HP-BERT: A framework for longitudinal study of Hinduphobia on social media via language models
This work addresses the problem of monitoring religious discrimination on social media for researchers and policymakers, but it is incremental as it applies existing methods to a new domain-specific dataset.
The researchers tackled the problem of analyzing anti-Hindu sentiment (Hinduphobia) on social media during the COVID-19 pandemic by developing a computational framework and a BERT-based model, achieving 94.72% accuracy on a curated dataset of 8,000 tweets and analyzing 27.4 million tweets across six countries to find moderate correlations (r = 0.312-0.428) between COVID-19 case increases and Hinduphobic content.
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.