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    Detecting Cyberbullying in Thai Memes: A Multimodal Approach Using Deep Learning
    (2025-01-01)
    Phosit, Salisa
    ;
    Kongsamlit, Sawarod
    ;
    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
    ;
    Bhattacharya, Shaubhik
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    Phosit, Salisa
    ;
    Kongsamlit, Sawarod
    ;
    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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    HateThaiSent: Sentiment-Aided Hate Speech Detection in Thai Language
    (2024-01-01)
    Maity, Krishanu
    ;
    Poornash, A. S.
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    Bhattacharya, Shaubhik
    ;
    Phosit, Salisa
    ;
    Kongsamlit, Sawarod
    Social media platforms are a double-edged sword: on the one hand, they enable the dissemination of information; but on the other hand, they also provide an avenue for spreading online abuse and harassment, such as hate speech. While significant research efforts are being devoted to detecting online hate speech in the English language, little attention has been paid to the Thai language. In this study, we created a benchmark dataset, called HateThaiSent, which labels each post with both hate speech and sentiment information. To detect hate speech, we created a multitask model that uses a dual-channel deep learning approach based on FastText and BERT embeddings, with an added capsule network. One channel utilizes pretrained FastText embeddings while the other uses embeddings from the BERT language model. We aimed to answer two research questions: (Q1) Does incorporating sentiment information improves the performance of hate speech detection (HD) in the Thai language? (Q2) What is the comparative effectiveness of two different approaches for sentiment-Aware HD in the Thai language: feature engineering versus multitasking? Our proposed approach outperformed other baselines and state-of-The-Art models on the HateThaiSent dataset, with overall accuracy/macro-F1 values of 89.67%/89.79%, and 80.92%/80.97% for hate speech and sentiment detection tasks, respectively. We concluded that multitasking is more effective than feature engineering in enhancing the performance of the main task (HD).
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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
    ;
    Bhattacharya, Shaubhik
    ;
    Phosit, Salisa
    ;
    Kongsamlit, Sawarod
    ;
    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.