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Detecting Cyberbullying in Thai Memes: A Multimodal Approach Using Deep Learning
(2025-01-01)
Phosit, Salisa
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Kongsamlit, Sawarod
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Pasupa, Kitsuchart
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 F<inf>1</inf> -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.
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HANCaps: A Two-Channel Deep Learning Framework for Fake News Detection in Thai
(2024-01-01)
Maity, Krishanu
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Bhattacharya, Shaubhik
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Phosit, Salisa
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Kongsamlit, Sawarod
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Saha, Sriparna
The rapid advancement of internet technology, widespread smartphone usage, and the rise of social media platforms have drastically transformed the global communication landscape. These developments have resulted in both positive and negative consequences. On the one hand, they have facilitated the dissemination of information, connecting individuals across vast distances and fostering diverse perspectives. On the other hand, the ease of access to online platforms has led to the proliferation of misinformation, often in the form of fake news. Detecting and combatting fake news has become crucial to mitigate its adverse effects on society. This paper presents an investigation into fake news detection in the Thai language. It addresses current limitations in this domain by proposing a novel two-channel deep learning model named HANCaps, which integrates BERT and FastText embeddings with a hierarchical attention network and capsule network. The HANCaps model utilizes the BERT language model as one channel input, while the other channel incorporates pre-trained FastText embeddings. The proposed model undergoes evaluation using a benchmark Thai fake news dataset, and extensive experimentation demonstrates that HANCaps outperforms state-of-the-art methods by up to 3.28% in terms of F1 score, showcasing its superior performance.
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Ex-ThaiHate: A Generative Multi-task Framework for Sentiment and Emotion Aware Hate Speech Detection with Explanation in Thai
(2023-01-01)
Maity, Krishanu
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Bhattacharya, Shaubhik
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Phosit, Salisa
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Kongsamlit, Sawarod
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Saha, Sriparna
Social media platforms have both positive and negative impacts on users in diverse societies. One of the adverse effects of social media platforms is the usage of hate and offensive language, which not only fosters prejudice but also harms the vulnerable. Additionally, a person’s sentiment and emotional state heavily influence the intended content of any social media post. Despite extensive research being conducted to detect online hate speech in English, there is a lack of similar studies on low-resource languages such as Thai. The recent enactment of laws like the “right to explanations” in the General Data Protection Regulation has stimulated the development of interpretable models rather than solely focusing on performance. Motivated by this, we created the first benchmark hate speech corpus, called Ex-ThaiHate, in the Thai language. Each post is annotated with four labels, namely hate, sentiment, emotion, and rationales (explainability), which specify the phrases that are responsible for annotating the post as hate. In order to investigate the effect of sentiment and emotional information on detecting hate speech posts, we propose a unified generative framework called GenX, which redefines this multi-task problem as a text-to-text generation task to simultaneously solve four tasks: hate-speech identification, rationale detection, sentiment, and emotion detection. Our extensive experiments demonstrate that GenX significantly outperforms all baselines and state-of-the-art models, thereby highlighting its effectiveness in detecting hate speech and identifying the rationales in low-resource languages. The code and dataset are available at https://github.com/dsmlr/Ex-ThaiHate. Disclaimer: The article contains offensive text and profanity. This is due to the nature of the work and does not reflect any opinion or stance of the authors.

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