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Combating hate speech using an adaptive ensemble learning model with a case study on COVID-19

Social media platforms generate an enormous amount of data every day. Millions of users engage themselves with the posts circulated on these platforms. Despite the social regulations and protocols imposed by these platforms, it is difficult to restrict some objectionable posts carrying hateful conte...

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Detalles Bibliográficos
Autores principales: Agarwal, Shivang, Chowdary, C. Ravindranath
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9759712/
https://www.ncbi.nlm.nih.gov/pubmed/36567759
http://dx.doi.org/10.1016/j.eswa.2021.115632
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author Agarwal, Shivang
Chowdary, C. Ravindranath
author_facet Agarwal, Shivang
Chowdary, C. Ravindranath
author_sort Agarwal, Shivang
collection PubMed
description Social media platforms generate an enormous amount of data every day. Millions of users engage themselves with the posts circulated on these platforms. Despite the social regulations and protocols imposed by these platforms, it is difficult to restrict some objectionable posts carrying hateful content. Automatic hate speech detection on social media platforms is an essential task that has not been solved efficiently despite multiple attempts by various researchers. It is a challenging task that involves identifying hateful content from social media posts. These posts may reveal hate outrageously, or they may be subjective to the user or a community. Relying on manual inspection delays the process, and the hateful content may remain available online for a long time. The current state-of-the-art methods for tackling hate speech perform well when tested on the same dataset but fail miserably on cross-datasets. Therefore, we propose an ensemble learning-based adaptive model for automatic hate speech detection, improving the cross-dataset generalization. The proposed expert model for hate speech detection works towards overcoming the strong user-bias present in the available annotated datasets. We conduct our experiments under various experimental setups and demonstrate the proposed model’s efficacy on the latest issues such as COVID-19 and US presidential elections. In particular, the loss in performance observed under cross-dataset evaluation is the least among all the models. Also, while restricting the maximum number of tweets per user, we incur no drop in performance.
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spelling pubmed-97597122022-12-19 Combating hate speech using an adaptive ensemble learning model with a case study on COVID-19 Agarwal, Shivang Chowdary, C. Ravindranath Expert Syst Appl Article Social media platforms generate an enormous amount of data every day. Millions of users engage themselves with the posts circulated on these platforms. Despite the social regulations and protocols imposed by these platforms, it is difficult to restrict some objectionable posts carrying hateful content. Automatic hate speech detection on social media platforms is an essential task that has not been solved efficiently despite multiple attempts by various researchers. It is a challenging task that involves identifying hateful content from social media posts. These posts may reveal hate outrageously, or they may be subjective to the user or a community. Relying on manual inspection delays the process, and the hateful content may remain available online for a long time. The current state-of-the-art methods for tackling hate speech perform well when tested on the same dataset but fail miserably on cross-datasets. Therefore, we propose an ensemble learning-based adaptive model for automatic hate speech detection, improving the cross-dataset generalization. The proposed expert model for hate speech detection works towards overcoming the strong user-bias present in the available annotated datasets. We conduct our experiments under various experimental setups and demonstrate the proposed model’s efficacy on the latest issues such as COVID-19 and US presidential elections. In particular, the loss in performance observed under cross-dataset evaluation is the least among all the models. Also, while restricting the maximum number of tweets per user, we incur no drop in performance. Elsevier Ltd. 2021-12-15 2021-07-27 /pmc/articles/PMC9759712/ /pubmed/36567759 http://dx.doi.org/10.1016/j.eswa.2021.115632 Text en © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Agarwal, Shivang
Chowdary, C. Ravindranath
Combating hate speech using an adaptive ensemble learning model with a case study on COVID-19
title Combating hate speech using an adaptive ensemble learning model with a case study on COVID-19
title_full Combating hate speech using an adaptive ensemble learning model with a case study on COVID-19
title_fullStr Combating hate speech using an adaptive ensemble learning model with a case study on COVID-19
title_full_unstemmed Combating hate speech using an adaptive ensemble learning model with a case study on COVID-19
title_short Combating hate speech using an adaptive ensemble learning model with a case study on COVID-19
title_sort combating hate speech using an adaptive ensemble learning model with a case study on covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9759712/
https://www.ncbi.nlm.nih.gov/pubmed/36567759
http://dx.doi.org/10.1016/j.eswa.2021.115632
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