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
Linguistic characteristics and the dissemination of misinformation in social media: The moderating effect of information richness
Cheng Zhou,Kai Li,Yanhong Lu +2 more
TL;DR: A misinformation dissemination model that includes the direct effects of four novel linguistic characteristics on dissemination and the moderating effect of information richness is proposed, indicating that the four linguistic characteristics proposed by this study are also suitable for the dissemination of misinformation in English.
Journal ArticleDOI
A temporal ensembling based semi-supervised ConvNet for the detection of fake news articles
TL;DR: An innovative Convolutional Neural Network semi-supervised framework built on the self-ensembling concept to take leverage of the linguistic and stylometric information of annotated news articles, at the same time explore the hidden patterns in unlabelled data as well.
Journal ArticleDOI
Sensing and detecting traffic events using geosocial media data: A review
TL;DR: A systematic review of a wide variety of techniques applied in detecting traffic events from geosocial media data, arranged based on their adoption in each stage of an event detection framework developed from the literature review is presented.
Proceedings ArticleDOI
Perspective Matters: Sharing of Crisis Information in Social Media
Rongjuan Chen,Yasuaki Sakamoto +1 more
TL;DR: The authors examined information sharing behavior in social media when one was taking the perspective of self versus other, and found that imagining self in a disaster center, Fukushima, Japan, increased the likelihood of sharing crisis information relative to imagining another person, John, in the same place.
Journal ArticleDOI
Automatic Fact-Checking Using Context and Discourse Information
Pepa Atanasova,Preslav Nakov,Lluís Màrquez,Alberto Barrón-Cedeño,Georgi Karadzhov,Tsvetomila Mihaylova,Mitra Mohtarami,James Glass +7 more
TL;DR: This work addresses two related tasks: detecting check-worthy claims and fact-checking claims, and develops supervised systems based on neural networks, kernel-based support vector machines, and combinations thereof, which make use of rich input representations in terms of discourse cues and contextual features.
References
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Proceedings ArticleDOI
What is Twitter, a social network or a news media?
TL;DR: In this paper, the authors have crawled the entire Twittersphere and found a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks.
Proceedings ArticleDOI
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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