Rumor Detection with Self-supervised Learning on Texts and Social Graph
- URL: http://arxiv.org/abs/2204.08838v1
- Date: Tue, 19 Apr 2022 12:10:03 GMT
- Title: Rumor Detection with Self-supervised Learning on Texts and Social Graph
- Authors: Yuan Gao, Xiang Wang, Xiangnan He, Huamin Feng, Yongdong Zhang
- Abstract summary: We propose contrastive self-supervised learning on heterogeneous information sources, so as to reveal their relations and characterize rumors better.
We term this framework as Self-supervised Rumor Detection (SRD)
Extensive experiments on three real-world datasets validate the effectiveness of SRD for automatic rumor detection on social media.
- Score: 101.94546286960642
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Rumor detection has become an emerging and active research field in recent
years. At the core is to model the rumor characteristics inherent in rich
information, such as propagation patterns in social network and semantic
patterns in post content, and differentiate them from the truth. However,
existing works on rumor detection fall short in modeling heterogeneous
information, either using one single information source only (e.g. social
network, or post content) or ignoring the relations among multiple sources
(e.g. fusing social and content features via simple concatenation). Therefore,
they possibly have drawbacks in comprehensively understanding the rumors, and
detecting them accurately. In this work, we explore contrastive self-supervised
learning on heterogeneous information sources, so as to reveal their relations
and characterize rumors better. Technically, we supplement the main supervised
task of detection with an auxiliary self-supervised task, which enriches post
representations via post self-discrimination. Specifically, given two
heterogeneous views of a post (i.e. representations encoding social patterns
and semantic patterns), the discrimination is done by maximizing the mutual
information between different views of the same post compared to that of other
posts. We devise cluster-wise and instance-wise approaches to generate the
views and conduct the discrimination, considering different relations of
information sources. We term this framework as Self-supervised Rumor Detection
(SRD). Extensive experiments on three real-world datasets validate the
effectiveness of SRD for automatic rumor detection on social media.
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