Papers › Hate speech detection using static BERT embeddings
Hate speech detection using static BERT embeddings
Gaurav Rajput, Narinder Singh Punn, Sanjay Kumar Sonbhadra, Sonali Agarwal
With increasing popularity of social media platforms hate speech is emerging as a major concern, where it expresses abusive speech that targets specific group characteristics, such as gender, religion or ethnicity to spread violence. Earlier people use to verbally deliver hate speeches but now with the expansion of technology, some people are deliberately using social media platforms to spread hate by posting, sharing, commenting, etc. Whether it is Christchurch mosque shootings or hate crimes against Asians in west, it has been observed that the convicts are very much influenced from hate text present online. Even though AI systems are in place to flag such text but one of the key challenges is to reduce the false positive rate (marking non hate as hate), so that these systems can detect hate speech without undermining the freedom of expression. In this paper, we use ETHOS hate speech detection dataset and analyze the performance of hate speech detection classifier by replacing or integrating the word embeddings (fastText (FT), GloVe (GV) or FT + GV) with static BERT embeddings (BE). With the extensive experimental trails it is observed that the neural network performed better with static BE compared to using FT, GV or FT + GV as word embeddings. In comparison to fine-tuned BERT, one metric that significantly improved is specificity.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Hate Speech Detection | Ethos Binary | BiLSTM + static BE | Classification Accuracy | 0.8015 | #1 of 12 | Archive leaderboard | report |
| Hate Speech Detection | Ethos Binary | BiLSTM + static BE | F1-score | 0.7971 | #1 of 12 | Archive leaderboard | report |
| Hate Speech Detection | Ethos Binary | BiLSTM + static BE | Precision | 0.8037 | #1 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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