Abstract
Cyberbullying has become a big issue with the popularity of different social media networks and online communication apps. While plenty of research is going on to develop better models for cyberbullying detection in monolingual language, there is very little research on the code-mixed languages and explainability aspect of cyberbullying. Recent laws like “right to explanations” of General Data Protection Regulation, have spurred research in developing interpretable models rather than focusing on performance. Motivated by this we develop the first interpretable multi-task model called mExCB for automatic cyberbullying detection from code-mixed languages which can simultaneously solve several tasks, cyberbullying detection, explanation/rationale identification, target group detection and sentiment analysis. We have introduced BullyExplain, the first benchmark dataset for explainable cyberbullying detection in code-mixed language. Each post in BullyExplain dataset is annotated with four labels, i.e., bully label, sentiment label, target and rationales (explainability), i.e., which phrases are being responsible for annotating the post as a bully. The proposed multitask framework (mExCB) based on CNN and GRU with word and sub-sentence (SS) level attention is able to outperform several baselines and state of the art models when applied on BullyExplain dataset.
Disclaimer: The article contains offensive text and profanity. This is owing to the nature of the work, and do not reflect any opinion or stand of the authors.(The code and dataset are available at https://github.com/MaityKrishanu/BullyExplain-ICDAR.)
K. Maity and P. Jha–Both authors contributed equally to this research.
Krishanu Maity, Prince Jha: Both authors contributed equally to this research.
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Acknowledgement
Dr. Sriparna Saha gratefully acknowledges the Young Faculty Research Fellowship (YFRF) Award, supported by Visvesvaraya Ph.D. Scheme for Electronics and IT, Ministry of Electronics and Information Technology (MeitY), Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia) for carrying out this research. The Authors would also like to acknowledge the support of Ministry of Home Affairs (MHA), India, for conducting this research.
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Maity, K., Jha, P., Jain, R., Saha, S., Bhattacharyya, P. (2023). “Explain Thyself Bully”: Sentiment Aided Cyberbullying Detection with Explanation. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds) Document Analysis and Recognition - ICDAR 2023. ICDAR 2023. Lecture Notes in Computer Science, vol 14189. Springer, Cham. https://doi.org/10.1007/978-3-031-41682-8_9
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