@inproceedings{4171af2264794b9080784e614ca0fb0a,
title = "PERSONA: Personality-based deep learning for detecting hate speech",
abstract = "Hate speech in an online environment has detrimental impacts on the wellbeing of individuals, online communities, and social network platforms. Consequently, the automated detection of hate speech has become a significant issue for various stakeholders. While previous studies have proposed many approaches for this issue, we find an important research gap that they have neglected a plethora of studies from psychology investigating the relationship between personality and hate. To fill the gap, we adopt a text-mining approach which fully automates the process of personality inference. Based its results, we build a personality-based deep learning model for detecting online hate speech (i.e., PERSONA). We validated our model with two real-world cases. The results show that our model significantly outperforms state-of-the-art baselines including a method proposed by Google. Our study paves the way for future research by incorporating psychological aspects into the design of a deep-learning model for hate speech detection.",
keywords = "Deep learning, Online hate speech, Personality, Text mining",
author = "Kyuhan Lee and Sudha Ram",
note = "Publisher Copyright: {\textcopyright} ICIS 2020. All rights reserved.; 2020 International Conference on Information Systems - Making Digital Inclusive: Blending the Local and the Global, ICIS 2020 ; Conference date: 13-12-2020 Through 16-12-2020",
year = "2021",
language = "English (US)",
series = "International Conference on Information Systems, ICIS 2020 - Making Digital Inclusive: Blending the Local and the Global",
publisher = "Association for Information Systems",
booktitle = "International Conference on Information Systems, ICIS 2020 - Making Digital Inclusive",
}