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Detecting offensive tweets via topical feature discovery over a large scale twitter corpus
2012
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12
In this paper, we propose a novel semi-supervised approach for detecting profanity-related offensive content in Twitter. Our approach exploits linguistic regularities in profane language via statistical topic modeling on a huge Twitter corpus, and detects offensive tweets using these automatically generated features. Our approach performs competitively with a variety of machine learning (ML) algorithms. For instance, our approach achieves a true positive rate (TP) of 75.1% over 4029 testing
doi:10.1145/2396761.2398556
dblp:conf/cikm/XiangFWHR12
fatcat:f347mar4tjaflmwctjbhpxj2vi