Information Disorder
\nWardle & Derakhshan (2017), in a foundational report for the Council of Europe, introduce Information Disorder as the umbrella term for false, misleading, or harmfully shared information in the public information environment. They distinguish three categories along two axes: whether the content is false or true, and whether it is shared with intent to harm.
\n| Type | \nContent | \nSharer's knowledge of falseness | \nIntent to harm | \n
|---|---|---|---|
| Misinformation | \nfalse or misleading | \ndoes not know | \nno | \n
| Disinformation | \nfalse or misleading | \nknows | \nyes | \n
| Malinformation | \ntrue | \n(not the criterion) | \nyes | \n
The three categories form an interconnected ecology: the same content item can move between them depending on who shares it and with what intent.
\nFake News
\nThe term fake news is widely used in public debate but analytically imprecise. It usually refers to false or misleading information presented in the style of news reporting. However, because the term can refer to different types of information disorder and is often used politically to discredit unwanted reporting, more precise terms such as misinformation, disinformation, fabricated content, false context, or manipulated content should be preferred.
\nMisinformation
\nMisinformation is false or misleading information shared by people who do not realise it is false. The sharer believes the content is accurate; there is no intent to deceive or harm.
\nMisinformation typically arises in fast-moving news situations (early reports with unconfirmed details), in passing along claims without checking accuracy, and in repeating information from trusted sources that turn out to be wrong. It can be as widespread and consequential as disinformation, despite the absence of malicious intent — its sharers are often genuinely motivated and trusted within their networks, which gives the content reach and perceived credibility (Lewandowsky et al., 2017).
\nMisinformation Susceptibility Factors
\n\n
\n\n\n\n
\n1. Cognitive factors — how information is processed
\n- \n
- Confirmation bias: People pay more attention to information that supports what they already believe, and scrutinise opposing information more harshly. — Nickerson (1998)\n \n
- Illusory truth effect: Claims can feel more believable simply because people have seen them before, even when they are false. — Unkelbach et al. (2019)\n \n
- Processing fluency: Information that is easy to read, familiar in wording, or visually clear feels more trustworthy than information that is harder to process. — Reber & Schwarz (1999)\n \n
- Low cognitive reflection: People who rely on quick, intuitive judgements rather than pausing to check are more likely to fall for false claims. — Pennycook & Rand (2019)\n \n
- Bullshit receptivity: Some people are inclined to find vague but impressive-sounding statements meaningful, even when they are empty. — Pennycook et al. (2015)\n \n
- Low numeracy / scientific literacy: People may struggle to evaluate statistics, graphs, risk statements, or scientific evidence; education alone is no automatic protection. — Kahan et al. (2017)\n
2. Affective / motivational factors — how emotions and identity shape judgement
\n \n - Emotional reasoning / reliance on emotion: People accept claims because they feel right, frightening, satisfying, or morally urgent, rather than because they have been checked. — Martel, Pennycook & Rand (2020)\n \n
- Specific emotions (anger, fear, moral outrage): Strong emotions can lead people to react quickly and share before checking; moral-emotional language is especially powerful in spreading political content. (Belief and sharing are distinct effects with different evidence.) — Brady et al. (2017); Martel et al. (2020)\n \n
- Identity-protective cognition: People process information in ways that protect their political, religious, or group identity, and resist evidence that would threaten it. (Closely linked to political identity in category 5.) — Kahan (2013, 2017)\n \n
- Conspiracy mindset: A general tendency to suspect hidden plots or powerful actors behind events makes conspiracy-style misinformation more plausible. — Douglas, Sutton & Cichocka (2017)\n
3. Social factors — whom and which groups people trust
\n \n - Source credibility / trust in the sender: How believable a claim feels depends on who appears to be saying it — a friend, an influencer, an expert, a politician, a news outlet, or an anonymous account. — Traberg & van der Linden (2022)\n \n
- Social proof / popularity cues: Likes, shares, views, and comments can act as credibility cues, although the effect depends on context. — Avram et al. (2020)\n \n
- In-group / partisan congruency: Information from "people like us", or aligned with a group's position, is accepted more easily. (Closely linked to political identity in category 5.) — Sultan et al. (2024)\n \n
- Network homophily / echo chambers: When people mostly encounter information through networks of similar others, certain views appear more widely shared than they really are. (See also D1G5.) — Cinelli et al. (2021)\n
4. Contextual / situational factors — the conditions under which people meet information
\n \n - Time pressure: When people have to judge information quickly, they distinguish true from false claims less accurately. — Sultan et al. (2022)\n \n
- Information overload: When too much information arrives at once, people fall back on shortcuts like headlines, emotion, source labels, or popularity. — Laato et al. (2020)\n \n
- Distraction / cognitive load: When attention is drawn elsewhere, people may share without checking accuracy, even if they could otherwise recognise the misinformation. — Pennycook et al. (2020)\n \n
- Crisis / uncertainty contexts (pandemics, war, disasters): Urgent need for explanation increases openness to false or premature claims. — Roozenbeek et al. (2020)\n \n
- Platform design: Feeds, notifications, recommendation systems, autoplay, and fast scrolling encourage reactive rather than reflective behaviour. (See also D1G1.) — Lorenz-Spreen et al. (2020)\n
5. Background and dispositional factors — who may be more or less vulnerable in specific contexts
\n \n - Age: Older adults sometimes distinguish true from false headlines better in studies, but share misinformation more often online. — Guess, Nagler & Tucker (2019)\n \n
- Education: Not an automatic shield; meta-analyses show no simple effect on misinformation discrimination. — Sultan et al. (2024)\n \n
- Political identity and prior beliefs: Shape which information feels plausible, threatening, trustworthy, or worth sharing. (Operates via identity-protective cognition in category 2 and in-group cues in category 3 — not purely a demographic variable.) — Sultan et al. (2024)\n \n
- Media literacy habits: People who compare sources, read beyond headlines, and recognise manipulation techniques rely less on emotional or social shortcuts. (Note: this is the learning goal of SciLMi itself — listed here as a documented protective disposition, not as a prerequisite.) — Guess et al. (2020)\n \n
\n
\n\n\n\n
\n- \n
- Avram, M., Micallef, N., Patil, S., & Menczer, F. (2020). Exposure to social engagement metrics increases vulnerability to misinformation. Harvard Kennedy School (HKS) Misinformation Review, 1(5). https://doi.org/10.37016/mr-2020-033
\n
\n - Brady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A., & Van Bavel, J. J. (2017). Emotion shapes the diffusion of moralized content in social networks. Proceedings of the National Academy of Sciences, 114(28), 7313–7318. https://doi.org/10.1073/pnas.1618923114 \n
- Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9), e2023301118. https://doi.org/10.1073/pnas.2023301118 \n
- Douglas, K. M., Sutton, R. M., & Cichocka, A. (2017). The psychology of conspiracy theories. Current Directions in Psychological Science, 26(6), 538–542. https://doi.org/10.1177/0963721417718261 \n
- Guess, A., Nagler, J., & Tucker, J. (2019). Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Science Advances, 5(1), eaau4586. https://doi.org/10.1126/sciadv.aau4586 \n
- Jones-Jang, S. M., Mortensen, T., & Liu, J. (2021). Does media literacy help identification of fake news? Information literacy helps, but other literacies don't. American Behavioral Scientist, 65(2), 371–388. https://doi.org/10.1177/0002764219869406 \n
- Kahan, D. M. (2013). Ideology, motivated reasoning, and cognitive reflection. Judgment and Decision Making, 8(4), 407–424.\n \n
- Kahan, D. M., Peters, E., Dawson, E. C., & Slovic, P. (2017). Motivated numeracy and enlightened self-government. Behavioural Public Policy, 1(1), 54–86. https://doi.org/10.1017/bpp.2016.2 \n
- Laato, S., Islam, A. K. M. N., Islam, M. N., & Whelan, E. (2020). What drives unverified information sharing and cyberchondria during the COVID-19 pandemic? European Journal of Information Systems, 29(3), 288–305. https://doi.org/10.1080/0960085X.2020.1770632 \n
- Lorenz-Spreen, P., Lewandowsky, S., Sunstein, C. R., & Hertwig, R. (2020). How behavioural sciences can promote truth, autonomy and democratic discourse online. Nature Human Behaviour, 4(11), 1102–1109. https://doi.org/10.1038/s41562-020-0889-7 \n
- Martel, C., Pennycook, G., & Rand, D. G. (2020). Reliance on emotion promotes belief in fake news. Cognitive Research: Principles and Implications, 5(1), 47. https://doi.org/10.1186/s41235-020-00252-3 \n
- Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175–220. https://doi.org/10.1037/1089-2680.2.2.175 \n
- Pennycook, G., Cheyne, J. A., Barr, N., Koehler, D. J., & Fugelsang, J. A. (2015). On the reception and detection of pseudo-profound bullshit. Judgment and Decision Making, 10(6), 549–563.\n \n
- Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., & Rand, D. G. (2020). Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention. Psychological Science, 31(7), 770–780. https://doi.org/10.1177/0956797620939054 \n
- Pennycook, G., & Rand, D. G. (2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, 188, 39–50. https://doi.org/10.1016/j.cognition.2018.06.011 \n
- Reber, R., & Schwarz, N. (1999). Effects of perceptual fluency on judgments of truth. Consciousness and Cognition, 8(3), 338–342. https://doi.org/10.1006/ccog.1999.0386 \n
- Roozenbeek, J., Schneider, C. R., Dryhurst, S., Kerr, J., Freeman, A. L. J., Recchia, G., van der Bles, A. M., & van der Linden, S. (2020). Susceptibility to misinformation about COVID-19 around the world. Royal Society Open Science, 7(10), 201199. https://doi.org/10.1098/rsos.201199 \n
- Sultan, M., Tump, A. N., Geers, M., Lorenz-Spreen, P., Herzog, S. M., & Kurvers, R. H. J. M. (2022). Time pressure reduces misinformation discrimination ability but does not alter response bias. Scientific Reports, 12(1), 22416. https://doi.org/10.1038/s41598-022-26209-8 \n
- Sultan, M., Tump, A. N., Ehmann, N., Lorenz-Spreen, P., Hertwig, R., Gollwitzer, A., & Kurvers, R. H. J. M. (2024). Susceptibility to online misinformation: A systematic meta-analysis of demographic and psychological factors. Proceedings of the National Academy of Sciences, 121(47), e2409329121. https://doi.org/10.1073/pnas.2409329121 \n
- Traberg, C. S., & van der Linden, S. (2022). Birds of a feather are persuaded together: Perceived source credibility mediates the effect of political bias on misinformation susceptibility. Personality and Individual Differences, 185, 111269. https://doi.org/10.1016/j.paid.2021.111269 \n
- Unkelbach, C., Koch, A., Silva, R. R., & Garcia-Marques, T. (2019). Truth by repetition: Explanations and implications. Current Directions in Psychological Science, 28(3), 247–253. https://doi.org/10.1177/0963721419827854 \n
☑ Logical Fallacies
\n\n
\n\n\n\n
\n- \n
- Check for hasty generalisations - Conclusion from too little evidence.\n \n
- Check for false dilemmas - Limiting options to two when more exist.\n \n
- Check for straw man arguments - Misrepresenting a position to refute it easily.\n \n
- Check for appeals to ignorance - Claiming truth due to lack of disproof.\n \n
- Check for appeals to authority - Assuming truth based on authority alone.\n \n
- Check for red herrings - Distracting from the main issue.\n \n
- Check for false causes - Confusing correlation with causation.\n \n
- Check for ad hominem - Attacking the person, not the argument.\n \n
- Check for ad populum - Arguing truth from popularity.\n \n
- Check for slippery slopes - Asserting one step leads to extremes.\n \n
- Check for circular reasoning - Using the conclusion as a premise.\n \n
\n\n
\nDisinformation
\nDisinformation is false or misleading information shared deliberately, by people who know it is false, with the intent to harm, deceive, or manipulate. Both the sharer's awareness of the falseness and the harmful intent are definitional features.
\nCommon forms include political influence operations, commercial deception, propaganda, fabricated content, and manipulated media. Disinformation is often produced by organised actors and amplified through Coordinated Inauthentic / Artificial Amplification (above).
\nMalinformation
\nMalinformation is true information shared with intent to harm. The content itself is accurate, but its release, framing, or timing is calculated to damage a person, group, or institution. The classical pattern is the deliberate movement of private, sensitive, or context-bound information into a public or harmful context.
\nExamples include the publication of leaked private communications to discredit a target, the release of accurate but stigmatising personal data (e.g. revenge porn), and the strategic disclosure of factually correct but contextually damaging information at moments calculated for maximum impact.
\nMalinformation is the least-discussed of the three categories because the content is not false, which places it outside fact-checking frameworks. But it forms a significant part of the information-disorder ecology (Wardle & Derakhshan, 2017).
\n\n
\n\n\n\n
\nThe boundaries are porous. The three categories often overlap or shift in practice:
\n- \n
- A piece of disinformation, once shared by people who genuinely believe it, becomes misinformation as it spreads further. The same content can sit in different categories depending on who shares it.\n \n
- Content can be partly true and partly false. Wardle (2017) lists seven forms within information disorder — including misleading content, false context, and manipulated content — not all false content is fully fabricated.\n \n
- Determining intent from outside is empirically hard. Without access to the sharer's knowledge state and motivation, the line between misinformation and disinformation often cannot be drawn definitively.\n
This is why the framework asks learners to explain why the boundary between misinformation and disinformation is often difficult to determine in practice.
\n \n
\n
\n\n\n\n
\n- \n
- Wardle, C., & Derakhshan, H. (2017). Information Disorder: Toward an interdisciplinary framework for research and policy making. Council of Europe Report DGI(2017)09. https://rm.coe.int/information-disorder-toward-an-interdisciplinary-framework-for-researc/168076277c\n \n
- Wardle, C. (2017). Fake news. It's complicated. First Draft News. https://firstdraftnews.org/articles/fake-news-complicated/\n \n
- Lewandowsky, S., Ecker, U. K. H., & Cook, J. (2017). Beyond misinformation: Understanding and coping with the "post-truth" era. Journal of Applied Research in Memory and Cognition, 6(4), 353–369. https://doi.org/10.1016/j.jarmac.2017.07.008 \n
- Vraga, E. K., & Bode, L. (2020). Defining misinformation and understanding its bounded nature. Political Communication, 37(1), 136–144. https://doi.org/10.1080/10584609.2020.1716500 \n
AI Hallucination / Confabulation
\nThe systematic tendency of generative AI systems to produce plausible-sounding content that is factually incorrect, fabricated, or unverifiable — including invented references, non-existent studies, misattributed quotations, made-up statistics, and false biographical, historical, or scientific details.
\nHallucinations occur because generative AI systems produce output by predicting plausible continuations from patterns learned during training, not by retrieving verified information from a knowledge source. The system optimises for fluency and plausibility, not for accuracy. Fabricated content is typically presented with the same confidence as accurate content — there is no internal signal that reliably distinguishes the two.
\nThe term confabulation is sometimes preferred over hallucination in research contexts, since confabulation in psychology denotes the construction of false but sincerely believed accounts without intent to deceive — closer to what generative systems actually do. Hallucination remains the dominant term in public and technical discourse and is retained here.
\nHallucinations are not occasional bugs. They are a structural feature of the underlying generation process, not errors that can be reliably eliminated through better prompting (Kalai & Vempala, 2024). They occur across all generative systems — including ☑ RAG AI, where fabrication can also affect how retrieved sources are summarised, quoted, or attributed.
\nDidactic positioning: Hallucinations can be located conceptually as a forth category alongside misinformation, disinformation, and malinformation. Unlike both, they are not produced by a human source with intent or error in mind, but emerge from the generation process itself — system-generated false content without an intentional source. This three-way distinction is offered here as a pedagogically useful framing rather than as established consensus terminology.
\nCommon types:
\n- \n
- fabricated references — citations to articles, books, or studies that do not exist\n \n
- misattributed or fabricated quotations\n \n
- invented statistics or numerical claims\n \n
- inaccurate biographical, historical, or scientific details presented confidently\n \n
- confidently incorrect summaries of real sources\n \n
\n
\n\n\n\n
\nTreat any factual claim from a generative AI system as unverified until checked against an independent source. References and quotations are particularly easy to verify — search whether the cited source exists and contains what the AI claimed.
\n
\n\n\n\n
\n- \n
- Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21), 610–623. https://doi.org/10.1145/3442188.3445922 \n
- Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), Article 248. https://doi.org/10.1145/3571730 \n
- Kalai, A. T., & Vempala, S. S. (2024). Calibrated language models must hallucinate. Proceedings of the 56th Annual ACM Symposium on Theory of Computing (STOC 2024), 160–171. https://doi.org/10.1145/3618260.3649777 \n
- Smith, A. L., Greaves, F., & Panch, T. (2023). Hallucination or confabulation? Neuroanatomy as metaphor in large language models. PLOS Digital Health, 2(11), e0000388. https://doi.org/10.1371/journal.pdig.0000388 \n
Content Misrepresentation
\n| Format | \nManipulated | \nFabricated | \n
|---|---|---|
| Text | \ngenuine quote stripped of context, doctored headline | \ninvented quote, fictional article, AI-generated text | \n
| Image | \nretouched photograph, cropped original | \nAI-generated image, painted fake "photograph" | \n
| Audio | \nedited or sped-up original recording | \nvoice clone, AI-generated speech | \n
| Video | \ncheap fake, recut clips | \nfully synthetic deepfake video | \n
| Document | \nforged letterhead on modified template | \ninvented "official" letter | \n
Fabricated Content
\nFabricated content is information — text, image, audio, video, document, or other format — that has been entirely invented, with no genuine source material. It may be produced manually (invented quotes, fictional news articles) or generated synthetically by AI systems (synthetic videos, voice clones, AI-generated images, AI-written text).
\nManipulated Content
\nManipulated content is geniune information — text, image, audio, video, document, or other format — that has been altered to deceive. The source material is real, but it is modified through editing, selective cropping, speed adjustment, recontextualisation, framing changes, voice substitution, or other transformations that change its meaning or apparent context.
\nCheap Fake
\nA cheap fake is media that has been altered using conventional, widely available tools — image editing software, video editing software, or basic recontextualisation — rather than AI-based generative methods. The term was coined by Paris and Donovan (2019) to draw attention to the fact that the most consequential forms of manipulated media in public discourse are typically not sophisticated AI-generated deepfakes, but simple, low-cost techniques that anyone can apply.
\nCommon cheap fake techniques include:
\n- \n
- speed manipulation (e.g. the slowed-down Pelosi video, 2019)\n \n
- recutting and selective editing of audio or video\n \n
- recontextualisation (real media presented with a false caption or framing)\n \n
- photo retouching, cropping, or compositing\n \n
- swapped captions and doctored screenshots\n \n
- audio splicing\n \n
Whether a cheap fake constitutes Misinformation, Disinformation, or Malinformation depends on the actor's knowledge and intent (→ Information Disorder), not on the technique itself. The same edited video can serve satire, fiction, education, or deception.
\nCompared with Deep Fakes, cheap fakes require less technical skill but are not necessarily easier to detect — well-executed recontextualisation or selective editing can be extremely difficult to identify without access to the original source material.
\n\n
\n\n\n\n
\n- \n
- Paris, B., & Donovan, J. (2019). Deepfakes and Cheap Fakes: The Manipulation of Audio and Visual Evidence. Data & Society Research Institute. https://datasociety.net/library/deepfakes-and-cheap-fakes/\n \n
\n
\n\n\n\n
\nCheap Fake Verification Tools:
\n- \n
- https://deepfake-total.com/\n \n
- https://scanner.deepware.ai/\n \n
- https://deepfake-detect.com/\n \n
- https://www.duckduckgoose.ai/deepfakeproof\n \n
- https://deepfakedetector.pro/\n \n
- https://illuminarty.ai/en/\n \n
- https://www.aiornot.com/\n \n
- https://www.fakeimagedetector.com/about-us/\n \n
- https://weverify.eu/verification-plugin/\n \n
Deep Fake
\nDeep Fake
\nA deep fake is synthetic media generated by artificial intelligence — specifically using deep learning techniques such as generative adversarial networks (GANs), diffusion models, or transformer-based generators. The term is a portmanteau of deep learning and fake, originating in 2017 in online communities producing AI-generated face-swap videos. The technology has since expanded across media types and is now broadly accessible through consumer-level tools.
\nCommon forms include:
\n- \n
- Face swaps: replacing one person's face with another's in video\n \n
- Voice cloning: synthesising a speaker's voice from a small audio sample\n \n
- Full synthetic video: AI-generated footage showing people, events, or scenes that never occurred\n \n
- Synthetic photography: AI-generated still images of fictional people or events\n \n
- Text-to-video and text-to-audio: generating media from written prompts\n \n
Whether a deep fake constitutes Misinformation, Disinformation, or Malinformation depends on the actor's knowledge and intent (→ Information Disorder), not on the technique itself. Deep fake methods are also used legitimately in film production, accessibility tools (synthetic voices for people who have lost theirs), language dubbing, satire, education, and the arts.
\nCompared with Cheap Fakes, deep fakes require more technical capacity to produce convincingly and can achieve higher visual or auditory realism. They are not, however, the dominant form of media manipulation in public discourse — Paris and Donovan (2019) note that simpler cheap-fake techniques remain more common and often more consequential. Detection is an active research area; current approaches combine artefact analysis, biological inconsistency checks, and provenance verification (e.g. C2PA content credentials).
\n\n
\n\n\n\n
\n- \n
- Paris, B., & Donovan, J. (2019). Deepfakes and Cheap Fakes: The Manipulation of Audio and Visual Evidence. Data & Society Research Institute. https://datasociety.net/library/deepfakes-and-cheap-fakes/\n \n
- Chesney, R., & Citron, D. K. (2019). Deep fakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107(6), 1753–1820.\n \n
- Vaccari, C., & Chadwick, A. (2020). Deepfakes and disinformation: Exploring the impact of synthetic political video on deception, uncertainty, and trust in news. Social Media + Society, 6(1), 1–13. https://doi.org/10.1177/2056305120903408 \n
\n
","UPDATEDAT":"2026-05-26T06:52:57.587Z","ID":"8e97ece5-27a9-44ca-b212-ce9ce8c29387","TITLE":"Misinformation & Manipulation"}\n\n\n
\nDeep Fake Verification Tools:
\n- \n
- https://deepfake-total.com/\n \n
- https://scanner.deepware.ai/\n \n
- https://deepfake-detect.com/\n \n
- https://www.duckduckgoose.ai/deepfakeproof\n \n
- https://deepfakedetector.pro/\n \n
- https://illuminarty.ai/en/\n \n
- https://www.aiornot.com/\n \n
- https://www.fakeimagedetector.com/about-us/\n \n
- https://weverify.eu/verification-plugin/\n \n