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      Social Media as a Catalyst for Policy Action and Social Change for Health and Well-Being: Viewpoint

      research-article
      , PhD 1 ,
      (Reviewer), (Reviewer), (Reviewer)
      Journal of Medical Internet Research
      JMIR Publications
      social media, health policy, health promotion, health knowledge, attitudes, practice, social change

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          Abstract

          This viewpoint paper argues that policy interventions can benefit from the continued use of social media analytics, which can serve as an important complement to traditional social science data collection and analysis. Efforts to improve well-being should provide an opportunity to explore these areas more deeply, and encourage the efforts of those conducting national and local data collection on health to incorporate more of these emerging data sources.

          Social media remains a relatively untapped source of information to catalyze policy action and social change. However, the diversity of social media platforms and available analysis techniques provides multiple ways to offer insight for policy making and decision making. For instance, social media content can provide timely information about the impact of policy interventions. Social media location information can inform where to deploy resources or disseminate public messaging. Network analysis of social media connections can reveal underserved populations who may be disconnected from public services. Machine learning can help recognize important patterns for disease surveillance or to model population sentiment. To fully realize these potential policy uses, limitations to social media data will need to be overcome, including data reliability and validity, and potential privacy risks.

          Traditional data collection may not fully capture the upstream factors and systemic relationships that influence health and well-being. Policy actions and social change efforts, such as the Robert Wood Johnson Foundation’s effort to advance a culture of health, which are intended to drive change in a network of upstream health drivers, will need to incorporate a broad range of behavioral information, such as health attitudes or physical activity levels. Applying innovative techniques to emerging data has the potential to extract insight from unstructured data or fuse disparate sources of data, such as linking health attitudes that are expressed to health behaviors or broader health and well-being outcomes.

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          Most cited references30

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          I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience

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            Quantifying the burden of disease: the technical basis for disability-adjusted life years.

            C. Murray (1994)
            Detailed assumptions used in constructing a new indicator of the burden of disease, the disability-adjusted life year (DALY), are presented. Four key social choices in any indicator of the burden of disease are carefully reviewed. First, the advantages and disadvantages of various methods of calculating the duration of life lost due to a death at each age are discussed. DALYs use a standard expected-life lost based on model life-table West Level 26. Second, the value of time lived at different ages is captured in DALYs using an exponential function which reflects the dependence of the young and the elderly on adults. Third, the time lived with a disability is made comparable with the time lost due to premature mortality by defining six classes of disability severity. Assigned to each class is a severity weight between 0 and 1. Finally, a three percent discount rate is used in the calculation of DALYs. The formula for calculating DALYs based on these assumptions is provided.
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              The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place

              We conduct a detailed investigation of correlations between real-time expressions of individuals made across the United States and a wide range of emotional, geographic, demographic, and health characteristics. We do so by combining (1) a massive, geo-tagged data set comprising over 80 million words generated over the course of several recent years on the social network service Twitter and (2) annually-surveyed characteristics of all 50 states and close to 400 urban populations. Among many results, we generate taxonomies of states and cities based on their similarities in word use; estimate the happiness levels of states and cities; correlate highly-resolved demographic characteristics with happiness levels; and connect word choice and message length with urban characteristics such as education levels and obesity rates. Our results show how social media may potentially be used to estimate real-time levels and changes in population-level measures such as obesity rates.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                March 2018
                19 March 2018
                : 20
                : 3
                : e94
                Affiliations
                [1] 1 RAND Corporation Santa Monica, CA United States
                Author notes
                Corresponding Author: Douglas Yeung dyeung@ 123456rand.org
                Author information
                http://orcid.org/0000-0002-8608-6823
                Article
                v20i3e94
                10.2196/jmir.8508
                5881041
                29555624
                95cc7516-2fa8-4270-96a0-6e41f2a4d1d3
                ©Douglas Yeung. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 19.03.2018.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 18 July 2017
                : 23 November 2017
                : 17 January 2018
                : 23 January 2018
                Categories
                Viewpoint
                Viewpoint

                Medicine
                social media,health policy,health promotion,health knowledge, attitudes, practice,social change

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