Hate Speech Detection Leveraging Emoji Descriptions and BERT-Based Contextual Data Augmentation
DOI:
https://doi.org/10.21512/comtech.v17i2.14618Keywords:
hate speech detection, emoji description, mBERT, data augmentation, Bi-LSTMAbstract
This study evaluates the efficacy of emoji-to-text transformation and contextual data augmentation in improving hate speech detection on social media. Using a unified dataset from OLID, TweetEval, and the Sushil benchmark, we compared Multinomial Naive Bayes (MNB), Bidirectional LSTM (Bi-LSTM), and Multilingual BERT (mBERT). To address class imbalance and semantic loss, we implemented a preprocessing pipeline that converts emojis into textual descriptions and utilized a BERT-based contextual augmenter. Results indicate that textualizing emojis consistently improves performance by providing critical semantic anchors. The mBERT model achieved the highest performance with a Macro F1-score of 0.87 and a hate speech recall of 0.94, outperforming the Bi-LSTM (0.835) and MNB (0.83) baselines. Notably, the Bi-LSTM showed the highest sensitivity to the "Emoji Description" method, with a 5% performance increase compared to the "No Emoji" condition. These findings demonstrate that bridging the gap between visual symbols and textual intent significantly reduces model bias and improves the detection of nuanced toxicity. This research provides an optimized framework for context-aware moderation in imbalanced digital environments.
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Copyright (c) 2026 Junita Amalia, Walker Valentinus Simanjuntak, Ruth Marelisa Hutagalung, Lamria Magdalena Tampubolon
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