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

arXiv: https://arxiv.org/abs/2501.05482 · HTML: https://mlsanity.com/paper/2501.05482/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 170 references that are in the map.
- First posted on arXiv 7 Jan 2025.

## Builds on (most cited first, 30 of 36)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [Fine-Tuning LLaMA for Multi-Stage Text Retrieval](https://mlsanity.com/paper/2310.08319/) (2023)
- [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020)
- [Directions in Abusive Language Training Data: Garbage In, Garbage Out](https://mlsanity.com/paper/2004.01670/) (2020)
- [A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media](https://mlsanity.com/paper/1910.12574/) (2019)
- [RobBERT: a Dutch RoBERTa-based Language Model](https://mlsanity.com/paper/2001.06286/) (2020)
- [Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media during the COVID-19 Crisis](https://mlsanity.com/paper/2005.12423/) (2020)
- [Developing a Multilingual Annotated Corpus of Misogyny and Aggression](https://mlsanity.com/paper/2003.07428/) (2020)
- [A systematic review of Hate Speech automatic detection using Natural Language Processing](https://mlsanity.com/paper/2106.00742/) (2021)
- [Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model](https://mlsanity.com/paper/2111.01515/) (2021)
- [Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities](https://mlsanity.com/paper/1804.05892/) (2018)
- [COVID-19 sentiment analysis via deep learning during the rise of novel cases](https://mlsanity.com/paper/2104.10662/) (2021)
- [SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic](https://mlsanity.com/paper/2006.10842/) (2020)
- [Deep learning via LSTM models for COVID-19 infection forecasting in India](https://mlsanity.com/paper/2101.11881/) (2021)
- [Semantic and sentiment analysis of selected Bhagavad Gita translations using BERT-based language framework](https://mlsanity.com/paper/2201.03115/) (2022)
- [Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-offs](https://mlsanity.com/paper/2003.11511/) (2020)
- [What Types of COVID-19 Conspiracies are Populated by Twitter Bots?](https://mlsanity.com/paper/2004.09531/) (2020)
- ["A Virus Has No Religion": Analyzing Islamophobia on Twitter During the COVID-19 Outbreak](https://mlsanity.com/paper/2107.05104/) (2021)
- [Multi-stage Pre-training over Simplified Multimodal Pre-training Models](https://mlsanity.com/paper/2107.14596/) (2021)
- [An evaluation of Google Translate for Sanskrit to English translation via sentiment and semantic analysis](https://mlsanity.com/paper/2303.07201/) (2023)
- [An analysis of vaccine-related sentiments from development to deployment of COVID-19 vaccines](https://mlsanity.com/paper/2306.13797/) (2023)

## Built on by (most cited first, 1 of 1)

- [An evaluation of LLMs and Google Translate for translation of selected Indian languages via sentiment and semantic analyses](https://mlsanity.com/paper/2503.21393/) (2025)

## Abstract (arXiv)

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.