用混合模型分析社交媒体假信息对心理健康的危害
Examining the Mental Health Impact of Misinformation on Social Media Using a Hybrid Transformer-Based Approach
- 结合RoBERTa与LSTM检测假信息及其心理影响
- 假信息与心理健康恶化相关性显著（p=0.003871）
- 适合关注数字健康与AI伦理的研究者
社交媒体重塑人际沟通，既促进连接也加速假信息传播。未经控制的虚假叙事对心理健康造成深远影响，加剧压力、焦虑及认知偏执。本研究提出一种基于RoBERTa-LSTM的混合模型，用于检测假信息、评估其心理影响，并分类与假信息暴露相关的心理障碍。模型在假信息检测、心理影响评估和障碍分类任务中的准确率分别为98.4%、87.8%和77.3%。皮尔逊卡方独立性检验（p值=0.003871）验证了假信息与心理状态恶化之间存在显著关联。研究强调亟需加强假信息管理以缓解其心理后果。未来可拓展至包含语言、人口与文化变量的更大数据集，深化对假信息引发心理困扰的理解。
原文摘要 · Abstract (English)
Social media has significantly reshaped interpersonal communication, fostering connectivity while also enabling the proliferation of misinformation. The unchecked spread of false narratives has profound effects on mental health, contributing to increased stress, anxiety, and misinformation-driven paranoia. This study presents a hybrid transformer-based approach using a RoBERTa-LSTM classifier to detect misinformation, assess its impact on mental health, and classify disorders linked to misinformation exposure. The proposed models demonstrate accuracy rates of 98.4, 87.8, and 77.3 in detecting misinformation, mental health implications, and disorder classification, respectively. Furthermore, Pearson's Chi-Squared Test for Independence (p-value = 0.003871) validates the direct correlation between misinformation and deteriorating mental well-being. This study underscores the urgent need for better misinformation management strategies to mitigate its psychological repercussions. Future research could explore broader datasets incorporating linguistic, demographic, and cultural variables to deepen the understanding of misinformation-induced mental health distress.
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