Rumor Detection on Social Media: Datasets, Methods and Opportunities

Quanzhi Li, Qiong Zhang, Luo Si, Yingchi Liu

Abstract
Social media platforms have been used for information and news gathering, and they are very valuable in many applications. However, they also lead to the spreading of rumors and fake news. Many efforts have been taken to detect and debunk rumors on social media by analyzing their content and social context using machine learning techniques. This paper gives an overview of the recent studies in the rumor detection field. It provides a comprehensive list of datasets used for rumor detection, and reviews the important studies based on what types of information they exploit and the approaches they take. And more importantly, we also present several new directions for future research.
Anthology ID:
D19-5008
Volume:
Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Anna Feldman, Giovanni Da San Martino, Alberto Barrón-Cedeño, Chris Brew, Chris Leberknight, Preslav Nakov
Venue:
NLP4IF
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
66–75
Language:
URL:
https://aclanthology.org/D19-5008
DOI:
10.18653/v1/D19-5008
Bibkey:
Cite (ACL):
Quanzhi Li, Qiong Zhang, Luo Si, and Yingchi Liu. 2019. Rumor Detection on Social Media: Datasets, Methods and Opportunities. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 66–75, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Rumor Detection on Social Media: Datasets, Methods and Opportunities (Li et al., NLP4IF 2019)
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PDF:
https://preview.aclanthology.org/ingest-bitext-workshop/D19-5008.pdf
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