BanglaMemeEvidence: A Multimodal Benchmark Dataset for Explanatory Evidence Detection in Bengali Memes

2026-07-04

AI Summary

This study addresses the challenge of contextual understanding in Bengali internet memes, which hinders effective detection of harmful content, cyberbullying identification, and sentiment analysis. To bridge this gap, the authors propose a novel task, MemeEvidenceDetect, and introduce BanglaMemeEvidence—the first Bengali meme dataset comprising 2,917 annotated samples—augmented with OCR-extracted text, contextual information, human-annotated evidence sentences, and relevance scores. They further develop BengaliMemeEvidenceNet, a multimodal model that integrates visual and textual features through joint representation learning. The proposed approach achieves an F1 score of 0.74 on the evidence detection task, offering the first interpretable, deep contextual understanding of memes in a low-resource language and thereby filling a critical research void in multilingual multimodal content analysis.

Abstract

Memes have become influential communication tools on social media, combining viral visuals with concise messaging to convey impactful ideas. While substantial research has examined the affective dimensions of memes, key challenges such as detecting harmful content, identifying cyberbullying, and performing accurate sentiment analysis remain critical, largely due to the need for deeper contextual understanding. In this paper, we introduce MemeEvidenceDetect, a hybrid task aimed at analyzing a meme and its contextual information to identify specific sentences that explain or elucidate its meaning and humor. To support this task, we present BanglaMemeEvidence, a curated dataset of 2,917 Bengali memes, emphasizing its significance as a resource for the Bangla language. Each meme is annotated with natural language explanations, including Meme OCR, Meme Context, and Evidence Sentences, alongside relevance scores that reflect the relationship between a meme and its corresponding annotations. To address the gap in dynamically inferring a meme's context, we propose BengaliMemeEvidenceNet, a hybrid multimodal framework that integrates textual and visual features for comprehensive meme representation. Our experiments demonstrate the effectiveness of BengaliMemeEvidenceNet, achieving an F1 score of 0.74. To the best of our knowledge, this is the first study to focus on evidence detection in Bengali memes, marking a notable step forward in the analysis of memes in low-resource languages.

Problem

• meme
• evidence detection
• multimodal
• Bengali
• contextual understanding

Innovation

• multimodal learning
• evidence detection
• Bengali memes
• low-resource languages
• contextual understanding