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

作者：

Highlights：

• An adaptive model for automatic hate speech detection is proposed.

• Case studies on hate speech related to COVID19 and US Presidential elections.

• Emphasis on cross-dataset generalization.

• User-bias present in the available datasets is managed by imposing restrictions.

摘要

•An adaptive model for automatic hate speech detection is proposed.•Case studies on hate speech related to COVID19 and US Presidential elections.•Emphasis on cross-dataset generalization.•User-bias present in the available datasets is managed by imposing restrictions.

论文关键词：Hate speech detection,Ensemble learning,Social media

论文评审过程：Received 27 August 2020, Revised 10 June 2021, Accepted 18 July 2021, Available online 27 July 2021, Version of Record 2 August 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115632