Detecting Cyberbullying in Thai Memes: A Multimodal Approach Using Deep Learning

Abstract

Today, people tend to communicate more through the Internet due to its speed and convenience. However, there are hidden drawbacks, such as the misuse of online social media and the occurrence of cyberbullying, which can lead to negative feelings or even mental health issues. Therefore, it is necessary to develop cyberbullying detection methods. While these methods have been studied in various languages, more research is still needed in Thai due to its status as a low-resource language. This research aims to develop a model for detecting cyberbullying in online social media, specifically focusing on memes. The primary objective is to develop a Thai meme dataset collected from Facebook using deep learning techniques for model development. The developed model utilizes multimodal concepts, incorporating both text and images. In addition to detecting cyberbullying, it also considers other aspects, such as harmfulness, offensiveness, sarcasm, sentiment, emotions, and topics. Our findings indicate that multimodal models outperform unimodal models, with the combination of WangchanBERTa and ViT-B/32 achieving the highest performance across most tasks. Compared to the best-performing unimodal models, the multimodal approach resulted in a 1.53% improvement in the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> -Score on the Bully task. This underscores the significant value of integrating textual and visual information for a more robust and nuanced understanding of cyberbullying content.