Sentiment analysis of post-COVID-19 health information needs of autism spectrum disorder community: insights from social media discussions - Larnyo, Ebenezer; Nutakor, Jonathan Aseye; Addai-Dansoh, Stephen; Nkrumah, Edmund Nana Kwame (Frontiers in Psychiatry, 2024)
This study explores the health information needs of individuals with autism spectrum disorder (ASD) and their caregivers in the post-COVID-19 era by analyzing discussions from Reddit, a popular social media platform. Utilizing a mixed-method approach that integrates qualitative content analysis with quantitative sentiment analysis, we analyzed user-generated content from the "r/autism" subreddit to identify recurring themes and sentiments. The qualitative analysis uncovered key themes, including symptoms, diagnostic challenges, caregiver experiences, treatment options, and stigma, reflecting the diverse concerns within the ASD community. The quantitative sentiment analysis revealed a predominance of positive sentiment across discussions, although significant instances of neutral and negative sentiments were also present, indicating varied experiences and perspectives among community members. Among the machine learning models used for sentiment classification, the Bi-directional Long Short-Term Memory (Bi-LSTM) model achieved the highest performance, demonstrating a validation accuracy of 95.74%. The findings highlight the need for improved digital platforms and community resources to address the specific health information needs of the ASD community, particularly in enhancing access to reliable information and fostering supportive environments. These insights can guide future interventions and policies aimed at improving the well-being of autistic persons and their caregivers.