Online Multilingual Hate Speech Detection: Experimenting with Hindi and English Social Media

Information

Neeraj Vashistha¹  Arkaitz Zubiaga¹ 

[1] School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK;

关键词: social media;    hate speech;    text classification;   

DOI  :  10.3390/info12010005

来源: DOAJ

【 摘 要 】

The last two decades have seen an exponential increase in the use of the Internet and social media, which has changed basic human interaction. This has led to many positive outcomes. At the same time, it has brought risks and harms. The volume of harmful content online, such as hate speech, is not manageable by humans. The interest in the academic community to investigate automated means for hate speech detection has increased. In this study, we analyse six publicly available datasets by combining them into a single homogeneous dataset. Having classified them into three classes, abusive, hateful or neither, we create a baseline model and improve model performance scores using various optimisation techniques. After attaining a competitive performance score, we create a tool that identifies and scores a page with an effective metric in near-real-time and uses the same feedback to re-train our model. We prove the competitive performance of our multilingual model in two languages, English and Hindi. This leads to comparable or superior performance to most monolingual models.

【 授权许可】

Unknown