MetaHate: A Meta-Model for Hate Speech Detection
Daniel G. Kyrollos, James Robert Green · 2021 IEEE International Conference on Big Data (Big Data) · 2021
We present MetaHate, a NLP meta-model for detecting hatefulness in tweets by combining predictors for hate, emotion, sentiment, and offensiveness. We evaluate this model with the TweetEval benchmark for hate speech detection. MetaHate improves the baseline TweetEval RoBERTa based model on the TweetEval benchmark. Optimizing the decision threshold for the macro-averaged F1-score, MetaHate achieves a F1-score of 0.70, while the TweetEval RoBERTa-Twitter Retrained Hate model achieves a F1-score of 0.63. This improvement on one of the most difficult tasks on the TweetEval benchmark was achieved with no additional training data and negligible computational time and cost. MetaHate demonstrates the utility of leveraging predictions from language models trained for various tasks to improve performance on a single task.