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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Proceedings ArticleDOI
Fully Automated Fact Checking Using External Sources
TL;DR: The authors proposed a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim, using a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings.
Journal ArticleDOI
Detection and visualization of misleading content on Twitter
Christina Boididou,Symeon Papadopoulos,Markos Zampoglou,Lazaros Apostolidis,Olga Papadopoulou,Yiannis Kompatsiaris +5 more
TL;DR: A system that supports the automatic classification of multimedia Twitter posts into credible or misleading status, and trains a two-step classification model based on a novel semisupervised learning scheme.
Journal ArticleDOI
Leveraging Financial Social Media Data for Corporate Fraud Detection
TL;DR: An analytic framework that taps into unstructured data from financial social media platforms to assess the risk of corporate fraud is proposed, guided by systemic functional linguistics (SFL) theory.
Dissertation
Automatic detection and verification of rumors on Twitter
TL;DR: The ability to track rumors and predict their outcomes may have practical applications for news consumers, financial markets, journalists, and emergency services, and more generally to help minimize the impact of false information on Twitter.
Proceedings ArticleDOI
Leveraging Joint Interactions for Credibility Analysis in News Communities
TL;DR: This paper presents a model to systematically analyze the different interactions in a news community between users, news, and sources and develops a probabilistic graphical model that leverages this joint interaction to identify 1) highly credible news articles, 2) trustworthy news sources, and 3) expert users who perform the role of "citizen journalists" in the community.
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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