Benchmarking Language Models for Cyberbullying Identification and Classification from Social-media Texts

Kanishk Verma, Tijana Milosevic, Keith Cortis, Brian Davis

Abstract
Cyberbullying is bullying perpetrated via the medium of modern communication technologies like social media networks and gaming platforms. Unfortunately, most existing datasets focusing on cyberbullying detection or classification are i) limited in number ii) usually targeted to one specific online social networking (OSN) platform, or iii) often contain low-quality annotations. In this study, we fine-tune and benchmark state of the art neural transformers for the binary classification of cyberbullying in social media texts, which is of high value to Natural Language Processing (NLP) researchers and computational social scientists. Furthermore, this work represents the first step toward building neural language models for cross OSN platform cyberbullying classification to make them as OSN platform agnostic as possible.
Anthology ID:
2022.lateraisse-1.4
Volume:
Proceedings of the First Workshop on Language Technology and Resources for a Fair, Inclusive, and Safe Society within the 13th Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Kolawole Adebayo, Rohan Nanda, Kanishk Verma, Brian Davis
Venue:
LATERAISSE
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
26–31
Language:
URL:
https://aclanthology.org/2022.lateraisse-1.4
DOI:
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Cite (ACL):
Kanishk Verma, Tijana Milosevic, Keith Cortis, and Brian Davis. 2022. Benchmarking Language Models for Cyberbullying Identification and Classification from Social-media Texts. In Proceedings of the First Workshop on Language Technology and Resources for a Fair, Inclusive, and Safe Society within the 13th Language Resources and Evaluation Conference, pages 26–31, Marseille, France. European Language Resources Association.
Cite (Informal):
Benchmarking Language Models for Cyberbullying Identification and Classification from Social-media Texts (Verma et al., LATERAISSE 2022)
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