Proceedings ArticleDOI
Information credibility on twitter
Carlos Castillo,Marcelo Mendoza,Barbara Poblete +2 more
- pp 675-684
TLDR
There are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70% to 80%.Abstract:
We analyze the information credibility of news propagated through Twitter, a popular microblogging service. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors, often unintentionally.On this paper we focus on automatic methods for assessing the credibility of a given set of tweets. Specifically, we analyze microblog postings related to "trending" topics, and classify them as credible or not credible, based on features extracted from them. We use features from users' posting and re-posting ("re-tweeting") behavior, from the text of the posts, and from citations to external sources.We evaluate our methods using a significant number of human assessments about the credibility of items on a recent sample of Twitter postings. Our results shows that there are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70% to 80%.read more
Citations
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Journal ArticleDOI
Supervised Learning for Fake News Detection
Julio Cesar dos Reis,Andre Correia,Fabricio Murai,Adriano Veloso,Fabrício Benevenuto,Erik Cambria +5 more
TL;DR: A new set of features is presented and the prediction performance of current approaches and features for automatic detection of fake news are measured, revealing interesting findings on the usefulness and importance of features for detecting false news.
Journal ArticleDOI
Novel Visual and Statistical Image Features for Microblogs News Verification
TL;DR: This paper explores the key role of image content in the task of automatic news verification on microblogs and proposes several visual and statistical features to characterize these patterns visually and statistically for detecting fake news.
Proceedings ArticleDOI
Epidemiological modeling of news and rumors on Twitter
TL;DR: This work uses epidemiological models to characterize information cascades in twitter resulting from both news and rumors, using the SEIZ enhanced epidemic model that explicitly recognizes skeptics to characterize eight events across the world and spanning a range of event types.
Proceedings ArticleDOI
Real-time Rumor Debunking on Twitter
TL;DR: This paper shows using real streaming data that it is possible, using their approach, to debunk rumors accurately and efficiently, often much faster than manual verification by professionals.
Journal ArticleDOI
Predicting information credibility in time-sensitive social media
TL;DR: The purpose of the research is to establish if an automatic discovery process of relevant and credible news events can be achieved and to focus on the analysis of information credibility on Twitter.
References
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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