Examining the Mental Health Impact of Misinformation on Social Media Using a Hybrid Transformer-Based Approach
It addresses the mental health consequences of misinformation for social media users, but it is incremental as it applies a hybrid method to a known problem.
This study tackled the problem of misinformation on social media and its impact on mental health by developing a hybrid transformer-based approach, achieving accuracy rates of 98.4% for misinformation detection, 87.8% for assessing mental health implications, and 77.3% for disorder classification, with statistical validation showing a direct correlation (p-value = 0.003871).
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.