Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media-Reference-Cited by-同舟云学术

Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media

Published:2022-04-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Web Conference 2022
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Author:

Min Erxue¹,Rong Yu²,Bian Yatao²,Xu Tingyang²,Zhao Peilin²,Huang Junzhou³,Ananiadou Sophia¹

Affiliation:

1. Department of Computer Science, National Centre for Text Mining, Department of Computer Science, The University of Manchester, United Kingdom

2. Tencent AI lab, China

3. Department of Computer Science and Engineering, University of Texas at Arlington, USA

Funder

The University of Manchester - China Scholarship Council joint scholarship award

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3485447.3512163

Reference52 articles.

1. Measuring User Credibility in Social Media

2. Koirala Abhishek. 2021. COVID-19 Fake News Dataset. https://data.mendeley.com/datasets/zwfdmp5syg/1 Koirala Abhishek. 2021. COVID-19 Fake News Dataset. https://data.mendeley.com/datasets/zwfdmp5syg/1

3. An information-theoretic perspective of tf–idf measures

4. Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks

5. Shaked Brody Uri Alon and Eran Yahav. 2021. How Attentive are Graph Attention Networks?arXiv preprint arXiv:2105.14491(2021). Shaked Brody Uri Alon and Eran Yahav. 2021. How Attentive are Graph Attention Networks?arXiv preprint arXiv:2105.14491(2021).

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