Beyond Benchmarks: Exposing the Hidden Crisis in Bangla Hate Speech Detection
The spread of hate speech (HS) across different social media platforms (SMPs) poses a major concern for online safety and ethical moderation. Automatic detection of HS remains a challenging task, especially in under-resourced languages like Bangla, due to cultural context, implicit expressions, and informal linguistic patterns. This study aimed to expose the crisis of Bangla HS detection systems by diagnosing how and why benchmark-trained models fail to identify implicit, context-dependent HS. Six architectures (FastText + CNN, FastText + LSTM, FastText + BiLSTM, BanglaBERT, BanglaBERT + CNN,
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- PossiblePossibly related (embedding) · 49%aadya940/numpyai →
- PossiblePossibly related (embedding) · 46%PacificAI/langtest →
- PossiblePossibly related (embedding) · 46%aibridge-afrilabs-group/aibridge-afrilabs-project →
- LinkedLinked via arxiv author · 85%Faria Afrin Tisha →
“Beyond Benchmarks: Exposing the Hidden Crisis in Bangla Hate Speech Detection”
- LinkedLinked via arxiv author · 85%Fariya Tabassum →
“Beyond Benchmarks: Exposing the Hidden Crisis in Bangla Hate Speech Detection”
- LinkedLinked via arxiv author · 85%Hafsa Binte Kibria →
“Beyond Benchmarks: Exposing the Hidden Crisis in Bangla Hate Speech Detection”
- LinkedLinked via arxiv author · 85%Md. Nahiduzzaman →
“Beyond Benchmarks: Exposing the Hidden Crisis in Bangla Hate Speech Detection”
- LinkedLinked via arxiv author · 85%Mominul Ahsan →
“Beyond Benchmarks: Exposing the Hidden Crisis in Bangla Hate Speech Detection”