DeWitt Skylar, Mills Kendall, Briggs Adam M
Department of Psychology, Eastern Michigan University, Ypsilanti, MI 48197, USA.
Behav Sci (Basel). 2025 Jun 13;15(6):812. doi: 10.3390/bs15060812.
Using an inductive computational approach, our present data exploration sought to use machine learning methodology to define and identify patterns and gain insight into autism-related discussions on Reddit across three different categories of subreddits: (a) individuals who self-identify as autistic, (b) parents of individuals on the autism spectrum, and (c) behavior therapists. By doing so, we sought to review authentic autism-related discussions and identify important topics that emerged across these three demographic groups, including insights related to assessing and treating challenging behavior. Following basic and advanced preprocessing, our extraction resulted in 57 subreddits and 46,914 comments from autism spectrum subreddit members, 46 subreddits and 27,838 comments from parent subreddit members, and six subreddits with 3163 comments from behavior therapist subreddit members. Subsequent network analyses revealed interesting patterns of discussion within and across subreddit groups that may be used to inform support and resources, practice considerations, and future directions for research.
通过采用归纳计算方法,我们当前的数据探索旨在运用机器学习方法来定义和识别模式,并深入了解Reddit上跨三类不同子版块的与自闭症相关的讨论:(a)自我认定为自闭症患者的个体,(b)自闭症谱系个体的父母,以及(c)行为治疗师。通过这样做,我们试图回顾真实的与自闭症相关的讨论,并识别出在这三个人口统计学群体中出现的重要话题,包括与评估和治疗具有挑战性的行为相关的见解。经过基础和高级预处理后,我们的提取得到了来自自闭症谱系子版块成员的57个子版块和46914条评论,来自父母子版块成员的46个子版块和27838条评论,以及来自行为治疗师子版块成员的6个子版块和3163条评论。随后的网络分析揭示了子版块群体内部和之间有趣的讨论模式,这些模式可用于为支持和资源、实践考量以及未来研究方向提供信息。