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

Author

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  • Yuehua Zhao

    (School of Information Management, Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing University, Nanjing 210023, China)

  • Jin Zhang

    (School of Information Studies, University of Wisconsin Milwaukee, Milwaukee, WI 53211, USA)

  • Min Wu

    (College of Health Sciences, University of Wisconsin Milwaukee, Milwaukee, WI 53211, USA)

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.

Suggested Citation

  • Yuehua Zhao & Jin Zhang & Min Wu, 2019. "Finding Users’ Voice on Social Media: An Investigation of Online Support Groups for Autism-Affected Users on Facebook," IJERPH, MDPI, vol. 16(23), pages 1-13, November.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:23:p:4804-:d:292424
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    References listed on IDEAS

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    1. Kun Lu & Dietmar Wolfram, 2012. "Measuring author research relatedness: A comparison of word-based, topic-based, and author cocitation approaches," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(10), pages 1973-1986, October.
    2. Kun Lu & Dietmar Wolfram, 2012. "Measuring author research relatedness: A comparison of word‐based, topic‐based, and author cocitation approaches," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(10), pages 1973-1986, October.
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    Cited by:

    1. Zixuan Weng & Aijun Lin, 2022. "Public Opinion Manipulation on Social Media: Social Network Analysis of Twitter Bots during the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(24), pages 1-17, December.
    2. Pavel Bachmann, 2020. "Caregivers’ Experience of Caring for a Family Member with Alzheimer’s Disease: A Content Analysis of Longitudinal Social Media Communication," IJERPH, MDPI, vol. 17(12), pages 1-22, June.
    3. Jingfang Liu & Jun Kong & Xin Zhang, 2020. "Study on Differences between Patients with Physiological and Psychological Diseases in Online Health Communities: Topic Analysis and Sentiment Analysis," IJERPH, MDPI, vol. 17(5), pages 1-17, February.
    4. Binfeng Shi, 2024. "Transmission mechanism of public concern in waste-sorting policy: Evidence from text mining," Energy & Environment, , vol. 35(3), pages 1616-1636, May.

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