Evaluating Event Credibility on Twitter

By Manish Gupta, Peixiang Zhao, Jiawei Han. SDM, 2012

298 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/9ed71a63206ea5248742fcf505477fbc04c528cb

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In Computer Networks and Communications

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Citations in the corpus

Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, background): “ credibility and (b) normal users, with relatively high credibility. User credibility has been used to study fake news, particularly, from a propagation-based perspective (Section4), either directly [Gupta et al. 2012], or indirectly through their posts [Dungs et al. 2018;Jin et al. 2014,2016;Ma et ”
News Verification by Exploiting Conflicting Social Viewpoints in Microblogs (2016, methodology): “Gupta, Zhao, and Han (2012) optimize the initial tweet classiﬁcation re-sults with a credibility propagation approach.”
Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs (2017, methodology): “Some works [10, 13] use bag-of-words text features to reveal inter-tweet relations.”
FakeBERT: Fake news detection in social media with a BERT-based deep learning approach (2021, background): “Social context-based approaches [14, 17, 38, 51, 53] deals with the latent information between the user and news article.”
A Survey of Fake News (2020, methodology): “[Gupta et al. 2012] evaluate news credibility by designing an algorithm similar to PageRank [Page et al.”
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection (2018, background): “The basic features of attached images in the posts are explored in the work [12, 15, 24, 31].”
Novel Visual and Statistical Image Features for Microblogs News Verification (2017, methodology): “For example, in most recent studies, the two data sets in [9] have 67 and 83”