Document Type

Conference Proceeding

Publication Date

1-24-2025

Publication Title

Social Networks Analysis and Mining

Volume

15213

Pages

355–370

Publisher Name

Springer Nature Switzerland AG

Publisher Location

Cham, Switzerland

Abstract

Social media has revolutionized communication, allowing people worldwide to connect and interact instantly. However, it has also led to increases in cyberbullying, which poses a significant threat to children and adolescents globally, affecting their mental health and well-being. It is critical to accurately detect the roles of individuals involved in cyberbullying incidents to effectively address the issue on a large scale. This study explores the use of machine learning models to detect the roles involved in cyberbullying interactions. After examining the AMiCA dataset and addressing class imbalance issues, we evaluate the performance of various models built with four underlying LLMs (i.e. BERT, RoBERTa, T5, and GPT-2) for role detection. Our analysis shows that oversampling techniques help improve model performance. The best model, a fine-tuned RoBERTa using oversampled data, achieved an overall F1 score of 83.5%, increasing to 89.3% after applying a prediction threshold. The top-2 F1 score without thresholding was 95.7%. Our method outperforms previously proposed models. After investigating the per-class model performance and confidence scores, we show that the models perform well in classes with more samples and less contextual confusion (e.g. Bystander Other), but struggle with classes with fewer samples (e.g. Bystander Assistant) and more contextual ambiguity (e.g. Harasser and Victim). This work highlights current strengths and limitations in the development of accurate models with limited data and complex scenarios.

Comments

Author Posting. © Springer Nature Switzerland AG, 2025. This is the author's version of the work. It is posted here by permission of Springer Nature for personal use, not for redistribution. The definitive version was published in Social Networks Analysis and Mining, January 24, 2025, http://dx.doi.org/10.1007/978-3-031-78548-1_26. 

Published as part of the ASONAM 2024 conference proceedings in the Lecture Notes in Computer Science (LNCS) series.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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