računa koordinirani je skup lažnih računa kojima upravlja jedna osoba ili mala skupina, a koriste se za simuliranje neovisnih glasova koji podržavaju zajednički narativ, kampanju, račun ili cilj. Mreže lažnih računa često se koriste u političkom astroturfingu, manipulaciji recenzijama i ocjenama te koordiniranim dezinformacijskim kampanjama. Za razliku od farmi botova, mreže lažnih računa oslanjaju se na ručno upravljanje od strane ljudi, što čini sadržaj pojedinačnih računa autentičnijim i teže ga je otkriti automatiziranim sredstvima. Njihova koordinacija obično postaje otkriviva tek kada se više računa može povezati putem obrazaca ponašanja, zajedničkih tehničkih signala ili međusobnog angažmana.</p>\n<h4>Farma klikova</h4>\n<p><strong>Klik farma</strong> je operacija u kojoj se veliki broj slabo plaćenih radnika, automatiziranih botova ili oboje koristi za klikanje na oglase, praćenje računa na društvenim mrežama, lajkanje objava, ostavljanje recenzija ili preuzimanje aplikacija. Cilj je umjetno povećati online angažman ili promet, čineći da sadržaj, računi ili proizvodi izgledaju popularniji nego što zapravo jesu.</p>\n<h2>Fenomeni</h2>\n<h4>☑ Viralnost vs. popularnost</h4>\n<table><thead><tr>\n<th>Značajka</th>\n<th>Viralizacija</th>\n<th>U trendu</th>\n</tr></thead><tbody>\n<tr>\n<td><strong>Što se širi</strong></td>\n<td>Jedinstvena informacija: određeni video, objava, slika ili drugi dio sadržaja</td>\n<td>Tema, hashtag, zvuk, format ili klaster rasprave: ne jedan određeni element, već više objava koje se odnose na istu stvar ili je koriste</td>\n</tr>\n<tr>\n<td><strong>Glavni pokretači</strong></td>\n<td>Korisnici dijele, repostaju ili prosljeđuju informaciju drugima, koji je zatim dalje prosljeđuju; ovo kaskadno širenje može se dodatno pojačati algoritmima preporuka</td>\n<td>Mnogi korisnici u kratkom razdoblju objavljuju o istoj temi, spominju je ili koriste isti hashtag ili format; platforma otkriva tu koncentraciju aktivnosti i ističe je u posebnom odjeljku &quot;U trendu&quot; (poput popisa tema u trendu, pregleda hashtagova u trendu ili stranice s trenderskim zvukovima)</td>\n</tr>\n<tr>\n<td><strong>Vremenski obrazac</strong></td>\n<td>Često kratko i eksplozivno; može se ponoviti kasnije</td>\n<td>Vremenski ograničeno; traje dok god je aktivnost visoka ili dok god ga platforma nastavlja prikazivati</td>\n</tr>\n<tr>\n<td><strong>Kako se može manipulirati</strong></td>\n<td>Koordinirano dijeljenje, pojačavanje botovima, umjetna interakcija usmjerena na određeni informativni sadržaj</td>\n<td>Koordinirane kampanje objavljivanja, izmišljeni lažni trendovi putem mreža botova, odluke platforme o promoviranju, filtriranju ili potiskivanju</td>\n</tr>\n</tbody></table>\n<p><em>I viralnost i trendovi mogu nastati organski ili biti umjetno pojačani koordiniranim kampanjama, aktivnošću botova ili odlukama platforme. Oba također mogu dati prednost emocionalno poticajnom, moralno nabijenom ili podjeljivom sadržaju, osobito u političkim ili konfliktno orijentiranim kontekstima.</em></p>\n<h3>Viralnost</h3>\n<p>Šablona prema kojoj se određeni informativni sadržaj brzo širi dijeljenjem, preporukama i ponovnom cirkulacijom kroz mreže, analogno načinu na koji se virus širi. Viralnost oblikuju karakteristike sadržaja, strukture društvenih mreža, mogućnosti platformi, vremenski okvir i algoritamsko pojačavanje.</p>\n<p>Sadržaj koji izaziva snažne emocije, moralne reakcije ili animozitet prema vanjskim skupinama često se lakše dijeli, osobito u političkim ili konfliktno orijentiranim kontekstima. Međutim, viralnost nije određena samo veličinom izvornog izvora: i manji računi ili mediji također mogu proizvesti vrlo viralne sadržaje.</p>\n<p>Viralnost može nastati organski, ali se može i umjetno pojačati koordiniranim dijeljenjem, manipulacijom platformom ili aktivnošću botova.</p>\n<h3>U trendu</h3>\n<p>Status koji platforma dodjeljuje, a koji označava da je tema, hashtag, zvuk, format ili skupina rasprava dobila neuobičajeno koncentriranu aktivnost u kratkom vremenskom razdoblju.</p>\n<p>Popularnost se identificira algoritamski i prikazuje putem značajki platforme kao što su teme u trendu na X-u / Twitteru, trendovi hashtagova, trendovi zvukova, trendovi izazova ili druge značajke trendova specifične za platformu. Popularnost ovisi o signalima specifičnim za platformu kao što su broj objava, stopa rasta, angažman, lokacija, personalizacija i filtri moderacije.</p>\n<p>Teme koje generiraju visoku razinu angažmana — uključujući kontroverzne, emocionalno poticajne ili moralno nabijene teme — mogu imati veću vjerojatnost da će biti u trendu, ali to ovisi o sustavu rangiranja platforme i pravilima moderacije.</p>\n<p>Popularni trendovi mogu se organski pojaviti iz mnogih neovisnih doprinosa, ali na njih mogu utjecati i koordinirane kampanje, aktivnost botova ili odluke platforme o tome što promovirati, filtrirati, moderirati ili potisnuti.</p>\n<div class=\"scilmi-callout scilmi-callout-info\">\n<div class=\"scilmi-callout-row\" aria-label=\"Info\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" d=\"M20 12C20 7.58172 16.4183 4 12 4C7.58172 4 4 7.58172 4 12C4 16.4183 7.58172 20 12 20C16.4183 20 20 16.4183 20 12ZM11 8C11 8.55228 11.4477 9 12 9C12.5523 9 13 8.55228 13 8C13 7.44772 12.5523 7 12 7C11.4477 7 11 7.44772 11 8ZM12 10C13 10 13 11 13 11V16C13 16 13 17 12 17C11 17 11 16 11 16V11C11 11 11 11 10.5 11C10 11 10 10 10 10H12Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><ul>\n<li><strong>Berger, J. (2013).</strong> <em>Contagious: Why Things Catch On.</em> New York: Simon &amp; Schuster.\n</li>\n<li><strong>Berger, J. i Milkman, K. L. (2012).</strong>\nŠto čini online sadržaj viralnim? <em>Journal of Marketing Research</em>\n, 49(2), 192–205. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1509&#x2f;jmr.10.0353\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1509/jmr.10.0353</a>\n</li>\n<li><strong>Brady, W. J., McLoughlin, K., Doan, T. N., &amp; Crockett, M. J. (2021).</strong>\nKako socijalno učenje pojačava izražavanje moralnog ogorčenja na internetskim društvenim mrežama. <em>Science Advances</em>\n, 7(33), eabe5641. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1126&#x2f;sciadv.abe5641\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1126/sciadv.abe5641</a>\n</li>\n<li><strong>Jenkins, H., Ford, S., &amp; Green, J. (2013).</strong> <em>Spreadable Media: Creating Value and Meaning in a Networked Culture.</em> New York: NYU Press.\n</li>\n<li><strong>Lee, J., &amp; Umback, J. (2026).</strong>\nViralni zaokret: preispitivanje viralnosti u ekonomiji kreatora na TikToku. <em>Continuum</em>\n, 1–26. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1080&#x2f;10304312.2026.2625794\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1080/10304312.2026.2625794</a>\n</li>\n<li><strong>Maarouf, A., Pröllochs, N. i Feuerriegel, S. (2024.).</strong>\nViralnost govora mržnje na društvenim mrežama. <em>Proceedings of the ACM on Human-Computer Interaction</em>\n, 8 (CSCW1), 1–22. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1145&#x2f;3641025\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1145/3641025</a>\n</li>\n<li><strong>Rathje, S., Van Bavel, J. J., &amp; van der Linden, S. (2021).</strong>\nAnimizam prema vanjskim skupinama potiče angažman na društvenim mrežama. <em>Zbornik Nacionalne akademije znanosti</em>\n, 118(26), e2024292118. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1073&#x2f;pnas.2024292118\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1073/pnas.2024292118</a>\n</li>\n<li><strong>Rathje, S. i Van Bavel, J. J. (2025).</strong>\nPsihologija viralnosti. <em>Trends in Cognitive Sciences</em>\n, 29(10), 914–927. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1016&#x2f;j.tics.2025.06.014\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1016/j.tics.2025.06.014</a>\n</li>\n<li><strong>Rogers, E. M. (2003).</strong> <em>Diffusion of Innovations</em> (5. izd.). New York: Free Press.\n</li>\n<li><strong>Sangiorgio, E., Cinelli, M., Cerqueti, R., &amp; Quattrociocchi, W. (2024).</strong>\nPratioci ne određuju viralnost medijskih kuća na društvenim mrežama. <em>PNAS Nexus</em>\n, 3(7), pgae257. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1093&#x2f;pnasnexus&#x2f;pgae257\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1093/pnasnexus/pgae257</a>\n</li>\n<li><strong>Schlessinger, J., Garimella, K., Jakesch, M. i Eckles, D. (2023).</strong>\nUtjecaji algoritamske promocije trendova: dokazi iz koordiniranih kampanja u Twitterovim temama trenutka. <em>Zbornik radova Međunarodne AAAI konferencije o webu i društvenim medijima (ICWSM)</em>\n, 17(1), 777–786. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1609&#x2f;icwsm.v17i1.22187\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1609/icwsm.v17i1.22187</a>\n</li>\n<li><strong>Schöne, J. P., Parkinson, B., &amp; Goldenberg, A. (2021).</strong>\nNegativnost se širi više od pozitivnosti na Twitteru nakon pozitivnih i negativnih političkih situacija. <em>Affective Science</em>\n, 2(4), 379–390. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1007&#x2f;s42761-021-00057-7\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1007/s42761-021-00057-7</a>\n</li>\n</ul></div>\n</div>\n</div>\n<h3>Učinci prelijevanja i epistemičko pranje</h3>\n<p>Proces kojim se informacije koje steknu vidljivost u jednom informacijskom okruženju — bilo kroz umjetno pojačanje, trendiranje ili urednički odabir — preuzimaju i dalje distribuiraju u druga informacijska okruženja ili sustave za pristup informacijama, čime se dosežu publike izvan izvornog okruženja.</p>\n<p>Prijenos može nastati kroz novinarsko izvještavanje, dijeljenje na više platformi, uredničku kuraciju ili redistribuciju potaknutu korisnicima.</p>\n<p>→ Istraživački nalaz podijeljen na znanstvenom forumu može se raspravljati na društvenim mrežama i sažeti pomoću AI asistenta. <br>→ Tema koju su botovi umjetno pojačali na platformi društvenih mreža mogu preuzeti novinari.</p>\n<p>→ Učinci prelijevanja mogu povećati doseg i pouzdanih i nepouzdanih informacija te mogu učiniti da se informacije čine šire utemeljenima nego što su izvorno bile.</p>\n<h4>Epistemičko pranje</h4>\n<p>Prijenos ne očuvava uvijek prividni status informacija. Kada kanali prijenosa i primanja imaju jače signale autoriteta ili vjerodostojnosti od izvornog kanala — akademski format, recenzija, formalna publikacija — sama informacija može se doživjeti kao vjerodostojnija jednostavno zato što je prešla na drugi kanal. Taj se učinak naziva epistemičko pranje: informacija stječe percipiranu vjerodostojnost prolaskom kroz kanale, bez stvarne promjene temeljnih tvrdnji ili dokaza. Iskorištava sklonost primatelja da vjerodostojnost kanala na kojem su naišli na informaciju pripišu samoj informaciji.</p>\n<p>→ Tvrdnja koja potječe iz anonimne objave na blogu može se citirati u predtisku, reproducirati u odgovoru generiranom umjetnom inteligencijom i na kraju citirati u recenziranom znanstvenom radu — pri čemu u svakom koraku stječe akademsku vidljivost i prividnu autoritet, dok temeljna tvrdnja ostaje nepromijenjena ili neprovjerena.</p>\n<div class=\"scilmi-callout scilmi-callout-success\">\n<div class=\"scilmi-callout-row\" aria-label=\"Success\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M12 20C16.4183 20 20 16.4183 20 12C20 7.58172 16.4183 4 12 4C7.58172 4 4 7.58172 4 12C4 16.4183 7.58172 20 12 20ZM9.26825 11.3599L10.9587 13.3885L14.7 8.40006C15.0314 7.95823 15.6582 7.86869 16.1 8.20006C16.5419 8.53143 16.6314 9.15823 16.3 9.60006L11.8 15.6001C11.4128 16.1164 10.645 16.1361 10.2318 15.6402L7.7318 12.6402C7.37824 12.216 7.43556 11.5854 7.85984 11.2318C8.28412 10.8783 8.91468 10.9356 9.26825 11.3599Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><p>Tim sa Sveučilišta u Göteborgu, predvođen medicinskim istraživačem, izumio je lažnu kožnu bolest nazvanu Bixonimania kako bi testirao hoće li AI sustavi usvajati i ponavljati medicinske dezinformacije. Predstavili su je kao navodno stanje povezano s izlaganjem plavom svjetlom s ekrana, sa simptomima poput bolnih, svrbežnih očiju i ružičaste nijanse na kapcima. Zatim su stvorili namjerno lažne predprinteve akademskog izgleda, posute očitim znakovima upozorenja — fiktivnim autorom s fotografijom generiranom umjetnom inteligencijom, nepostojećim sveučilištem i referencama na Akademiju Zvjezdane flote i USS Enterprise. Nature je izvijestio da su predprintevi od tada uklonjeni s Preprints.org. U roku od nekoliko tjedana, glavni chatbotovi s umjetnom inteligencijom počeli su reproducirati Bixonimaniju kao stvarno medicinsko stanje, u nekim slučajevima nudeći korisnicima objašnjenja ili savjete o zdravlju. Istovremeno, lažni je materijal citiran u barem jednom objavljenom radu, koji je u međuvremenu povučen, u časopisu <em>Cureus</em> iz naklade Springer Nature. Širenje: objave na forumima → lažni preprint → web-preglednici → odgovori chatbotova s AI → akademski citat</p></div>\n</div>\n</div>\n<div class=\"scilmi-callout scilmi-callout-info\">\n<div class=\"scilmi-callout-row\" aria-label=\"Info\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" d=\"M20 12C20 7.58172 16.4183 4 12 4C7.58172 4 4 7.58172 4 12C4 16.4183 7.58172 20 12 20C16.4183 20 20 16.4183 20 12ZM11 8C11 8.55228 11.4477 9 12 9C12.5523 9 13 8.55228 13 8C13 7.44772 12.5523 7 12 7C11.4477 7 11 7.44772 11 8ZM12 10C13 10 13 11 13 11V16C13 16 13 17 12 17C11 17 11 16 11 16V11C11 11 11 11 10.5 11C10 11 10 10 10 10H12Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><p>Stokel-Walker, C. (2026). Znanstvenici su izumili lažnu bolest. AI je ljudima rekao da je stvarna. Nature, 652(8110), 559-561. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1038&#x2f;d41586-026-01100-y\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1038/d41586-026-01100-y</a></p></div>\n</div>\n</div>\n<h1>Sužavanje informacija</h1>\n<p>Dok <em>Informacijska amplifikacija</em> (iznad) opisuje kako se vidljivost širi među korisničkim računima, <em>Informacijska suženost</em> opisuje suprotno: kako se raspon perspektiva koje dosežu pojedinog korisnika ili društvenu skupinu sužava. Dva različita mehanizma uzrokuju to suženje — <em>Filter Bubble</em> (algoritamski personalizacija) i <em>Echo Chamber</em> (samoprezentacija korisnika). Ta se dva često brkaju u popularnom diskursu, ali djeluju različito.</p>\n<h2>Filtracijska mjehurić</h2>\n<p><strong>Filtracijska mjehurić</strong> je izolirano informacijsko okruženje koje stvara <em>algoritamska personalizacija</em>, u kojem je korisnik sve više izložen sadržaju koji je u skladu s njegovim pretpostavljenim preferencijama i prošlošću ponašanjem, dok se sadržaj koji odstupa filtrira — obično bez znanja korisnika. Pojam je skovao Eli Pariser (2011.) kako bi opisao kako personalizacijski algoritmi na Googleu, Facebooku i sličnim platformama mogu stvoriti sustavne asimetrije u izloženosti na temelju korisničkih signala kao što su povijest klikova, lokacija i podaci s profila.</p>\n<p>Odredna značajka filtarskog mjehura jest <em>nenamjernost s korisnikove strane</em>: sužavanje generira optimizacija platforme, a ne korisnikov namjerni odabir izvora.</p>\n<div class=\"scilmi-callout scilmi-callout-warning\">\n<div class=\"scilmi-callout-row\" aria-label=\"Warning\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M12 20C7.58172 20 4 16.4183 4 12C4 7.58172 7.58172 4 12 4C16.4183 4 20 7.58172 20 12C20 16.4183 16.4183 20 12 20ZM12 15C12.5523 15 13 15.4477 13 16C13 16.5523 12.5523 17 12 17C11.4477 17 11 16.5523 11 16C11 15.4477 11.4477 15 12 15ZM12 14C13 14 13 13 13 13L13 10.5L13 8C13 8 13 7 12 7C11 7 11 8 11 8L11 13C11 13 11 14 12 14Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><p>Empirijska istraživanja uvelike su kvalificirala Pariserovu izvornu tezu. Istraživanja su pokazala da algoritamska personalizacija doista oblikuje ono što korisnici vide, ali se većina korisnika i dalje susreće s ideološki raznolikim sadržajem — djelomično zato što njihove vlastite društvene mreže uključuju različita gledišta, a djelomično zato što algoritmi ne izoliraju toliko potpuno kao što to sugerira popularni diskurs (Bakshy i dr., 2015.; Flaxman i dr., 2016.; Bruns, 2019.). Učinak filtracijske mjehurića je stvaran, ali obično slabiji nego što se pretpostavlja; selektivna izloženost prije interneta (npr. odabir novina ili TV kanala) u mnogim je slučajevima bila jača.</p></div>\n</div>\n</div>\n<div class=\"scilmi-callout scilmi-callout-info\">\n<div class=\"scilmi-callout-row\" aria-label=\"Info\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" d=\"M20 12C20 7.58172 16.4183 4 12 4C7.58172 4 4 7.58172 4 12C4 16.4183 7.58172 20 12 20C16.4183 20 20 16.4183 20 12ZM11 8C11 8.55228 11.4477 9 12 9C12.5523 9 13 8.55228 13 8C13 7.44772 12.5523 7 12 7C11.4477 7 11 7.44772 11 8ZM12 10C13 10 13 11 13 11V16C13 16 13 17 12 17C11 17 11 16 11 16V11C11 11 11 11 10.5 11C10 11 10 10 10 10H12Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><ul>\n<li>Pariser, E. (2011). <em>The Filter Bubble: What the Internet Is Hiding from You</em>. Penguin Press.\n</li>\n<li>\nBakshy, E., Messing, S., &amp; Adamic, L. A. (2015). Izloženost ideološki raznolikim vijestima i mišljenjima na Facebooku. <em>Science</em>\n, 348(6239), 1130–1132. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1126&#x2f;science.aaa1160\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1126/science.aaa1160</a>\n</li>\n<li>\nFlaxman, S., Goel, S., &amp; Rao, J. M. (2016). Filter bubbles, echo chambers, and online news consumption. <em>Public Opinion Quarterly</em>\n, 80(S1), 298–320. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1093&#x2f;poq&#x2f;nfw006\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1093/poq/nfw006</a>\n</li>\n<li>Bruns, A. (2019). <em>Postoje li filtracijske mjehuriće?</em> Polity Press.\n</li>\n</ul></div>\n</div>\n</div>\n<h2>Odjekna komora</h2>\n<p><strong>Odjekna komora</strong> je društveno informacijsko okruženje u kojem je korisnik prvenstveno izložen mišljenjima, tvrdnjama ili ideologijama koje potvrđuju njegova postojeća uvjerenja, dok se suprotna gledišta izostavljaju, odbacuju ili se aktivno diskreditiraju. Cass Sunstein (2017.) opisuje političke posljedice: kada se grupe izoliraju od vanjskih perspektiva, unutarnja uvjerenja se s vremenom intenziviraju i postaju ekstremnija (grupna polarizacija).</p>\n<p>Za razliku od <em>filtracijske mjehurića</em>, koji proizlazi iz algoritamske personalizacije, odjekna komora nastaje prvenstveno zbog <strong>samopobira korisnika</strong>: izbora koga pratiti, kojim se zajednicama pridružiti, kojim izvorima vjerovati i koje glasove zanemariti. Ti su izbori djelomično vođeni <em>potvrdnom pristranošću</em> — kognitivnom sklonosti traženju i vjerovanju informacijama koje su u skladu s postojećim uvjerenjima. Pojačavajući učinak proizlazi iz same društvene strukture, a ne iz nevidljivog algoritamskog filtriranja.</p>\n<p>C. Thi Nguyen (2020.) povlači konceptualnu razliku koja je važna za intervenciju:</p>\n<ul>\n<li><strong>Epistemička mjehurić</strong> je društvena struktura u kojoj su drugi relevantni glasovi jednostavno <em>odsutni</em>. Njegovi stanovnici ne čuju suprotne perspektive, ali ih ni ne odbacuju aktivno.\n</li>\n<li><strong>Odjekna komora</strong> u užem smislu je društvena struktura u kojoj se drugi relevantni glasovi <em>aktivno diskreditiraju</em>. Članovi mogu čuti suprotne perspektive, ali nauče ne vjerovati njihovim izvorima.\n</li>\n</ul>\n<p>Epistemički mjehur može se otvoriti uvođenjem novih informacija; odjekna komora se opire ispravljanju čak i kada se predstave vanjski dokazi, jer su izvori tih dokaza već delegitimirani.</p>\n<div class=\"scilmi-callout scilmi-callout-warning\">\n<div class=\"scilmi-callout-row\" aria-label=\"Warning\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M12 20C7.58172 20 4 16.4183 4 12C4 7.58172 7.58172 4 12 4C16.4183 4 20 7.58172 20 12C20 16.4183 16.4183 20 12 20ZM12 15C12.5523 15 13 15.4477 13 16C13 16.5523 12.5523 17 12 17C11.4477 17 11 16.5523 11 16C11 15.4477 11.4477 15 12 15ZM12 14C13 14 13 13 13 13L13 10.5L13 8C13 8 13 7 12 7C11 7 11 8 11 8L11 13C11 13 11 14 12 14Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><p>Empirijski radovi sugeriraju da su snažne, ideološki izolirane odjekne komore rjeđe nego što popularni diskurs implicira (Cinelli i dr., 2021.; Guess i dr., 2018.), ali gdje god postoje, mogu biti vrlo otporne na ispravljanje. Puko slaganje unutar grupe samo po sebi nije odjekna komora — odlučujuća značajka je aktivno isključivanje ili diskreditiranje vanjskih perspektiva.</p></div>\n</div>\n</div>\n<div class=\"scilmi-callout scilmi-callout-info\">\n<div class=\"scilmi-callout-row\" aria-label=\"Info\">\n<span class=\"scilmi-callout-icon\" aria-hidden=\"true\"><svg fill=\"currentColor\" width=\"24px\" height=\"24px\" viewBox=\"0 0 24 24\" xmlns=\"http://www.w3.org/2000/svg\"><path fill-rule=\"evenodd\" d=\"M20 12C20 7.58172 16.4183 4 12 4C7.58172 4 4 7.58172 4 12C4 16.4183 7.58172 20 12 20C16.4183 20 20 16.4183 20 12ZM11 8C11 8.55228 11.4477 9 12 9C12.5523 9 13 8.55228 13 8C13 7.44772 12.5523 7 12 7C11.4477 7 11 7.44772 11 8ZM12 10C13 10 13 11 13 11V16C13 16 13 17 12 17C11 17 11 16 11 16V11C11 11 11 11 10.5 11C10 11 10 10 10 10H12Z\"></path></svg></span>\n<div class=\"scilmi-callout-body\"><ul>\n<li>Sunstein, C. R. (2017). <em>#Republic: Divided Democracy in the Age of Social Media</em>. Princeton University Press.\n</li>\n<li>\nNguyen, C. T. (2020). Echo chambers and epistemic bubbles. <em>Episteme</em>\n, 17(2), 141–161. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1017&#x2f;epi.2018.32\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1017/epi.2018.32</a>\n</li>\n<li>\nCinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., &amp; Starnini, M. (2021). The echo chamber effect on social media. <em>PNAS</em>\n, 118(9), e2023301118. <a href=\"https&#x3a;&#x2f;&#x2f;doi.org&#x2f;10.1073&#x2f;pnas.2023301118\" class=\"use-hover-preview\" rel=\"noopener noreferrer nofollow\">https://doi.org/10.1073/pnas.2023301118</a>\n</li>\n<li>Guess, A., Lyons, B., Nyhan, B., &amp; Reifler, J. (2018). <em>Izbjegavanje odjekne komore o odjeknim komorama: Zašto je selektivna izloženost političkim vijestima istomišljenika rjeđa nego što mislite</em>. Bijela knjiga zaklade Knight.\n</li>\n</ul></div>\n</div>\n</div>","UPDATEDAT":"2026-05-08T06:56:38.108Z","LANG":"hr","ID":"3b24684f-982f-46f5-bc94-0c5e6b459760","TITLE":"Cirkulacija i vidljivost informacija","SOURCELANG":"en"}