Deep learning for hate speech detection in tweets

26th International World Wide Web Conference 2017, WWW 2017 Companion

Volumn , Issue , 2017, Pages 759-760

  Badjatiya, Pinkesh ^a   Gupta, Shashank ^a   Gupta, Manish ^a,b   Varma, Vasudeva ^a  

^a INTERNATIONAL INSTITUTE OF INFORMATION TECHNOLOGY   (India)

^b MICROSOFT   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS; SEMANTICS; SENTIMENT ANALYSIS; SPEECH RECOGNITION; WORLD WIDE WEB;

BENCHMARK DATASETS; CONTENT RECOMMENDATIONS; EVENT EXTRACTION; LEARNING ARCHITECTURES; LEARNING METHODS; NATURAL LANGUAGES; SPEECH DETECTION; STATE OF THE ART;

DEEP LEARNING;

EID: 85044462256     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/3041021.3054223     Document Type: Conference Paper

References (6)

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2. A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov. Bag of Tricks for Efficient Text Classification. arXiv preprint arXiv:1607.01759, 2016.
3. Y. Kim. Convolutional Neural Networks for Sentence Classification. In EMNLP, pages 1746-1751, 2014.
4. C. Nobata, J. Tetreault, A. Thomas, Y. Mehdad, and Y. Chang. Abusive Language Detection in Online User Content. In WWW, pages 145-153, 2016.
5. J. Pennington, R. Socher, and C. D. Manning. GloVe: Global Vectors for Word Representation. In EMNLP, volume 14, pages 1532-43, 2014.
6. Z. Waseem and D. Hovy. Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter. In NAACL-HLT, pages 88-93, 2016.