Department of Child Psychiatry, Université de Lyon, Lyon, France.
Department of Internal Medicine, Toulouse University, Toulouse, France.
J Med Internet Res. 2022 Jun 15;24(6):e32912. doi: 10.2196/32912.
Social media provide a window onto the circulation of ideas in everyday folk psychiatry, revealing the themes and issues discussed both by the public and by various scientific communities.
This study explores the trends in health information about autism spectrum disorder within popular and scientific communities through the systematic semantic exploration of big data gathered from Twitter and PubMed.
First, we performed a natural language processing by text-mining analysis and with unsupervised (machine learning) topic modeling on a sample of the last 10,000 tweets in English posted with the term #autism (January 2021). We built a network of words to visualize the main dimensions representing these data. Second, we performed precisely the same analysis with all the articles using the term "autism" in PubMed without time restriction. Lastly, we compared the results of the 2 databases.
We retrieved 121,556 terms related to autism in 10,000 tweets and 5.7x109 terms in 57,121 biomedical scientific articles. The 4 main dimensions extracted from Twitter were as follows: integration and social support, understanding and mental health, child welfare, and daily challenges and difficulties. The 4 main dimensions extracted from PubMed were as follows: diagnostic and skills, research challenges, clinical and therapeutical challenges, and neuropsychology and behavior.
This study provides the first systematic and rigorous comparison between 2 corpora of interests, in terms of lay representations and scientific research, regarding the significant increase in information available on autism spectrum disorder and of the difficulty to connect fragments of knowledge from the general population. The results suggest a clear distinction between the focus of topics used in the social media and that of scientific communities. This distinction highlights the importance of knowledge mobilization and exchange to better align research priorities with personal concerns and to address dimensions of well-being, adaptation, and resilience. Health care professionals and researchers can use these dimensions as a framework in their consultations to engage in discussions on issues that matter to beneficiaries and develop clinical approaches and research policies in line with these interests. Finally, our study can inform policy makers on the health and social needs and concerns of individuals with autism and their caregivers, especially to define health indicators based on important issues for beneficiaries.
社交媒体为日常民间精神病学中思想的传播提供了一个窗口，揭示了公众和各种科学共同体讨论的主题和问题。
本研究通过对从 Twitter 和 PubMed 系统收集的大数据进行自然语言处理的文本挖掘分析和无监督（机器学习）主题建模，探索大众和科学界中关于自闭症谱系障碍的健康信息趋势。
首先，我们对过去 10000 条以英文发布的带有#自闭症（2021 年 1 月）的推文进行自然语言处理的文本挖掘分析和无监督（机器学习）主题建模，分析了 10000 条推文。我们构建了一个单词网络来可视化代表这些数据的主要维度。其次，我们在 PubMed 中对所有使用“自闭症”的文章进行了完全相同的分析，没有时间限制。最后，我们比较了这两个数据库的结果。
我们从 10000 条推文中检索到 121556 个与自闭症相关的术语，从 57121 篇生物医学科学文章中检索到 5.7x109 个术语。从 Twitter 提取的 4 个主要维度如下：融合和社会支持、理解和心理健康、儿童福利以及日常挑战和困难。从 PubMed 提取的 4 个主要维度如下：诊断和技能、研究挑战、临床和治疗挑战以及神经心理学和行为。
本研究首次对大众和科学研究中关于自闭症谱系障碍的信息可用性和将公众知识碎片联系起来的困难程度方面的两个语料库进行了系统和严格的比较。结果表明，社交媒体和科学界关注的主题之间存在明显区别。这种区别强调了知识动员和交流的重要性，以便更好地使研究重点与个人关注相一致，并解决福祉、适应和弹性等方面的问题。医疗保健专业人员和研究人员可以将这些维度用作咨询框架，就受益人的重要问题进行讨论，并根据这些利益制定临床方法和研究政策。最后，我们的研究可以为政策制定者提供有关自闭症患者及其照顾者的健康和社会需求和关注点的信息，特别是根据受益人的重要问题来定义健康指标。
