Predicting online participation and adoption of autism unproven interventions: a case study of dietary interventions

Zhenni Ni, Yuxing Qian, Hao Li, Jin Mao and Feicheng Ma

Behaviour and Information Technology, 2025, vol. 44, issue 10, 2213-2225

Abstract: Family caregivers of autistic children are susceptible to unconfirmed fads and false claims regarding to the efficacy of unproven interventions. This study aims to predict family caregivers’ participation and adoption of unproven interventions in online communities. Based on the Diffusion of Innovations theory, we first divided the family caregivers’ adoption process into five stages: awareness, persuasion, decision, implementation, and confirmation. Social network analysis and natural language processing methods were subsequently utilised to characterise personal, environmental, and behavioural factors for predicting the formation of last three stage. The results indicated promising evidence for the application of machine learning algorithms in predicting family caregivers’ decision (AUC = 0.823), implementation (AUC = 0.887), and confirmation (AUC = 0.921). Furthermore, the results showed that factors such as social interaction, social persuasion, and modelling significantly contributed to family caregivers’ online community participation and facilitated the adoption process of unproven interventions. Family caregivers with stronger negative emotions expressed were more likely to adopt unproven interventions and recommend these interventions to other community members, thereby accelerating the diffusion of these interventions.

Date: 2025
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DOI: 10.1080/0144929X.2023.2284241

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