Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection

ACL 2020 Lei ZhongJuan CaoQiang ShengJunbo GuoZiang Wang

Identifying controversial posts on social media is a fundamental task for mining public sentiment, assessing the influence of events, and alleviating the polarized views. However, existing methods fail to 1) effectively incorporate the semantic information from content-related posts; 2) preserve the structural information for reply relationship modeling; 3) properly handle posts from topics dissimilar to those in the training set... (read more)

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