@inproceedings{li-etal-2019-rumor,
title = "Rumor Detection on Social Media: Datasets, Methods and Opportunities",
author = "Li, Quanzhi and
Zhang, Qiong and
Si, Luo and
Liu, Yingchi",
booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5008",
doi = "10.18653/v1/D19-5008",
pages = "66--75",
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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<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.</abstract>
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%0 Conference Proceedings
%T Rumor Detection on Social Media: Datasets, Methods and Opportunities
%A Li, Quanzhi
%A Zhang, Qiong
%A Si, Luo
%A Liu, Yingchi
%S Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2019
%8 nov
%I Association for Computational Linguistics
%C Hong Kong, China
%F li-etal-2019-rumor
%X 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.
%R 10.18653/v1/D19-5008
%U https://aclanthology.org/D19-5008
%U https://doi.org/10.18653/v1/D19-5008
%P 66-75
Markdown (Informal)
[Rumor Detection on Social Media: Datasets, Methods and Opportunities](https://aclanthology.org/D19-5008) (Li et al., EMNLP 2019)
ACL