Ben-Misandry: A Bengali Dataset and Late-Fusion Transformer Framework for Detecting Anti-Male Gender Hate

Key points

• Gender based hate speech research in Bengali has focused almost exclusively on misogyny, leaving anti male hate, or misandry, entirely unstudied for this language and creating a systematic blind spot in moderation systems trained to catch attacks on women but not on men.1
• We benchmark it with a classical pipeline (seven classifiers over four text representations) and a transformer pipeline (BanglaBERT, XLM-RoBERTa, and two fusion strategies).1
• We relate this to classifier combination theory, under which averaging encoders with decorrelated errors is expected to reduce variance, and corroborate the decision boundary with Shapley explanations that recover the same class- discriminative vocabulary found in our exploratory analysis, while noting this cannot rule out the model partly keying on synthetic-generation artifacts.1
• Our results indicate misandry detection in Bengali is feasible and measurable on this synthetic corpus, offered as an initial public resource pending validation on real-world data.1

Sources (1)

• [1]Ben-Misandry: A Bengali Dataset and Late-Fusion Transformer Framework for Detecting Anti-Male Gender Hate
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 05:42 AM

  We introduce Ben-Misandry, the first Bengali misandry detection dataset: 5,000 comments across six taxonomy classes, including a novel category for mocking male emotional vulnerability with no direct analogue in existing misogyny taxonomies.

  Gender based hate speech research in Bengali has focused almost exclusively on misogyny, leaving anti male hate, or misandry, entirely unstudied for this language and creating a systematic blind spot in moderation systems trained to catch attacks on women but not on men.

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