Hate speech detection using static BERT embeddings

06/29/2021
by   Gaurav Rajput, et al.
0

With increasing popularity of social media platforms hate speech is emerging as a major concern, where it expresses abusive speech that targets specific group characteristics, such as gender, religion or ethnicity to spread violence. Earlier people use to verbally deliver hate speeches but now with the expansion of technology, some people are deliberately using social media platforms to spread hate by posting, sharing, commenting, etc. Whether it is Christchurch mosque shootings or hate crimes against Asians in west, it has been observed that the convicts are very much influenced from hate text present online. Even though AI systems are in place to flag such text but one of the key challenges is to reduce the false positive rate (marking non hate as hate), so that these systems can detect hate speech without undermining the freedom of expression. In this paper, we use ETHOS hate speech detection dataset and analyze the performance of hate speech detection classifier by replacing or integrating the word embeddings (fastText (FT), GloVe (GV) or FT + GV) with static BERT embeddings (BE). With the extensive experimental trails it is observed that the neural network performed better with static BE compared to using FT, GV or FT + GV as word embeddings. In comparison to fine-tuned BERT, one metric that significantly improved is specificity.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/13/2022

CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection

The last decade has witnessed a surge in the interaction of people throu...
research
03/14/2021

DeepHate: Hate Speech Detection via Multi-Faceted Text Representations

Online hate speech is an important issue that breaks the cohesiveness of...
research
02/08/2021

A study of text representations in Hate Speech Detection

The pervasiveness of the Internet and social media have enabled the rapi...
research
06/01/2021

Improving Automatic Hate Speech Detection with Multiword Expression Features

The task of automatically detecting hate speech in social media is gaini...
research
12/20/2022

AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection

Supervised approaches generally rely on majority-based labels. However, ...
research
04/03/2023

Hate Speech Targets Detection in Parler using BERT

Online social networks have become a fundamental component of our everyd...
research
04/11/2018

Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media

While social media empowers freedom of expression and individual voices,...

Please sign up or login with your details

Forgot password? Click here to reset