Larnyo Ebenezer, Nutakor Jonathan Aseye, Addai-Dansoh Stephen, Nkrumah Edmund Nana Kwame
Center for Black Studies Research, University of California, Santa Barbara, Santa Barbara, CA, United States.
Department of Health Policy and Management, School of Management, Jiangsu University, Zhenjiang, Jiangsu, China.
Front Psychiatry. 2024 Oct 11;15:1441349. doi: 10.3389/fpsyt.2024.1441349. eCollection 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.
本研究通过分析热门社交媒体平台Reddit上的讨论，探讨新冠疫情后时代自闭症谱系障碍（ASD）患者及其照顾者的健康信息需求。
采用定性内容分析与定量情感分析相结合的混合方法，我们分析了“r/自闭症”子版块中用户生成的内容，以识别反复出现的主题和情感。
定性分析揭示了关键主题，包括症状、诊断挑战、照顾者经历、治疗选择和污名化，反映了ASD群体内的各种关切。定量情感分析显示，尽管讨论中也存在大量中性和负面情感的情况，但总体上积极情感占主导，这表明群体成员的经历和观点各不相同。在用于情感分类的机器学习模型中，双向长短期记忆（Bi-LSTM）模型表现最佳，验证准确率达95.74%。
研究结果凸显了改进数字平台和社区资源的必要性，以满足ASD群体的特定健康信息需求，特别是在增加获取可靠信息的机会和营造支持性环境方面。这些见解可为未来旨在改善自闭症患者及其照顾者福祉的干预措施和政策提供指导。
