Building Safer Social Spaces: Addressing Body Shaming with LLMs and Explainable AI
This study tackles body shaming on Reddit using a novel dataset of 8,067 comments from June to November 2024, encompassing external and self-directed harmful discourse. We assess traditional Machine Learning (ML), Deep Learning (DL), and transformer-based Large Language Models (LLMs) for detection,...
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University of science and culture
2025-07-01
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| Series: | International Journal of Web Research |
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| Online Access: | https://ijwr.usc.ac.ir/article_227438_8081ef7921e45be421d85e66e3dfe79b.pdf |
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| author | Sajedeh Talebi Neda Abdolvand |
| author_facet | Sajedeh Talebi Neda Abdolvand |
| author_sort | Sajedeh Talebi |
| collection | DOAJ |
| description | This study tackles body shaming on Reddit using a novel dataset of 8,067 comments from June to November 2024, encompassing external and self-directed harmful discourse. We assess traditional Machine Learning (ML), Deep Learning (DL), and transformer-based Large Language Models (LLMs) for detection, employing accuracy, F1-score, and Area Under the Curve (AUC). Fine-tuned Psycho-Robustly Optimized BERT Pretraining Approach (Psycho-RoBERTa), pre-trained on psychological texts, excels (accuracy: 0.98, F1-score: 0.994, AUC: 0.990), surpassing models like Extreme Gradient Boosting (XG-Boost) (accuracy: 0.972) and Convolutional Neural Network (CNN) (accuracy: 0.979) due to its contextual sensitivity. Local Interpretable Model-agnostic Explanations (LIME) enhance transparency by identifying influential terms like “fat” and “ugly.” A term co-occurrence network graph uncovers semantic links, such as “shame” and “depression,” revealing discourse patterns. Targeting Reddit’s anonymity-driven subreddits, the dataset fills a platform-specific gap. Integrating LLMs, LIME, and graph analysis, we develop scalable tools for real-time moderation to foster inclusive online spaces. Limitations include Reddit-specific data and potential misses of implicit shaming. Future research should explore multi-platform datasets and few-shot learning. These findings advance Natural Language Processing (NLP) for cyberbullying detection, promoting safer social media environments. |
| format | Article |
| id | doaj-art-3a3f1e1beed847909118aac97aacce05 |
| institution | Kabale University |
| issn | 2645-4343 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | University of science and culture |
| record_format | Article |
| series | International Journal of Web Research |
| spelling | doaj-art-3a3f1e1beed847909118aac97aacce052025-08-25T12:03:22ZengUniversity of science and cultureInternational Journal of Web Research2645-43432025-07-0183597210.22133/ijwr.2025.525312.1286Building Safer Social Spaces: Addressing Body Shaming with LLMs and Explainable AISajedeh Talebi0https://orcid.org/0009-0009-6761-6516Neda Abdolvand1https://orcid.org/0000-0003-3623-1284Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran.Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran.This study tackles body shaming on Reddit using a novel dataset of 8,067 comments from June to November 2024, encompassing external and self-directed harmful discourse. We assess traditional Machine Learning (ML), Deep Learning (DL), and transformer-based Large Language Models (LLMs) for detection, employing accuracy, F1-score, and Area Under the Curve (AUC). Fine-tuned Psycho-Robustly Optimized BERT Pretraining Approach (Psycho-RoBERTa), pre-trained on psychological texts, excels (accuracy: 0.98, F1-score: 0.994, AUC: 0.990), surpassing models like Extreme Gradient Boosting (XG-Boost) (accuracy: 0.972) and Convolutional Neural Network (CNN) (accuracy: 0.979) due to its contextual sensitivity. Local Interpretable Model-agnostic Explanations (LIME) enhance transparency by identifying influential terms like “fat” and “ugly.” A term co-occurrence network graph uncovers semantic links, such as “shame” and “depression,” revealing discourse patterns. Targeting Reddit’s anonymity-driven subreddits, the dataset fills a platform-specific gap. Integrating LLMs, LIME, and graph analysis, we develop scalable tools for real-time moderation to foster inclusive online spaces. Limitations include Reddit-specific data and potential misses of implicit shaming. Future research should explore multi-platform datasets and few-shot learning. These findings advance Natural Language Processing (NLP) for cyberbullying detection, promoting safer social media environments.https://ijwr.usc.ac.ir/article_227438_8081ef7921e45be421d85e66e3dfe79b.pdfbody shamingreddit machine learningdeep learninglarge language modelslocal interpretable model-agnostic explanationscontent moderation |
| spellingShingle | Sajedeh Talebi Neda Abdolvand Building Safer Social Spaces: Addressing Body Shaming with LLMs and Explainable AI International Journal of Web Research body shaming reddit machine learning deep learning large language models local interpretable model-agnostic explanations content moderation |
| title | Building Safer Social Spaces: Addressing Body Shaming with LLMs and Explainable AI |
| title_full | Building Safer Social Spaces: Addressing Body Shaming with LLMs and Explainable AI |
| title_fullStr | Building Safer Social Spaces: Addressing Body Shaming with LLMs and Explainable AI |
| title_full_unstemmed | Building Safer Social Spaces: Addressing Body Shaming with LLMs and Explainable AI |
| title_short | Building Safer Social Spaces: Addressing Body Shaming with LLMs and Explainable AI |
| title_sort | building safer social spaces addressing body shaming with llms and explainable ai |
| topic | body shaming reddit machine learning deep learning large language models local interpretable model-agnostic explanations content moderation |
| url | https://ijwr.usc.ac.ir/article_227438_8081ef7921e45be421d85e66e3dfe79b.pdf |
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