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      Social media engagement analysis of U.S. Federal health agencies on Facebook

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          Abstract

          Background

          It is becoming increasingly common for individuals and organizations to use social media platforms such as Facebook. These are being used for a wide variety of purposes including disseminating, discussing and seeking health related information. U.S. Federal health agencies are leveraging these platforms to ‘engage’ social media users to read, spread, promote and encourage health related discussions. However, different agencies and their communications get varying levels of engagement. In this study we use statistical models to identify factors that associate with engagement.

          Methods

          We analyze over 45,000 Facebook posts from 72 Facebook accounts belonging to 24 health agencies. Account usage, user activity, sentiment and content of these posts are studied. We use the hurdle regression model to identify factors associated with the level of engagement and Cox proportional hazards model to identify factors associated with duration of engagement.

          Results

          In our analysis we find that agencies and accounts vary widely in their usage of social media and activity they generate. Statistical analysis shows, for instance, that Facebook posts with more visual cues such as photos or videos or those which express positive sentiment generate more engagement. We further find that posts on certain topics such as occupation or organizations negatively affect the duration of engagement.

          Conclusions

          We present the first comprehensive analyses of engagement with U.S. Federal health agencies on Facebook. In addition, we briefly compare and contrast findings from this study to our earlier study with similar focus but on Twitter to show the robustness of our methods.

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          Most cited references37

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          Trust and sources of health information: the impact of the Internet and its implications for health care providers: findings from the first Health Information National Trends Survey.

          The context in which patients consume health information has changed dramatically with diffusion of the Internet, advances in telemedicine, and changes in media health coverage. The objective of this study was to provide nationally representative estimates for health-related uses of the Internet, level of trust in health information sources, and preferences for cancer information sources. Data from the Health Information National Trends Survey were used. A total of 6369 persons 18 years or older were studied. The main outcome measures were online health activities, levels of trust, and source preference. Analyses indicated that 63.0% (95% confidence interval [CI], 61.7%-64.3%) of the US adult population in 2003 reported ever going online, with 63.7% (95% CI, 61.7%-65.8%) of the online population having looked for health information for themselves or others at least once in the previous 12 months. Despite newly available communication channels, physicians remained the most highly trusted information source to patients, with 62.4% (95% CI, 60.8%-64.0%) of adults expressing a lot of trust in their physicians. When asked where they preferred going for specific health information, 49.5% (95% CI, 48.1%-50.8%) reported wanting to go to their physicians first. When asked where they actually went, 48.6% (95% CI, 46.1%-51.0%) reported going online first, with only 10.9% (95% CI, 9.5%-12.3%) going to their physicians first. The Health Information National Trends Survey data portray a tectonic shift in the ways in which patients consume health and medical information, with more patients looking for information online before talking with their physicians.
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              Sentiment strength detection in short informal text

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                Author and article information

                Contributors
                sanmitra-bhattacharya@uiowa.edu
                padmini-srinivasan@uiowa.edu
                philip-polgreen@uiowa.edu
                Journal
                BMC Med Inform Decis Mak
                BMC Med Inform Decis Mak
                BMC Medical Informatics and Decision Making
                BioMed Central (London )
                1472-6947
                21 April 2017
                21 April 2017
                2017
                : 17
                : 49
                Affiliations
                [1 ]ISNI 0000 0004 1936 8294, GRID grid.214572.7, Department of Computer Science, , The University of Iowa, ; Iowa City, IA 52242 USA
                [2 ]Linguamatics Solutions Inc., Westborough, MA 01581 USA
                [3 ]ISNI 0000 0004 1936 8294, GRID grid.214572.7, Department of Internal Medicine, , The University of Iowa, ; Iowa City, IA 52242 USA
                Author information
                http://orcid.org/0000-0002-1697-3179
                Article
                447
                10.1186/s12911-017-0447-z
                5401385
                28431582
                25a2b1c9-8f78-44ef-9805-a47e5aadc69d
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 5 November 2016
                : 13 April 2017
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Bioinformatics & Computational biology
                social media mining,facebook,engagement analysis,data mining,hurdle model,proportional hazards model,statistical modeling

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