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Get Back! You Don’t Know Me Like That: The Social Mediation of Fact Checking Interventions in Twitter Conversations
Open accessAbstract
The prevalence of misinformation within social media and online communities can undermine public security and distract attention from important issues. Fact-checking interventions, in which users cite fact-checking websites such as Snopes.com and FactCheck.org, are a strategy users can employ to refute false claims made by their peers. While laboratory research suggests such interventions are not effective in persuading people to abandon false ideas, little work considers how such interventions are actually deployed in real-world conversations. Using approximately 1,600 interventions observed on Twitter between 2012 and 2013, we examine the contexts and consequences of fact-checking interventions.We focus in particular on the social relationship between the individual who issues the fact-check and the individual whose facts are challenged. Our results indicate that though fact-checking interventions are most commonly issued by strangers, they are more likely to draw user attention and responses when they come from friends. Finally, we discuss implications for designing more effective interventions against misinformation.
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Some studies used online sources like Snopes.com as expert ground truth for rumour veracity.
“For instance, Hannak et al. (2014) used Snopes.comas a database that provides ground truth annotations of veracity for rumours put together by experts.”
From Detection and Resolution of Rumours in Social Media: A Survey (2019) · cites this paper as Hannak et al. 2014
Data and Methods (page unavailable) says Hannak et al. selected tweets linking to Snopes.com and two other fact-checking websites. The paper uses those links to study fact-checking interactions; it does not say it used Snopes.com as a database of expert ground-truth annotations of rumour veracity. (LLM, conf=0.86)
Experimental — accuracy not yet measuredTop-down sampling can be used for long-standing rumours via keywords, retrospective event rumours, or rumour repositories like Snopes.com.
“This can apply to long-standing rumours, where keywords can be defined for sampling posts related to a rumour known to have been circulating for a long time (Qazvinian et al. 2011), for retrospective sampling of rumours known to have emerged during an event (Procter et al. 2013b), or using rumour repositories like Snopes.com to collect posts associated with those rumours (Hannak et al. 2014).”
From Detection and Resolution of Rumours in Social Media: A Survey (2019) · cites this paper as Hannak et al. 2014
Top-down sampling strategies dominated early social media rumour work, sampling posts for rumours identified in advance.
“Top-down sampling strategies prevailed in early work on social media rumours, i.e., sampling posts related to rumours identified in advance.”
From Detection and Resolution of Rumours in Social Media: A Survey (2019) · cites this paper as Hannak et al. 2014
The Data and Methods section describes selecting conversational tweets that linked to three specified fact-checking websites, not sampling posts about rumours identified in advance. The Background and Theory section discusses earlier misinformation research but does not say that top-down sampling prevailed in early social-media rumour work. Searches for predefined rumours and top-down sampling found no passage addressing that claim. (LLM, conf=0.75)
Experimental — accuracy not yet measured
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Metadata
- Authors
- Hannak, A., Margolin, D., Keegan, B., & Weber, I.
- Affiliations
- Cornell University (US)
- Northeastern University (US)
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2014-05-16
- Volume
- 8
- Issue
- 1
- Pages
- 187-196
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W2188594517
- Cited by
- 90
- References
- 28
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 4 · 2023: 10 · 2022: 13 · 2021: 12 · 2020: 8 · 2019: 8 · 2018: 11 · 2017: 6 · 2016: 3 · 2015: 4 · 2014: 2
- FWCI
- 16.10
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Communication › Social Media and Politics
- Keywords
- misinformation, Twitter, social mediation, online discourse, friendship, persuasion, intervention design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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