HP-BERT: A framework for longitudinal study of Hinduphobia on social media via language models

TL;DR

This paper introduces HP-BERT, a language model framework for longitudinal analysis of Hinduphobia on social media during COVID-19, utilizing a new dataset and achieving high accuracy in detecting anti-Hindu sentiments.

Contribution

The study presents a novel dataset and a specialized BERT-based model for detecting Hinduphobia, enabling large-scale, longitudinal social media analysis during the pandemic.

Findings

Achieved 94.72% accuracy in Hinduphobia detection.

Found moderate correlation between COVID-19 case increases and Hinduphobic content.

Analyzed 27.4 million tweets across six countries.

Abstract

During the COVID-19 pandemic, community tensions intensified, contributing to discriminatory sentiments against various religious groups, including Hindu communities. Recent advances in language models have shown promise for social media analysis with potential for longitudinal studies of social media platforms, such as X (Twitter). We present a computational framework for analyzing anti-Hindu sentiment (Hinduphobia) during the COVID-19 period, introducing an abuse detection and sentiment analysis approach for longitudinal analysis on X. We curate and release a "Hinduphobic COVID-19 XDataset" containing 8,000 annotated and manually verified tweets. We then develop the Hinduphobic BERT (HP-BERT) model using this dataset and achieve 94.72\% accuracy, outperforming baseline Transformer-based language models. The model incorporates multi-label sentiment analysis capabilities through additional fine-tuning. Our analysis encompasses approximately 27.4 million tweets from six countries, including Australia, Brazil, India, Indonesia, Japan, and the United Kingdom. Statistical analysis reveals moderate correlations (r = 0.312-0.428) between COVID-19 case increases and Hinduphobic content volume, highlighting how pandemic-related stress may contribute to discriminatory discourse. This study provides evidence of social media-based religious discrimination during a COVID-19 crisis.