Susan Gauch
2024
Using Sarcasm to Improve Cyberbullying Detection
Xiaoyu Guo
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Susan Gauch
Proceedings of the Fourth Workshop on Threat, Aggression & Cyberbullying @ LREC-COLING-2024
Cyberbullying has become more prevalent over time, especially towards minority groups, and online human moderators cannot detect cyberbullying content efficiently. Prior work has addressed this problem by detecting cyberbullying with deep learning approaches. In this project, we compare several BERT-based benchmark methods for cyberbullying detection and do a failure analysis to see where the model fails to correctly identify cyberbullying. We find that many falsely classified texts are sarcastic, so we propose a method to mitigate the false classifications by incorporating neural network-based sarcasm detection. We define a simple multilayer perceptron (MLP) that incorpo- rates sarcasm detection in the final cyberbully classifications and demonstrate improvement over benchmark methods.
2023
Improving Cross-Domain Hate Speech Generalizability with Emotion Knowledge
Shi Yin Hong
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Susan Gauch
Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation
1993
Experiments in Syntactic and Semantic Classification and Disambiguation using Bootstrapping
Robert Futrelle
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Susan Gauch
Acquisition of Lexical Knowledge from Text
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