IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i23p4804-d292424.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/23/4804/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/16/23/4804/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Siluo & Han, Ruizhen & Wolfram, Dietmar & Zhao, Yuehua, 2016. "Visualizing the intellectual structure of information science (2006–2015): Introducing author keyword coupling analysis," Journal of Informetrics, Elsevier, vol. 10(1), pages 132-150.
    2. Lu Huang & Yijie Cai & Erdong Zhao & Shengting Zhang & Yue Shu & Jiao Fan, 2022. "Measuring the interdisciplinarity of Information and Library Science interactions using citation analysis and semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6733-6761, November.
    3. Rob Koopman & Shenghui Wang & Andrea Scharnhorst, 2017. "Contextualization of topics: browsing through the universe of bibliographic information," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1119-1139, May.
    4. Ballester, Omar & Penner, Orion, 2022. "Robustness, replicability and scalability in topic modelling," Journal of Informetrics, Elsevier, vol. 16(1).
    5. Wang, Qiang & Li, Rongrong, 2017. "Research status of shale gas: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 715-720.
    6. Manika Lamba & Margam Madhusudhan, 2019. "Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 477-505, August.
    7. Wang, Feifei & Dong, Jiaxin & Lu, Wanzhao & Xu, Shuo, 2023. "Collaboration prediction based on multilayer all-author tripartite citation networks: A case study of gene editing," Journal of Informetrics, Elsevier, vol. 17(1).
    8. Mehmet Ali Koseoglu, 2016. "Mapping the institutional collaboration network of strategic management research: 1980–2014," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(1), pages 203-226, October.
    9. Andrea Bonaccorsi & Nicola Melluso & Francesco Alessandro Massucci, 2022. "Exploring the antecedents of interdisciplinarity at the European Research Council: a topic modeling approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 6961-6991, December.
    10. Karine Bastos Leal & Luís Eduardo de Souza Robaina & André de Souza De Lima, 2022. "Coastal impacts of storm surges on a changing climate: a global bibliometric analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(2), pages 1455-1476, November.
    11. Jiang, Hanchen & Qiang, Maoshan & Lin, Peng, 2016. "A topic modeling based bibliometric exploration of hydropower research," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 226-237.
    12. Samira Ranaei & Arho Suominen & Alan Porter & Stephen Carley, 2020. "Evaluating technological emergence using text analytics: two case technologies and three approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 215-247, January.
    13. Yosuke Miyata & Emi Ishita & Fang Yang & Michimasa Yamamoto & Azusa Iwase & Keiko Kurata, 2020. "Knowledge structure transition in library and information science: topic modeling and visualization," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 665-687, October.
    14. Leah G. Nichols, 2014. "A topic model approach to measuring interdisciplinarity at the National Science Foundation," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 741-754, September.
    15. Jeong, Yoo Kyung & Song, Min & Ding, Ying, 2014. "Content-based author co-citation analysis," Journal of Informetrics, Elsevier, vol. 8(1), pages 197-211.
    16. Shalini Upadhyay & Nitin Upadhyay, 2023. "Mapping crisis communication in the communication research: what we know and what we don’t know," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-19, December.
    17. Dietmar Wolfram, 2015. "The symbiotic relationship between information retrieval and informetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2201-2214, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:16:y:2019:i:23:p:4804-:d:292424. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.