Please use this identifier to cite or link to this item: https://research.matf.bg.ac.rs/handle/123456789/1371
Title: An approach to automatic classification of hate speech in sports domain on social media
Authors: Vujičić Stanković, Staša 
Mladenović, Miljana
Affiliations: Informatics and Computer Science 
Keywords: Automatic hate speech recognition;Hate speech;Social media;Social networks;Sport
Issue Date: 1-Dec-2023
Rank: M21a
Publisher: Springer
Journal: Journal of Big Data
Abstract: 
Hate Speech encompasses different forms of trolling, bullying, harassment, and threats directed against specific individuals or groups. This phenomena is mainly expressed on Social Networks. For sports players, Social Media is a means of communication with the widest part of their fans and a way to face different cyber-aggression forms. These virtual attacks can harm players, distress them, cause them to feel bad for a long time, or even escalate into physical violence. To date, athletes were not observed as a vulnerable group, so they were not a subject of automatic Hate Speech detection and recognition from content published on Social Media. This paper explores whether a model trained on the dataset from one Social Media and not related to any specific domain can be efficient for the Hate Speech binary classification of test sets regarding the sports domain. The experiments deal with Hate Speech detection in Serbian. BiLSTM deep neural network was learned with different parameters, and the results showed high Precision of detecting Hate Speech in sports domain (96% and 97%) and pretty low Recall.
Description: 
Article is published in Journal of Big Data vol. 10, no. 1 Article no. 109, DOI 10.1186/s40537-023-00766-9
URI: https://research.matf.bg.ac.rs/handle/123456789/1371
DOI: 10.1186/s40537-023-00766-9
Rights: Attribution 3.0 United States
Appears in Collections:Research outputs

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