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Unsupervised Explainable Controversy Detection from Online News

  • Youngwoo KimEmail author
  • James Allan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11437)

Abstract

Alerting users that a web page is controversial has been proposed as one method to support critical thinking about text and discourse. We propose an approach to discover controversial topics in a generic document using unsupervised training. Our approach comprises iterative training of a controversy classifier using a disagreement signal within comments and explaining the controversy of the document by generating a topic phrase describing it. Experiments show the effectiveness of our proposed training method using an EM algorithm. When controversial topic extraction is restricted to quality phrases and incorporates TextRank signals, it outperforms several baseline approaches.

Keywords

Controversy Topic extraction Controversy detection 

Notes

Acknowledgment

This work was supported in part by the Center for Intelligent Information Retrieval and in part by NSF grant #IIS-1813662. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor. We thank Kaspar Beelen for sharing the labeled data.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Center for Intelligent Information Retrieval, College of Information and Computer SciencesUniversity of Massachusetts AmherstAmherstUSA

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