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.
Author supplied keywords
Cite
CITATION STYLE
Kim, Y., & Allan, J. (2019). Unsupervised explainable controversy detection from online news. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11437 LNCS, pp. 836–843). Springer Verlag. https://doi.org/10.1007/978-3-030-15712-8_60
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
