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
Understanding User Profiles on Social Media for Fake News Detection
Kai Shu,Suhang Wang,Huan Liu +2 more
TL;DR: A comparative analysis over explicit and implicit profile features between “experienced” users who are able to recognize fake news items as false and “naïve” Users who are more likely to believe fake news reveals their potential to differentiate fake news.
Journal ArticleDOI
Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities
TL;DR: A holistic view of how the information is being weaponized to fulfil the malicious motives and forcefully making a biased user perception about a person, event or firm is put forward.
Proceedings ArticleDOI
A convolutional approach for misinformation identification
TL;DR: Convolutional Approach for Misinformation Identification (CAMI) based on Convolutional Neural Network (CNN) can flexibly extract key features scattered among an input sequence and shape high-level interactions among significant features, which help effectively identify misinformation and achieve practical early detection.
Proceedings ArticleDOI
Toward Automated Fact-Checking: Detecting Check-worthy Factual Claims by ClaimBuster
TL;DR: This paper introduces how ClaimBuster, a fact-checking platform, uses natural language processing and supervised learning to detect important factual claims in political discourses and explains the architecture and the components of the system and the evaluation of the model.
Proceedings ArticleDOI
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media
Yi-Ju Lu,Cheng-Te Li +1 more
TL;DR: This paper solves the fake news detection problem under a more realistic scenario on social media by developing a novel neural network-based model, Graph-aware Co-Attention Networks (GCAN), which can significantly outperform state-of-the-art methods by 16% in accuracy on average.
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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