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
Social Media in Crisis: When Professional Responders Meet Digital Volunteers
TL;DR: In this paper, the socio-technical impact that social media has had on coordination between professional emergency responders and digital volunteers is examined. And the problem space and explore ways to improve coordination and collaboration between these two groups.
Proceedings ArticleDOI
Multi-modal Knowledge-aware Event Memory Network for Social Media Rumor Detection
TL;DR: A novel Multimodal Knowledge-aware Event Memory Network (MKEMN) which utilizes the multi-modal representation of the post on social media and retrieves external knowledge from real-world knowledge graph to complement the semantic representation of short texts of posts and takes conceptual knowledge as additional evidence to improve rumor detection.
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A rule dynamics approach to event detection in Twitter with its application to sports and politics
Mariam Adedoyin-Olowe,Mohamed Medhat Gaber,Carlos Martin Dancausa,Frederic Stahl,João Bártolo Gomes +4 more
TL;DR: The approach is able to accurately detect and track newsworthy content and the adaptation of the time-window exhibits better performance especially on the sports dataset, which can be attributed to the usually shorter duration of football events.
Book ChapterDOI
Information-oriented trustworthiness evaluation in vehicular ad-hoc networks
TL;DR: A novel trust model to directly evaluate the trustworthiness of the content of a message received from other vehicles is proposed, built based on various factors such as content similarity, content conflict and route similarity.
Proceedings ArticleDOI
Explainable Machine Learning for Fake News Detection
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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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