Disinformation challenges tools and techniques to deal or live with it
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The document outlines the challenges posed by disinformation and misinformation in today's digital landscape, particularly emphasizing the role of social media and AI in spreading false information. It discusses various models and tools for detecting and countering misinformation, along with the importance of collaboration and media literacy to combat this issue. The European Union's action plan aims to enhance transparency, accountability, and resilience against disinformation among citizens and online platforms.
Disinformation challenges tools and techniques to deal or live with it
- 1. This project has received funding from the European Union DIGITAL-2021-TRUST-01. Contract number: 101083756 meddmo.eu Disinformation challenges, tools and techniques to deal or live with it Nikos Sarris Media analysis, veri fi cation and retrieval group (MeVer) Center for Research and Technology Hellas (CERTH)
- 2. Body Level One Body Level Two Body Level Three Body Level Four Body Level Five What happens is posted, but has what’s posted really happened? huge masses of information are produced by huge numbers of sources and delivered to huge audiences. Some is bound to be fake... the challenge How can we help people distinguish truth from lies?
- 3. Disinformation Misinformation Malinformation Information that is false and deliberately created to harm a person, social group, organization or country. Information that is false, but not created with the intention of causing harm. Information that is based on reality, used to inflict harm on a person, organization or country. INFORMATION DISORDER: Toward an interdisciplinary framework for research and policy making Claire Wardle, Hossein Derakhshan Council of Europe report DGI(2017)09
- 4. …as old as time A challenge… high cost in both cases {disinformation} {misinformation}
- 5. much harder today •the rapid growth of social media and the Internet has made it easier for false information to spread quickly and widely •people are often more likely to share or believe false information that confirms their preexisting beliefs or opinions (confirmation bias) •the rise of generative AI (e.g. deepfakes, ChatGPT) is making it increasingly difficult to distinguish between real and fake •political polarisation and the erosion of trust in traditional media sources has created an environment in which disinformation can thrive •disinformation campaigns are often carried out by governments, political organisations, and other powerful groups with an agenda to manipulate public opinion and undermine democratic processes
- 6. the role of social media •Amplification: algorithms, that actually reward users for sharing content, can amplify false information, making it more visible and easier to spread. •Speed of dissemination: false information to spread quickly and widely •Anonymity: sources can spread false information anonymously, making it harder to hold them accountable •Viral Sensationalism: False information that is sensational or attention- grabbing is more likely to go viral •Filter bubbles: users are commonly exposed to information that aligns with their existing beliefs and opinions, making it harder to challenge false information (debatable)
- 7. comes in many different shapes •Misleading headlines attract clicks and generate traffic, even if the content of the article doesn't support the headline •Hoaxes and conspiracies: Spread of false information with the intent to deceive, often involving politically motivated or sensational claims. •Fabricated stories with the intention of misleading people. •Satire and parody: although created for satirical purposes can be misinterpreted •Political propaganda: to promote a political agenda or discredit opponents. •Manipulated media: more attractive, persuasive and tempting to share
- 8. getting harder as we speak AI synthetic generation of text and visuals (combined?) is pushing the challenge to a next level Image synthetically generated by DALLE-2 Most text of this presentation up to this slide was generated by ChatGPT
- 9. the impact can be very high •Misleading the public: the spread false or misleading information can impact individuals' decision-making and beliefs. •Undermining democracy: false or misleading information can be used to manipulate elections and undermine trust in democratic institutions •Exacerbating conflicts: disinformation can fuel social and political tensions, leading to increased conflict and violence •Damaging reputations: the distribution of various rumors and claims is frequently used to harm the reputation of individuals and organizations •Impairing public health: conspiracy theories and dangerous misinformation, like non-scientifically based medical advice about causes, symptoms, and treatments
- 10. deal with detect model How can we… … (dis)information
- 11. Modeling (dis)information How can we decompose information into measurable signals of credibility
- 12. Contributor Content Context What can we find about the source of information? Does everything contextualise together? Does the posted content look reliable? The CCC model
- 13. Contributor: Who says? 1 2 3 4 5 Reputation: what do people think of this source? History: what is the past activity of this source? Presence: where does this source exist? Influence: what happens because of this source? Popularity: who follows this source?
- 14. Content: Sounds real? 1 2 3 4 5 Quality: what is the text/visual/audio style like? Popularity: what is the social interaction with it? Authenticity: has it been manipulated/ synthetically generated? History: can it be found in past publications? Reputation: how is it referenced by others?
- 15. Context: Does it stick together? 1 2 3 4 5 Cross-check: are there any similar reports? Diversity: are there multiple coherent reports? Provenance: how has this travelled through time? Influence: what happens because of this? Proximity: do source locations relate to events?
- 16. The ΑΜΙ model INFORMATION DISORDER: Toward an interdisciplinary framework for research and policy making Claire Wardle, Hossein Derakhshan Council of Europe report DGI(2017)09
- 17. The DACTA model Detecting, Assessing, Countering Training & raising Awareness
- 18. The BLOC model Nwala, A.C., Flammini, A. & Menczer, F. A language framework for modeling social media account behavior. EPJ Data Sci. 12, 33 (2023). https://doi.org/10.1140/epjds/s13688-023-00410-9 A language framework for modelling social media account behaviours consisting of symbols representing user actions and content Action alphabet T:: Post message P:: Reply to friend p:: Reply to non-friend π:: Reply to own post R:: Reshare friend’s post r:: Reshare non-friend’s post ρ:: Reshare own post Content alphabet t:: Text H:: Hashtag M:: Mention of friend m:: Mention of non-friend q:: Quote of other’s post ϕ:: Quote of own post E:: Media object U:: link (URL)
- 19. Modeling (dis)information Various models have been proposed The signals of credibility are many The most signals are addressed the better the assessment A wholistic view appears to be more constructive than a unit
- 20. Detecting (dis)information Some tools that can help measure signals of credibility
- 21. Assessment of each credibility signal Overall assessment of post credibility Overall assessment of each pillar of the model Assessment of trustworthiness Rule-based assessment of the credibility signals following the CCC model Further information provided for each pillar of the model
- 22. Evolution towards transparency Model evolved to more intuitive pillars: Activity - Network - Influence Automatic assessment was removed A lot more data was added to aid the users into making their own assessments
- 24. Visual heatmaps help users quickly understand the results 15 image analysis algorithms help uncover possible forgeries Heatmap overlay on image helps users spot manipulated areas Image Verification Assistant Quickly analyse images to identify possible forgeries Manual annotation space helps users discuss findings
- 25. Temporal video segmentation illustrating the manipulation probability of every segment Top level analysis for the entire video Image/video player window for detailed viewing Deepfake detection Assess the possibility of deepfake manipulation in image or video Detailed analysis per video segment
- 26. Example images from the most likely location illustrating visual similarity with the query image Map illustrating the most likely location for the image Window for detailed image viewing Location estimation Estimate the most likely location for an image based on visual cues
- 27. Objects and actions are automatically identified and added as tags Automatic image analysis creates short descriptions Assets are automatically classified to specific categories, as disturbing, NSFW or memes Multimedia Archive A platform where you can easily analyse, find and annotate media Any tag can be manually added by users
- 28. Results include images with flags of visual similarity to the query window Window for selecting part of image that search should be based on Search by visual similarity Find images that are visually and semantically similar Selecting to search for similar images
- 29. Results include images that are almost (visually) identical to the query image Search by visual similarity Find images that are visually nearly identical Selecting to search for near duplicate images
- 30. Results include video that includes scenes that are visually identical to scenes from the query video Search by visual similarity (video) Find videos that contain common scenes Selecting to search for near duplicate video Frames from the retrieved video Frames from the query video
- 31. Checks Google and Wolfram Knowledge Bases to return relevant results Fact-check a claim automatically Automatic fact-checking by Claimbuster Returns relevant fact-checks using the Google Fact- Check Explorer
- 32. Converts audio from an event into text and then searches for matches among the previously published fact- checks in the ClaimReview database Fact-check a live claim automatically Live, automated fact-checking during political events by Squash Relevant matches are chosen by human editors and displayed on users’ screens within seconds of the politician making the claim
- 33. Monitors Twitter accounts and alerts when a new tweet is classified as check-worthy Spot check-worthy posts Automatically notifying on check-worthy posts by ClaimHunter Beltrán, Javier et al. “ClaimHunter: An Unattended Tool for Automated Claim Detection on Twitter.” KnOD@WWW (2021).
- 34. Collaborative verification • Provides ways to easily discover content from various sources • Organises information in real-time shareable views • Provides access to internal and external archives • Allows use of many 3rd party tools • Easily exports findings to share or publish stories • Used in EDMO and hubs to easily share their findings
- 38. With fact-check retrieval features
- 39. Detecting (dis)information Various tools are available but none is the silver bullet Many more can be built, but still not the silver bullet Combining the findings from many tools is important Collaboration can help, but is hard to achieve
- 40. Dealing with (dis)information Can we eradicate all information that is false?
- 41. EU action plan •Increase transparency of online political advertising •Improve resilience of citizens against disinformation •Enforce accountability of online platforms and other actors for the spread of disinformation •Enhance cooperation between EU Member States, online platforms, and other stakeholders •Protect the confidentiality of journalists' sources and the freedom of the press https://digital-strategy.ec.europa.eu/en/library/action-plan-against-disinformation
- 42. Collaboration - EDMO and 14 hubs
- 43. Fact checking - 97 initiatives
- 44. Fact checking - IFCN signatories
- 45. Fact checking - EFCSN recently launched
- 46. Why don’t we just ask ChatGPT? Matthew R. DeVerna†, Harry Yaojun Yan, Kai-Cheng Yang, Filippo Menczer, Artificial intelligence is ineffective and potentially harmful for fact checking Observatory on Social Media, Indiana University, https://doi.org/10.48550/arXiv.2308.10800
- 47. Why don’t we just ask ChatGPT? Matthew R. DeVerna†, Harry Yaojun Yan, Kai-Cheng Yang, Filippo Menczer, Artificial intelligence is ineffective and potentially harmful for fact checking Observatory on Social Media, Indiana University, https://doi.org/10.48550/arXiv.2308.10800 does not significantly affect participants' ability to discern headline accuracy or share accurate news decreases beliefs in true headlines that it mislabels as false and increases beliefs for false headlines that it is unsure about
- 48. Why not let it self-regulate? Johnson T, Kromka SM. Psychological, Communicative, and Relationship Characteristics That Relate to Social Media Users' Willingness to Denounce Fake News. Cyberpsychol Behav Soc Netw. 2023 Jul;26(7):563-571. doi: 10.1089/cyber.2022.0204. Epub 2023 May 30. PMID: 37253156. 68% of social media users believe people should respond with a correction when they witness the sharing of misinformation 73% of users elect to ignore misinformation posted online self-esteem, argumentativeness, conflict style, and interpersonal relationships relate to users’ willingness to denounce (or ignore) disinformation. avoiding arguments on social media is easier than confrontation and this avoidance may take precedence if confrontation does not incentivise social capital benefits. users who are media literate and trained in argumentation can make a difference - but are there enough of such users?
- 49. Can platforms ban disinformation? Jahn, Laura & Kræmmer Rendsvig, Rasmus & Flammini, Alessandro & Menczer, Filippo & Hendricks, Vincent. (2023). Friction Interventions to Curb the Spread of Misinformation on Social Media, https://arxiv.org/abs/2307.11498 Do they want to? ✓ information (esp. false) is driving traffic (i.e. profit) into platforms Can they identify all false information with certainty? ✓ impossible as in most cases subjective views are involved (see next slide) Do we want them to? ✓ debatable as we also need to preserve freedom of speech (see next slide) Can they somehow help? ✓ Yes, by introducing transparency in their way of operation ✓ Yes, by introducing at least some fact-checking on clear cases ✓ Yes, by introducing friction, i.e. making it less easy for users to propagate questionable information*
- 50. Should we ban d/misinformation? Would it be ideal for the society to ban all false information? ✓ This would mean allowing only the truth How do we define truth? ✓ The correspondence theory: A belief is true if it corresponds to the way things actually are – to the facts ✓ The coherence theory: A belief is true if it is part of a coherent system of beliefs ✓ Pragmatist theories: Truth is satisfactory to believe. True beliefs will remain settled at the end of prolonged inquiry. Can we handle the truth? ✓ Imagine a society where everyone knows the truth about everything
- 51. Dealing with (dis)information All available tools should be used and more should built Fact-checking should be intensified in a coordinated way Media literacy should be organised centrally Transparency should be enforced on the platforms Laws and regulations should hold platforms, individuals and organisations accountable Strong coordination and collaboration is necessary
- 52. Thank you for your attention Nikos Sarris Senior Researcher Media analysis, veri fi cation and retrieval team Information Technologies Institute Center for Research and Technology Hellas mever.iti.gr - www.iti.gr - www.certh.gr Advisor on media technologies Athens Technology Centre http://ilab.atc.gr @nikossarris - https://www.linkedin.com/in/nsarris/
- 53. CONTACT us This project has received funding from the European Union DIGITAL-2021-TRUST-01. Contract number: 101083756 meddmo.eu Fact-Check by MedDMO @MEDDMOhub info@meddmo.eu