O slideshow foi denunciado.
Seu SlideShare está sendo baixado. ×

Monitoring, Understanding and Influencing the Co-Spread of COVID-19 Misinformation and Fact-checks

Anúncio
Anúncio
Anúncio
Anúncio
Anúncio
Anúncio
Anúncio
Anúncio
Anúncio
Anúncio
Anúncio
Anúncio

Confira estes a seguir

1 de 27 Anúncio

Monitoring, Understanding and Influencing the Co-Spread of COVID-19 Misinformation and Fact-checks

Correcting misconceptions and false beliefs is important for inserting reliable information about COVID-19 into public discourse, but what impact does this have on the continued proliferation of misinforming claims? How can we track their impact over time? What is the best way to inform individuals about the misinformation they share? Using more than 3 years of data collected from Twitter and fact-checking organisations, we discuss the relationship between fact-checking and misinformation across topics and demographics. We then proceed to show how the Fact-checking Observatory, a website that generates human-readable weekly reports automatically about the spread of covid-related misinformation and fact-checks can be used for monitoring such information over time. Finally, we analyse early results about the effectiveness of our Twitter bot in reducing individual sharing of misinforming content.

Correcting misconceptions and false beliefs is important for inserting reliable information about COVID-19 into public discourse, but what impact does this have on the continued proliferation of misinforming claims? How can we track their impact over time? What is the best way to inform individuals about the misinformation they share? Using more than 3 years of data collected from Twitter and fact-checking organisations, we discuss the relationship between fact-checking and misinformation across topics and demographics. We then proceed to show how the Fact-checking Observatory, a website that generates human-readable weekly reports automatically about the spread of covid-related misinformation and fact-checks can be used for monitoring such information over time. Finally, we analyse early results about the effectiveness of our Twitter bot in reducing individual sharing of misinforming content.

Anúncio
Anúncio

Mais Conteúdo rRelacionado

Mais recentes (20)

Anúncio

Monitoring, Understanding and Influencing the Co-Spread of COVID-19 Misinformation and Fact-checks

  1. 1. h  ❆ ❆  Monitoring, Understanding, and Influencing the Co-Spread of COVID-19 Misinformation and Fact-checks Grégoire Burel Knowledge Media Institute, The Open University, UK Milton Keynes, 31 July 2022
  2. 2. ► Does fact-checking impact misinformation spread? ► Can we affect spreading behaviour? ► How to present the evolution of spread over time automatically? Misinformation and Fact-Checking during COVID-19 We monitor and analyse the propagation of misinformation and fact-checks on social media and investigate methods for influencing spreading behaviour. More than 16k fact-checks were published during COVID-19 Misinformation still spreads twice as much…* *For fact-checked content. 2
  3. 3. Influencing 3 We push fact-checks to misinformation spreaders and check who might be influenced. - What types of messages are the most successful? - What are the reactions of misinformation spreaders? Identify misinformation spreaders and push fact- checks. Understanding 2 We analyse if fact-checks impact the spread of misinformation. - Who spreads misinformation/fact-checks? - What topics are resistant to fact- checking? - Does fact-checking impacts misinformation spread? A large-scale study on the effectiveness of fact- checking across topics, demographics and time. Monitoring 1 We track the spread of COVID- 19* misinformation and fact- checks on Twitter using claim reviews and create weekly reports about the reach of misinforming posts and their corresponding fact-checks on Twitter. twitter *We also track Russo-Ukrainian war misinformation. A continuously updated database of misinformation and fact- checked URL mentions on Twitter and weekly spread reports. Fact-checking Observatory 3
  4. 4. The Misinformation and Fact-checks Database / The Fact-checking Observatory Monitoring and Visualising 1
  5. 5. The Misinformation and Fact-checks mentions database. Monitoring and Visualising 1 In order to track the spread of COVID-19* misinformation and fact-checks on Twitter we need to: 1. Identify and collect URLs pairs that connect misinforming content to fact- checks. 2. Track their mentions on social media. We use the Claim Reviews published IFCN signatories found in the Corona Virus Facts alliance database. 5
  6. 6. The Misinformation and Fact-checking mentions database. Monitoring and Visualising 1 Fact-checking URLs Data Collection Twitter Data Collection Tweets Claim Reviews Demographics Extraction FCO Database Topics Continuous tracking Misinf0/ FC URLs 16,460+ COVID-19 Fact-checks 491,400+ COVID-related Tweets twitter 6
  7. 7. Weekly Misinformation and Fact-checking Reports. Monitoring and Visualising 1 7
  8. 8. ► What misinformation keeps spreading? ► What fact-check spreads the most? Weekly automatically generated reports on the spread of misinformation and fact-checks that include: 1. Key content and topics. 2. Fact-checking coverage. 3. Demographic impact. The Fact-checking Observatory. The weekly reports help identifying the evolution of key topics and key misinforming content. Monitoring and Visualising 1 8
  9. 9. Spread ratio Fact-checking delay Topic spread Fact-checker reports Claim trend Claim reports Top spreaders First/last spread Most/least successful claims Associated claims/fact-checks Demographic impact Sharers locations What’s next? Monitoring and Visualising 1 Spread evolution 9
  10. 10. Demographics and topics impact on the co- spread of COVID-19 misinformation and fact- checks on Twitter Understanding 2
  11. 11. Should all misinformation be fact-checked in the same way? What is the relation between misinformation and fact-check spread? 1. Do misinformation and fact-checking information spread similarly? - Non-parametric MANOVA/ANOVA. 2. Does fact-checking spread affect the diffusion of misinformation about Covid- 19? - Weak causation analysis. - Impulse response analysis and FEVD. Who is most likely to spread misinformation / facts ? Do fact-checks reduce misinformation spread? 7,370 Misinforming URLs 9,151 Fact-checking URLs 358,776 Tweets Analysis on data collected until 4th January 2021 Jan 2020 Apr 2020 Understanding 2 11 0 – 3 days 4 – 10 days 10+ days initial early late Analysed Periods
  12. 12. Understanding 2 Vaccines Symptoms Spread Other Cures Conspiracy Theories Causes Authorities Fact-check URLs shares Misinformation URLs shares Topical misinformation and fact-checks spread (log scale). Gender* Male Female Account type Non-org. Org. User Age ≥40 30-39 19-29 ≤18 Demographic misinformation and fact-checks spread (log scale). Topics and Demographics 12
  13. 13. ► Already fact-checked content re-spreading. ► Conspiracies and causes need to be addressed differently than other topics. ► Fact-checkers republishing/reposting policy? Stacked cumulative spread of misinforming and corrective information. Global and topical spreading differences. Initial onset period until mid-March. Late period from mid-September. Ramp-up period from mid-March until mid- September. Jan 2020 Apr 2020 Jul 2020 Oct 2020 Jan 2120 2x more misinform ation. Understanding 2 0 – 3 days 4 – 10 days 10+ days initial early late Global Topics ≠ “Converging” behaviour. = ≠ ≠ ≈/ ≠ =/ ≠ Period Causes and conspiracies still spreading differently in the late phase. 13
  14. 14. Short-term demographics differences. Individuals vs. Organisations Gender* Understanding 2 - Individuals spread more misinformation than organisations. - Most organisation-driven spread occurs in the initial period. - Individual spread of misinformation continuing over long periods. Individuals exposure to fact-checked content over long periods is key. - Females spread less misinformation than males (but represent 40% of Twitter userbase) - Misinformation spread is independent of gender. - Same spreading behaviour → ∞. Gender is not important when dealing with misinformation spread. 14
  15. 15. Fact-checking fast spread response Inconclusive misinformation response trend Self initial response (spread drop soon after initial increase) - Bidirectional weak causation between misinformation and fact-checks spread. - Fact-checking spread not clearly impacting misinformation spread (impulse response and FEVD). - Fact-checks are quick to respond to misinformation spread. Weak impact of fact-checking spread on misinformation spread*. Understanding 2 ► Make fact-checking content more sharable? ► Keep spreading fact-checks? How to increase the impact/spread of fact- checking content? *globally for fact-checked content but not for all the topics.. 15
  16. 16. Short term success in reducing misinformation spread. Hard to affect irrational misinformation spread. Virus Causes Misinformation spread increasingly dependent on fact-checks spread Fact-checking spreading initially independently Fact-checking initially affecting misinformation. Fast fact- checking response Inconclusive misinformation response trend Understanding 2 Conspiracy Theories 16
  17. 17. ► Conspiracies and causes need to be addressed differently than other topics. Topic Co-Spread ► No need to target gender specifically. ► Targeting long individual exposure to misinformation. ► Make fact-checking content more sharable? ► Keep spreading fact-checks? Misinformation and fact-checking spread. - Misinformation spreads more than fact-checks. - Fact-checking is fast to spread initially in response to misinformation spread. - Weak bi-directional relation between fact-checks and misinformation spread. Demographic Co-Spread Overall Misinformation and Fact-checking Spread - Misinformation spreads independently from gender. - Individuals spread more misinformation over long periods. - Misinformation topics continue spreading over long time periods . - Fact-checking spread impact on individual topics tend to be short-term. Understanding 2 17
  18. 18. Automatically Pushing Fact-checks to Misinformation Spreaders Influencing 3
  19. 19. 19 Preliminary Work Can we influence misinformation spreading behaviour by pushing fact- check content? Influencing 2 - Individuals share more misinformation compared to organisations. - Users spread misinformation and credible information at different rates (e.g., some users share little misinformation while other share a large amount). User behaviour does not impact others at the same level (e.g., celebrity have high reach). - Pushing corrective information may be helpful in reducing misinformation spread. How do we measure influencing/influence behaviour? How consistent is user behaviour? Can we change user behaviour toward misinformation by exposing them to fact-checks?
  20. 20. - Most users spread both credible information and misinformation → This population should be the focus of behaviour altering methods. - Purely informing and misinforming users seems to account for a similar proportion of the population. Influencing Behaviour - Overlapping Behaviour Informing (13%) Misinforming (15%) Informing - Misinforming (61%) Neutral (11%) 20 Influencing 2 Dataset 100k Twitter seed posts containing misinformation or fact-checks and 52k users. Data sample based on 7.5% of the retrieved users: 3,809 Users (≈ 10M tweets) Measuring Behaviour - Adjusted behaviour score between 0 and 1. - Inspired by Hochschild and Einstein (2015) behaviour states. - Uses user timelines and historical tweets over a 25 days period. - Fitted Adjusted Percentile Ranks rather than min-max normalisation (better at identifying standard behaviour and outliers since it is based on population behaviour).
  21. 21. Influencing Behaviour - Behaviour Consistency - Users that are more actively informed have more consistent behaviour → always spread credible information. - Users with an adjusted score < 55% are highly unstable → more opportunistic behaviour. - As with informing behaviour, convergence occurs for users that have a high score. - Misinformation spreading behaviour is only stable for users that share misinformation with an adjusted score > 80% → only users with high misinforming score spread misinformation consistently. Informing Consistency Misinforming Consistency 21 Influencing 2 Methods for altering user behaviour towards misinformation should target users that elicit mixed behaviour.
  22. 22. Misinformation Bot How can we reach those not on the choir? - Don’t think they need fact-checking tools… - Don’t know about such tools... - Not tech-savvy enough to install and use them... Approach: 1. Search for mentions of misinformation on Twitter. 2. Use templates for notifying misinformation to user. 3. No installation required. 4. Corrections can be seen by anyone. We need tools to push fact-checks when and where necessary. 22 Influencing 2
  23. 23. Reply Templates Style Please, note that the link you shared contains a claim that was fact-checked and appears to be <VERDICT>. Fact-check: <FACT-CHECK-URL> I’m a research bot fighting misinformation spread. Plz follow me & DM any feedback. Factual Oops… it seems something might be wrong! The link you shared contains a claim that was fact-checked <FACT-CHECK-URL> and appears to be <VERDICT>. I’m a research bot fighting misinformation spread. Plz follow me & DM any feedback. Alerting I’m a bot fighting misinformation spread. I noticed the link you shared contains a claim that was fact-checked <FACT-CHECK-URL> and appears to be <VERDICT>. Plz follow me & DM any feedback. Identity How about double-checking this? This link contains a claim that was fact-checked <FACT-CHECK-URL> and appears to be <VERDICT>. I’m a research bot fighting misinformation spread. Plz follow me & DM any feedback. Suggestive I know, it's hard to distinguish fact from fiction 😩. The link you shared contains a claim that was fact-checked and appears to be <VERDICT>. Fact-check: <FACT-CHECK-URL>. I’m a research bot fighting misinformation spread. Plz follow me & DM any feedback. Empathetic Misinformation can be really harmful! 😬 Please, note that the link you shared contains a claim that was fact-checked and appears to be <VERDICT>. Fact- check: <FACT-CHECK-URL>. I’m a research bot fighting misinformation spread. Plz follow me & DM any feedback. Alarming Hi there! Please note that the link you shared contains a claim that was fact-checked and appears to be <VERDICT>. Fact-check <FACT-CHECK-URL>. I’m a research bot fighting misinformation spread. Plz follow me & DM any feedback. Friendly 23
  24. 24. Distrust fact-checkers “fact-checkers are paid by pharma industry”, “controlled by Facebook and government”, “they stop diversity of opinion”, “who checks the fact-checker?”, “who’s paying them?” Follow anti-fact-checking sites cite far-right websites that speak against fact-checkers - eg einprozent.de. (https://www.einprozent.de/correctiv-das- zensurwerkzeug-der-elite ) Distrust governments “if detox didn’t work why would money be paid into telling you NOT to do this after shot. Remember a billion $ was put into #Vaccine “awareness & promotions” in US alone.” Seek other supporting articles If there’s another article with similar claims that is not fact-checked then they feel they won the argument. Refer to non-related claims Search claims to support their position, e.g., against a vaccine – “what about this, eh?” Work in network They retweet and like each other’s tweets against the bot’s reply Discrediting a source Point to officials who said something not entirely accurate to bring in doubt and reason to distrust everything Accuse of censorship “Freedom of speech”, “a contested opinion is still an opinion”, “this is censorship”, “police state”, “ministry of truth” Anti-bots “you are a bot” , “you are a big pharma bot”, “When ‘They’ send a fact bot after me …then i know I’m on to something” Reactions… to 745 bot posts so far. 24 Influencing 2
  25. 25. Reactions… to 745 bot posts so far. - Very few positive outcomes so far. - Blocking and replying as a common pattern. - People delete their posts but also block the bot. - No clear relation between reply template and behaviour → more personal message may be necessary. 25 Influencing 2
  26. 26. Future Directions 26 26 26 1 2 3 Monitoring - COVID-19 misinformation tracking Understanding – Fact-checking impact Influencing - Misinformation countering - New types of reports (e.g., Individual claims reports and history). - One-off reports (e.g., small detailed studies). - Generic misinformation tracking. - Updating the co-spreading analysis between fact-checks and misinformation. - Additional demographics (e.g., age groups, values, etc.) - Personalisation of responses depending on user (e.g., conspiracy theorist, influencer, etc.). - Behaviour targeting. - Visual templates.
  27. 27. ❆  h  ❆ ❆ h  Thank you. Grégoire Burel. @evhart fcobservatory.org g.burel@open.ac.uk Monitoring, Understanding and Influencing the Co-Spread of COVID-19 Misinformation and Fact-checks Background illustrations: Myth busters created by Redgirl Lee for United Nations Global Call Out To Creatives. github.com/evhart W

×