Examining the Mental Health Impact of Misinformation on Social Media Using a Hybrid Transformer-Based Approach

2025-03-04

AI Summary

This study systematically investigates the mechanisms through which social media misinformation impacts public mental health. We propose a RoBERTa-LSTM hybrid architecture and develop a three-tier joint modeling framework—comprising misinformation detection, psychological impact assessment, and psychiatric disorder classification—enabling end-to-end, fine-grained cross-level risk modeling. Using multi-task joint fine-tuning and Pearson’s chi-square test, our model achieves state-of-the-art performance on all three tasks: 98.4% accuracy in misinformation detection, 87.8% in psychological impact identification, and 77.3% in psychiatric disorder classification. Crucially, we provide the first empirical evidence of a statistically significant association between misinformation exposure and deteriorating mental health (p = 0.003871). Our contributions include: (1) establishing the first interpretable causal inference pathway linking misinformation to mental health outcomes; (2) enabling granular, end-to-end psychological risk modeling across cognitive, affective, and clinical levels; and (3) delivering a deployable technical paradigm for platform-level intervention and clinical mental health screening.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Misinformation & Fake News
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Responsible Web: Mitigating misinformation and disinformation
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• Analyzes mental health impact of social media misinformation.
• Develops hybrid transformer model to detect misinformation.
• Validates correlation between misinformation and mental health decline.

Innovation

• Hybrid transformer-based approach for misinformation detection
• RoBERTa-LSTM classifier for mental health impact assessment
• High accuracy in misinformation and disorder classification