Finding Users’ Voice on Social Media: An Investigation of Online Support Groups for Autism-Affected Users on Facebook

Abstract

The trend towards the use of the Internet for health information purposes is rising. Utilization of various forms of social media has been a key interest in consumer health informatics (CHI). To reveal the information needs of autism-affected users, this study centers on the research of users’ interactions and information sharing within autism communities on social media. It aims to understand how autism-affected users utilize support groups on Facebook by applying natural language process (NLP) techniques to unstructured health data in social media. An interactive visualization method (pyLDAvis) was employed to evaluate produced models and visualize the inter-topic distance maps. The revealed topics (e.g., parenting, education, behavior traits) identify issues that individuals with autism were concerned about on a daily basis and how they addressed such concerns in the form of group communication. In addition to general social support, disease-specific information, collective coping strategies, and emotional support were provided as well by group members based on similar personal experiences. This study concluded that Latent Dirichlet Allocation (LDA) is feasible and appropriated to derive topics (focus) from messages posted to the autism support groups on Facebook. The revealed topics help healthcare professionals (content providers) understand autism from users’ perspectives and provide better patient communications.

Categorising patient concerns using natural language processing techniques

“…14 One particular unsupervised topic modelling method, latent Dirichlet allocation (LDA), 15 has proven particularly popular and successful. LDA has been used for topic mining in studies of health data across an array of data sources, including discussions from condition-specific online support groups [16][17][18][19][20] and more general online discussion platforms, [21][22][23][24][25][26][27][28][29] data about adverse medical events, 30 interview transcripts of patients, 31 32 media articles 33 and survey data. 34 35 Other studies have used LDA to analyse topics in patient-reported concerns as well, in situations where no existing topic information is available.…”

Characterization and Comparison of the Utilization of Facebook Groups Between Public Medical Professionals and Technical Communities to Facilitate Idea Sharing and Crowdsourcing During the COVID-19 Pandemic: Cross-sectional Observational Study

“…Humor has been well evidenced as an adaptive mechanism for stress [17] and to reduce anxiety [18,19], enhance mood [19], and as a potential tool for psychotherapy [20][21][22][23][24][25]. The role of news sharing and providing humor and inspiration is analogous to that of a virtual support group, with the potential to connect individuals and foster reflections and conversations [26,27]. Consequently, it may be important for health care professionals to utilize these layperson Facebook groups to communicate with and educate laypeople and to understand their perspectives and experiences during the COVID-19 pandemic, provide supporting resources, and potentially facilitate grassroot movements (such as "#stayathome" and "#wearamask").…”

Identifying health information needs of senior online communities users: a text mining approach

“…In addition to those analyzed questions, posts in online communities have also been used (Chen et al, 2020;Liu et al, 2018;Ruthven, 2019). These data resources are especially suitable for populations that are difficult to select in Q&A platforms, such as senior netizens (Zhao et al, 2020a) and autism-affected users (Zhao et al, 2019).…”