用AI分析疫情中社交媒体对印度教徒的歧视，发现疫情越严重歧视言论越多。
HP-BERT: A framework for longitudinal study of Hinduphobia on social media via language models
- 基于8000条人工标注推文训练专用模型HP-BERT，提升反印度教情绪识别准确率。
- 分析6国2740万条推文，发现新冠病例增加与反印度教内容呈中度正相关（r=0.312-0.428）。
- 公开了首个疫情期反印度教推文数据集，适合社会学与算法研究者使用。
新冠疫情期间，社区矛盾加剧，导致对多个宗教群体的歧视情绪上升，包括印度教徒。语言模型的进展为社交媒体分析提供了新可能，尤其适用于长期追踪平台如X（原Twitter）。本文提出一种计算框架，用于分析疫情期间反印度教情绪（Hinduphobia），并开发了一种针对长期分析的滥用检测与情感分析方法。我们构建并发布了「Hinduphobic COVID-19 XDataset」，包含8,000条已标注且人工验证的推文。在此基础上，我们训练了反印度教BERT（HP-BERT）模型，达到94.72%的准确率，优于基线Transformer模型。该模型通过额外微调具备多标签情感分析能力。分析覆盖六个国家约2740万条推文，包括澳大利亚、巴西、印度、印度尼西亚、日本和英国。统计分析显示，新冠病例增长与反印度教内容数量存在中度相关性（r = 0.312–0.428），表明疫情压力可能助长歧视性言论。本研究为疫情危机中社交媒体上的宗教歧视提供了实证依据。
原文摘要 · Abstract (English)
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.
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