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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Fake Account Identification in Social Networks
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Target oriented tweets monitoring system during natural disasters
Si Si Mar Win,Than Nwe Aung +1 more
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Book ChapterDOI
Frauds in Online Social Networks: A Review
TL;DR: This paper identifies manipulation of identities and diffusion of misinformation as two important aspects in the modus operandi of most types of OSN frauds.
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‘Breaking’ news: uncovering sense-breaking patterns in social media crisis communication during the 2017 Manchester bombing
Milad Mirbabaie,Julian Marx +1 more
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DEAN: Learning Dual Emotion for Fake News Detection on Social Media
TL;DR: A new Dual Emotion-based fAke News detection framework (DEAN), which can i) learn content- and comment- emotion representations for publishers and users respectively; and ii) exploit the dual emotion representations simultaneously for fake news detection.
References
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Proceedings ArticleDOI
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