Article
Version 1
Preserved in Portico This version is not peer-reviewed
Multi-Model Learning to Detect Twitter Hate Speech
Version 1
: Received: 23 March 2022 / Approved: 25 March 2022 / Online: 25 March 2022 (02:10:12 CET)
How to cite: Patil, D.; Pattewar, T. Multi-Model Learning to Detect Twitter Hate Speech. Preprints 2022, 2022030333. https://doi.org/10.20944/preprints202203.0333.v1 Patil, D.; Pattewar, T. Multi-Model Learning to Detect Twitter Hate Speech. Preprints 2022, 2022030333. https://doi.org/10.20944/preprints202203.0333.v1
Abstract
Users on the social networking platform have the freedom to express themselves freely. Towards the same time, this has created a forum for disagreement and hate directed at someone, society, racism, sexual orientation, and so on. Identifying hate online is a challenging task. Researchers from all around the world have contributed major methods for detecting hate speech, but owing to the issue's complexity, there are still many unresolved issues. In this research, we offer a multi-model learning strategy for detecting hate speech on Twitter. We utilised the Kaggle TwitterHate dataset, which had 31962 tweets categorised as binary hate or non-hate, to evaluate our technique. The suggested method is tested using commonly used machine learning classifiers with multi-model technique. Using TF-IDF features, we acquired detection results of 96.29 %, precision of 96%, recall of 96%, and f1-score of 96%.
Keywords
Hate speech detection; Social media; Machine learning; Multi-model learning
Subject
Engineering, Control and Systems Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment