Rumor Detection on Social Media: Datasets, Methods and Opportunities
November 17, 2019 Β· Declared Dead Β· π Conference on Empirical Methods in Natural Language Processing
"No code URL or promise found in abstract"
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Authors
Quanzhi Li, Qiong Zhang, Luo Si, Yingchi Liu
arXiv ID
1911.07199
Category
cs.IR: Information Retrieval
Cross-listed
cs.CL,
cs.CY,
cs.SI
Citations
58
Venue
Conference on Empirical Methods in Natural Language Processing
Last Checked
3 months ago
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.
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