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Abuse on the Internet represents a significant societal problem of our time.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
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Ellen Spertus. 1997 · 1997
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio. 2010 · 2010
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Detecting hate speech on the world wide web
William Warner and Julia Hirschberg. 2012 · 2012
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Abusive language detection in online user content
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Thomas N. Kipf and Max Welling. 2017 · 2017
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Deeper attention to abusive user content moderation
John Pavlopoulos, Prodromos Malakasiotis, and Ion Androutsopoulos. 2017a · 2017
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Improved abusive comment moderation with user embeddings
John Pavlopoulos, Prodromos Malakasiotis, Juli Bakagianni, and Ion Androutsopoulos. 2017b · 2017
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Leveraging intra-user and inter-user representation learning for automated hate speech detection
Jing Qian, Mai ElSherief, Elizabeth Belding, and William Yang Wang. 2018 · 2018
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Mapping racist tweets in response to president obama’s re-election
Matthew Zook. 2012 · 2018
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Hateful symbols or hateful people? predictive features for hate speech detection on twitter
Zeerak Waseem and Dirk Hovy. 2016 · 2016
Cited alongside, same era.
Author profiling for abuse detection
Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, and Ekaterina Shutova. 2018a
Cited in the paper.
Neural character-based composition models for abuse detection
Pushkar Mishra, Helen Yannakoudakis, and Ekaterina Shutova. 2018b
Cited in the paper.