Multimodal cyberbullying detection in Hinglish memes using a classroom framework based on large language models
Year: 2026
Type: article
Abstract: The widespread use of social media has led to the emergence of harmful memes, which can perpetuate hate speech and cyberbullying. Detecting such harmful content is crucial for maintaining a safe online environment. Cyberbullying detection in code-mixed languages, particularly Hinglish (Hindi-English), poses significant challenges due to the complexity of multimodal content. This paper presents a novel approach for multimodal cyberbullying detection in Hinglish memes, leveraging the power of Large Language Models (LLMs) and a teacher-student learning based classroom framework. The proposed method involves generating explanations from both harmless and harmful perspectives using LLMs, followed by a final decision made by a smaller language model, mT5. This gives our model the ability to use multimodal explanations derived from both harmful and harmless arguments to execute dialectical reasoning over complex and implicit harm-indicative patterns. The model is trained on the MultiBully dataset, a benchmark dataset of Hinglish memes annotated with bully and non-bully labels. Experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy of 73.31% and an F1 score of 73.23%, outperforming several baselines. The paper concludes by discussing the implications of the findings and potential future directions for research in this area.
Source: Discover Computing
Authors: Pratik Joshi, Shikha Mundra, Ankit Mundra
Institution: Manipal University Jaipur
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,520
OpenAlex ID: W7127046487