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Modeling social support in autism community on social media

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Abstract

With the high prevalence of autism spectrum disorder (ASD) among the younger generation, there is a shortage of adequate resources to deliver care for the individuals dealing with autism. Families dealing with autism face huge economic costs and emotional stress to provide care for personnel diagnosed with ASD. Globally almost two billion people use social media regularly. Social media have many advantages such as it connects a large group of individuals with the same subject of interest located across geographical miles. For the families dealing with autism, social media sites provide an open and easily accessible platform to share, gather and exchange information. Social science and information science literature lacks any mathematical definition of social support assessment; our proposed information-theoretic model to assess social support leverages theoretical definitions of social support from social science, linguistics, and social network theory. The proposed methodology enables us not only to estimate social support in a health community, but also to evaluate the effectiveness of communities. The research design used in the study is also generic and applies to other online healthcare communities as well as general online or virtual communities, such as gaming, education, and learning. In the study, we systematically analyze the interactions among families of autism communities on different social media platforms to extract knowledge and to assess the support propagated via those interactions within the autism community. The study found that the autism community provides significant social support to its members both in Twitter and blogs. Social support facilitated by community members can help caregivers surmount challenges and be effective in reducing psychological stress and enhancing the quality of life for individuals diagnosed with ASD.

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Acknowledgments

This research was funded in part by the Jerry L. Maulden-Entergy endowment at the UALR. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organization. The researchers gratefully acknowledge the support.

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Authors and Affiliations

  1. Center for Distance Health, University of Arkansas Medical Science, Little Rock, AR, USA

    Amit Saha

  2. Maulden-Entergy Endowed Chair Professor of Information Science, University of Arkansas at Little Rock, Little Rock, AR, USA

    Nitin Agarwal

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  1. Amit Saha
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  2. Nitin Agarwal
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Correspondence to Nitin Agarwal.

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Saha, A., Agarwal, N. Modeling social support in autism community on social media. Netw Model Anal Health Inform Bioinforma 5, 8 (2016). https://doi.org/10.1007/s13721-016-0115-8

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