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
Ising Model of User Behavior Decision in Network Rumor Propagation
Chengcheng Li,Fengming Liu,Pu Li +2 more
TL;DR: Based on the Ising model, this paper constructs a social network rumor propagation dynamics model and reveals the rumor transmission rules and shows that, in the rumor propagation system, von Neumann entropy can quantify well the phase transition of the system and is consistent with the phase Transition information obtained by measuring the spontaneous magnetization and magnetic susceptibility of theSystem.
Journal ArticleDOI
A corpus of debunked and verified user-generated videos
TL;DR: An annotated dataset of 380 user-generated videos, 200 debunked and 180 verified, along with 5,195 near-duplicate reposted versions of them, and a set of automatic verification experiments aimed to serve as a baseline for future comparisons are presented.
Proceedings ArticleDOI
Detecting Misinformation in Social Networks Using Provenance Data
TL;DR: It is argued that the quality of information or objects created in social networks can be analyzed by using their provenance data and an algorithm that assesses the credibility of information on social networks to detect the propagation of fake or malicious information is proposed.
Posted Content
Detection and Analysis of 2016 US Presidential Election Related Rumors on Twitter
TL;DR: A thorough analysis of rumor tweets from the followers of two presidential candidates: Hillary Clinton and Donald Trump to overcome the difficulty of labeling a large amount of tweets as training data, and detect rumor tweets by matching them with verified rumor articles.
Proceedings ArticleDOI
The Impact of Posting URLs in Disaster-Related Tweets on Rumor Spreading Behavior
TL;DR: The authors conducted an experiment to find out whether posting URLs in disaster-related tweets increased rumor-spreading behavior even though the URLs lacked the hyperlink function and identified some psychological factors that could explain this effect.
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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