Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 23 November 2020 / Approved: 25 November 2020 / Online: 25 November 2020 (14:12:07 CET)

A peer-reviewed article of this Preprint also exists.

Vashistha, N.; Zubiaga, A. Online Multilingual Hate Speech Detection: Experimenting with Hindi and English Social Media. Information 2021, 12, 5. Vashistha, N.; Zubiaga, A. Online Multilingual Hate Speech Detection: Experimenting with Hindi and English Social Media. Information 2021, 12, 5.

Journal reference: Information 2020, 12, 5
DOI: 10.3390/info12010005

Abstract

The exponential increase in the use of the Internet and social media over the last two decades has changed human interaction. This has led to many positive outcomes, but at the same time it has brought risks and harms. While the volume of harmful content online, such as hate speech, is not manageable by humans, 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 and classify them into three classes, abusive, hateful or neither. We create a baseline model and we improve model performance scores using various optimisation techniques. After attaining a competitive performance score, we create a tool which identifies and scores a page with effective metric in near-real time and uses the same as feedback to re-train our model. We prove the competitive performance of our multilingual model on two langauges, English and Hindi, leading to comparable or superior performance to most monolingual models.

Subject Areas

social media; hate speech; text classification

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