[Objective] This paper is an empirical study aimed at gaining insightsinto identifying the instinctive information demands of individuals involved in or impacted by ADHD (attention-deficit/hyperactivity disorder), including those caring for individuals with ADHD, to engage in online health community activities. We accomplish this work by collecting, cleaning, and analyzing user-generated posts in ADHD OHCs (online health communities).

[Design/methodology/approach] This study uses Self-Determination Theory (SDT) to explore users’ instinctive motivations. This paper introduces the classical Latent Dirichlet Allocation (LDA) as a text-mining method to analyze and categorize topics. In the process, this study conducts a joint consistency and perplexity test on the clustering results to detect the optimal number of topics and improve the accuracy of topic identification. In addition, this study mapped the obtained topic clustering results to four main scenarios, including the online health community homepage, topic detail page, personal information page, and posting page, to explore users’ health information demands in different scenarios.

[Results] The results suggest that text mining methods can help identify user demands related to health and platforms and enable a more comprehensive assessment of the information demands of users with ADHD.

[Originality/value] This study uses text mining methods to summarize the vast amount of text content in OHCs, further enriching our understanding of the types of user information demands. Applying the SDT theory to the motivation assessment of ADHD patients enhances the interpretability of the text clustering results. It helps discover online users’ motivations and preferences when engaging in community activities.