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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The Impact of Posting URLs in Disaster-Related Tweets on Rumor Spreading Behavior
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Journal ArticleDOI
Credibility Detection in Twitter Using Word N-gram Analysis and Supervised Machine Learning Techniques
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Graph-based Modeling of Online Communities for Fake News Detection
Shantanu Chandra,Pushkar Mishra,Helen Yannakoudakis,Madhav Nimishakavi,Marzieh Saeidi,Ekaterina Shutova +5 more
TL;DR: This work proposes a novel social context-aware fake news detection framework, SAFER, based on graph neural networks (GNNs), and introduces novel methods based on relational and hyperbolic GNNs, which have not been previously used for user or community modeling within NLP.
References
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Proceedings ArticleDOI
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