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Co-Spread of Misinformation and Fact-Checking Content during the Covid-19 Pandemic
The document discusses the rise of misinformation on social media during the COVID-19 pandemic and the role of fact-checking organizations in addressing this issue. It analyzes the spread patterns of misinformation and fact-checking content, revealing that while fact-checking has a positive impact in reducing misinformation, its effectiveness is hindered by the high volume of misinformation shared. The study concludes that creating more shareable fact-checking content may be key to combating misinformation effectively.
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Co-Spread of Misinformation and Fact-Checking Content during the Covid-19 Pandemic
- 1. Co-Spread of Misinformation andFact-CheckingContent during the Covid-19 Pandemic Grégoire Burel, Tracie Farrell, Martino Mensio, Prashant Khare, and Harith Alani SocIinfo 2020, 6-9 October 2020
- 2. • During theCovid-19 pandemic the amount of misinformation shared on social media has risen significantly with some tragic results. • Fact-checking organisations have been correcting misinforming content. However, the effectiveness of corrective information remains largely unknown. Misinformationand Fact-Checkingspread duringCovid-19 Amount of new fact-checks per day. (Source: Poynter) Peak: 143 per day. “Rumours about coronavirus – that it can be prevented by drinking alcohol; that it is killed by cold or heat – pose serious risk.” The United Nations 17 Apr 2020 Did fact-checking during the Covid-19 pandemic have a positive impact in reducing misinformation spread? 2
- 3. Co-Spread of MisinformationandFact- Checking • Research Questions: 1. Are misinformation and fact-checking information spread similarly? 2. How do misinformation and fact-checking spread patterns vary in relation to each other, and to the pandemic level? 3. Does fact-checking spread affect the diffusion of misinformation about Covid-19? Misinformation is false or inaccurate information. Examples of misinformation include false rumours or more deliberate disinformation such as malicious content such as hoaxes and computational propaganda. Fact-checking is the process of verifying information in non-fictional text in order to determine its veracity and correctness. 3
- 4. Data collection • Wecollect tweets mentioning misinforming and fact- checking URLs. • Fact-checks and misinforming URLs are obtained from the misinfo.me tool that collect websites belonging to the International Fact-Checking Network (IFCN). • ClaimReview is a tagging system that fact-checkers use to identify their articles (i.e., claims, ratings fact-checked content and fact-checking URL). twitter misinfo.me Stacked cumulative spread of misinforming and corrective information. 2,830 Misinforming URLs 734 Fact-checking URLs 21,394 Tweets Data collected until 4th May 2020 4
- 5. Methodology 1. Misinformation andfact-checks spread analysis over time: • Relative and pandemic-level analysis. • Non-parametric MANOVA/ANOVA (Analysis of Variance) → Identifies if there is a significant variance in means between the misinformation and fact-check spreads. • Identifies if there are significant differences in how misinformation and fact-checking information spreads in different time periods. 2. Relational analysis between misinformation and fact- checking spread: • Relative analysis. • Causation analysis (Granger causality) model) for estimating if the spread of a given information type can be used to predict the spread of another type of information. • Impulse response analysis and Forecast Error Variance Decomposition (FEVD) to evaluate the spread response of a given information type depending on change in misinformation or fact-check spread. Relative analysis: Data is aligned based on their initial sharing date and then divided in initial, early and late periods (using linear regressions and inflection points). Pandemic-level analysis: Divide pandemic in the same three different time periods using worldwide cases. AnalysisLevels 0 – 2 days 2 – 14 days 14+ days initial early late < 14th March 14/3 – 2/4 > 2nd April initial early late 5
- 6. • Results: • Pandemic-levelanalysis: • Misinformation and Fact-checks spread differently globally (MANOVA, p = 0.01) • At individual periods, significant differences are observed for the initial and late periods. • Misinformation is shared much more than fact-checks. Stacked cumulative spread of misinforming and corrective information. Multivariate Analysis Initial onset period until 14th March . • Relative analysis: • There are significant differences in how misinforming URLs and fact-checking URLs spread globally (MANOVA, P < 0.001). • Significant differences are only observed in the early and late periods. • The highest difference in terms of mean and standard deviation between the different URL types appears to be mostly during the initial phase. Late period from 2nd April Ramp-up period from 14th March until 2nd April . 6
- 7. Fact-checking delayed spread response Downward misinformation spread trend Selfinitial response (spread drop soon after initial increase) • Results: • Misinformation spread can be predicted from fact-checking spread (Granger causality, p = 0.02 vs. p = 0.93). • Fact-checking impulse generates a slow downward trend in misinformation spread. • Misinformation impulse generates a delayed fact-checking spread response. • FEVD analysis confirms that misinformation spread is influenced by fact-checking. Causalityand Impulse Analysis Misinformation spread is affected by Fact-checks. Fact-checks spread marginally affected by misinformation.. 7
- 8. Conclusions and FutureWork 88 • Limitations and future work: • Limited but accurate data sample (restricted by Fact-checking data) → Automatic identification of fact- checked claims. • Granular features necessary for better understanding of spread variance (e.g., demographics and topics) → Topic analysis and demographics extraction. • Conclusions: • Fact-checking spread has a positive impact in reducing misinformation. • However, the impact of fact-checking is seriously impeded by: 1) the amount of shared misinformation, and; 2) the short period of time in which fact-checks are likely to spread. • Creating fact-checking content that is more spreadable may be the key to reduce misinformation spread. - Improving Covid-19 fact- check database (7,100+ fact-checks) and data collection (226,000+ tweets). - Topic and demographics analysis. - Automatic tracking and reporting (fc-observatory). FC-Observatory prototype. CurrentWork
- 9. Thank you. Grégoire Burel. @evhart github.com/evhart g.burel@open.ac.uk Co-Spreadof Misinformation and Fact-Checking Content during the Covid-19 Pandemic Background illustrations: Myth busters created by Redgirl Lee for United Nations Global Call Out To Creatives.