Behind the Laughter: Uncovering Gender Bias in Code-Mixed Bangla Memes

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Social Sciences & Behavioral Studies · Depth: Expert, quick

Summary

A new multimodal framework has been developed to detect gender bias in code-mixed Bangla memes, addressing a gap in computational analysis for this content. The framework jointly analyzes textual and visual content, utilizing a custom dataset of 6,846 Bangla and Banglish code-mixed memes, categorized as male-biased, female-biased, or neutral. For textual representation, BanglishBERT is employed, while visual features are extracted using ConvNeXt. These two modalities are then fused for final classification. The top-performing model, combining ConvNeXt and BanglishBERT, achieved an accuracy of 0.67 and an F1-score of 0.63. This performance surpasses several multimodal baselines, demonstrating the efficacy of multimodal learning for understanding culturally nuanced and code-mixed meme content, particularly in low-resource languages.

Key takeaway

For NLP Engineers developing bias detection systems for culturally nuanced, code-mixed social media content, this research highlights the necessity of multimodal approaches. You should integrate both textual and visual features, as demonstrated by the ConvNeXt + BanglishBERT model's 0.67 accuracy on Bangla memes. Consider building custom datasets and leveraging specialized language models like BanglishBERT to effectively address the complexities of low-resource languages and their unique linguistic characteristics.

Key insights

A multimodal framework effectively detects gender bias in code-mixed Bangla memes using textual and visual analysis.

Principles

• Multimodal learning is effective for nuanced content.
• Code-mixed language analysis benefits from specialized models.
• Low-resource languages require custom dataset construction.

Method

Construct a code-mixed meme dataset, annotate for bias. Extract textual features with BanglishBERT and visual features with ConvNeXt. Fuse modalities for classification.

In practice

• Apply multimodal analysis to other low-resource languages.
• Develop bias detection for diverse social media content.
• Use BanglishBERT for code-mixed Bangla NLP tasks.

Topics

• Gender Bias Detection
• Code-Mixed Language
• Bangla Memes
• Multimodal AI
• BanglishBERT
• ConvNeXt

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.