Detecting harassment and defamation in cyberbullying with emotion-adaptive training
Authors: Peiling Yi, Arkaitz Zubiaga, YUNFEI LONG


Reviewer paper summaries: This paper proposes a novel approach to detect cyberbullying in online spaces, specifically targeting celebrities. Traditional methods have largely focused on binary classification of explicit cyberbullying, which overlooks more subtle forms like denigration and defamation. The authors introduce an emotion-adaptive training framework (EAT) designed to enhance detection capabilities by leveraging emotional data.
Summary of strengths: The author made an attempt to transfer emotion detection dataset to cyberbullying detection with evaluations of language models with different training objectives.
Summary of weaknesses: The writing lacks rigor and demonstrates a limited understanding of large language models. It would benefit from a more detailed and precise analysis, providing clearer insights into the complexities and functionalities of these models.
Comments, suggestions and typos: From line 10 to line 15, I am not sure if the author is writing about the training objective or training methods. Also, I think the author is referring to "low-resource settings" rather than "low source setting".
Line 167: ", However" should be revised to ". However"
There are many typos and inaccurate usage of English throughout the paper.
Overall assessment scores: 1