Proceedings ArticleDOI
Information credibility on twitter
Carlos Castillo,Marcelo Mendoza,Barbara Poblete +2 more
- pp 675-684
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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
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Journal ArticleDOI
The spread of low-credibility content by social bots
Chengcheng Shao,Giovanni Luca Ciampaglia,Onur Varol,Kai-Cheng Yang,Alessandro Flammini,Filippo Menczer +5 more
TL;DR: In this paper, the authors analyze 14 million messages spreading 400 thousand articles on Twitter during ten months in 2016 and 2017 and find evidence that social bots played a disproportionate role in spreading articles from low-credibility sources.
Journal ArticleDOI
Computational Fact Checking from Knowledge Networks
Giovanni Luca Ciampaglia,Prashant Shiralkar,Luis M. Rocha,Luis M. Rocha,Johan Bollen,Filippo Menczer,Alessandro Flammini +6 more
TL;DR: It is shown that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs.
Journal ArticleDOI
Social Media in Disaster Risk Reduction and Crisis Management
TL;DR: The widespread adoption and use of social media by members of the public throughout the world heralds a new age in which it is imperative that emergency managers adapt their working practices to the challenge and potential of this development, but they must heed the ethical warnings and ensure that social media are not abused or misused when crises and emergencies occur.
Proceedings ArticleDOI
What to Expect When the Unexpected Happens: Social Media Communications Across Crises
TL;DR: This paper investigates several crises-including natural hazards and human-induced disasters-in a systematic manner and with a consistent methodology, leading to insights about the prevalence of different information types and sources across a variety of crisis situations.
Proceedings ArticleDOI
Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning
Jing Ma,Wei Gao,Kam-Fai Wong +2 more
TL;DR: A kernel-based method is proposed, which captures high-order patterns differentiating different types of rumors by evaluating the similarities between their propagation tree structures, and can detect rumors more quickly and accurately than state-of-the-art rumor detection models.
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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