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      Using Social Media Data to Understand the Impact of Promotional Information on Laypeople’s Discussions: A Case Study of Lynch Syndrome

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          Abstract

          Background

          Social media is being used by various stakeholders among pharmaceutical companies, government agencies, health care organizations, professionals, and news media as a way of engaging audiences to raise disease awareness and ultimately to improve public health. Nevertheless, it is unclear what effects this health information has on laypeople.

          Objective

          This study aimed to provide a detailed examination of how promotional health information related to Lynch syndrome impacts laypeople’s discussions on a social media platform (Twitter) in terms of topic awareness and attitudes.

          Methods

          We used topic modeling and sentiment analysis techniques on Lynch syndrome–related tweets to answer the following research questions (RQs): (1) what are the most discussed topics in Lynch syndrome–related tweets?; (2) how promotional Lynch syndrome–related information on Twitter affects laypeople’s discussions?; and (3) what impact do the Lynch syndrome awareness activities in the Colon Cancer Awareness Month and Lynch Syndrome Awareness Day have on laypeople’s discussions and their attitudes? In particular, we used a set of keywords to collect Lynch syndrome–related tweets from October 26, 2016 to August 11, 2017 (289 days) through the Twitter public search application programming interface (API). We experimented with two different classification methods to categorize tweets into the following three classes: (1) irrelevant, (2) promotional health information, and (3) laypeople’s discussions. We applied a topic modeling method to discover the themes in these Lynch syndrome–related tweets and conducted sentiment analysis on each layperson’s tweet to gauge the writer’s attitude (ie, positive, negative, and neutral) toward Lynch syndrome. The topic modeling and sentiment analysis results were elaborated to answer the three RQs.

          Results

          Of all tweets (N=16,667), 87.38% (14,564/16,667) were related to Lynch syndrome. Of the Lynch syndrome–related tweets, 81.43% (11,860/14,564) were classified as promotional and 18.57% (2704/14,564) were classified as laypeople’s discussions. The most discussed themes were treatment (n=4080) and genetic testing (n=3073). We found that the topic distributions in laypeople’s discussions were similar to the distributions in promotional Lynch syndrome–related information. Furthermore, most people had a positive attitude when discussing Lynch syndrome. The proportion of negative tweets was 3.51%. Within each topic, treatment (16.67%) and genetic testing (5.60%) had more negative tweets compared with other topics. When comparing monthly trends, laypeople’s discussions had a strong correlation with promotional Lynch syndrome–related information on awareness ( r=.98, P<.001), while there were moderate correlations on screening ( r=.602, P=.05), genetic testing ( r=.624, P=.04), treatment ( r=.69, P=.02), and risk ( r=.66, P=.03). We also discovered that the Colon Cancer Awareness Month (March 2017) and the Lynch Syndrome Awareness Day (March 22, 2017) had significant positive impacts on laypeople’s discussions and their attitudes.

          Conclusions

          There is evidence that participative social media platforms, namely Twitter, offer unique opportunities to inform cancer communication surveillance and to explore the mechanisms by which these new communication media affect individual health behavior and population health.

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          Most cited references31

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          Social media use in medical education: a systematic review.

          The authors conducted a systematic review of the published literature on social media use in medical education to answer two questions: (1) How have interventions using social media tools affected outcomes of satisfaction, knowledge, attitudes, and skills for physicians and physicians-in-training? and (2) What challenges and opportunities specific to social media have educators encountered in implementing these interventions? The authors searched the MEDLINE, CINAHL, ERIC, Embase, PsycINFO, ProQuest, Cochrane Library, Web of Science, and Scopus databases (from the start of each through September 12, 2011) using keywords related to social media and medical education. Two authors independently reviewed the search results to select peer-reviewed, English-language articles discussing social media use in educational interventions at any level of physician training. They assessed study quality using the Medical Education Research Study Quality Instrument. Fourteen studies met inclusion criteria. Interventions using social media tools were associated with improved knowledge (e.g., exam scores), attitudes (e.g., empathy), and skills (e.g., reflective writing). The most commonly reported opportunities related to incorporating social media tools were promoting learner engagement (71% of studies), feedback (57%), and collaboration and professional development (both 36%). The most commonly cited challenges were technical issues (43%), variable learner participation (43%), and privacy/security concerns (29%). Studies were generally of low to moderate quality; there was only one randomized controlled trial. Social media use in medical education is an emerging field of scholarship that merits further investigation. Educators face challenges in adapting new technologies, but they also have opportunities for innovation.
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            National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic

            Social media have been proposed as a data source for influenza surveillance because they have the potential to offer real-time access to millions of short, geographically localized messages containing information regarding personal well-being. However, accuracy of social media surveillance systems declines with media attention because media attention increases “chatter” – messages that are about influenza but that do not pertain to an actual infection – masking signs of true influenza prevalence. This paper summarizes our recently developed influenza infection detection algorithm that automatically distinguishes relevant tweets from other chatter, and we describe our current influenza surveillance system which was actively deployed during the full 2012-2013 influenza season. Our objective was to analyze the performance of this system during the most recent 2012–2013 influenza season and to analyze the performance at multiple levels of geographic granularity, unlike past studies that focused on national or regional surveillance. Our system’s influenza prevalence estimates were strongly correlated with surveillance data from the Centers for Disease Control and Prevention for the United States (r = 0.93, p < 0.001) as well as surveillance data from the Department of Health and Mental Hygiene of New York City (r = 0.88, p < 0.001). Our system detected the weekly change in direction (increasing or decreasing) of influenza prevalence with 85% accuracy, a nearly twofold increase over a simpler model, demonstrating the utility of explicitly distinguishing infection tweets from other chatter.
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              A density-based method for adaptive LDA model selection

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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
                December 2017
                13 December 2017
                : 19
                : 12
                : e414
                Affiliations
                [1] 1 Department of Health Outcomes & Biomedical Informatics College of Medicine University of Florida Gainesville, FL United States
                [2] 2 Department of Management Warrington College of Business University of Florida Gainesville, FL United States
                [3] 3 Department of Epidemiology College of Public Health and Health Professions University of Florida Gainesville, FL United States
                [4] 4 Department of Epidemiology College of Medicine University of Florida Gainesville, FL United States
                [5] 5 School of Information Florida State University Tallahassee, FL United States
                [6] 6 School of Business Administration Zhejiang Gongshang University Hangzhou, Zhejiang China
                Author notes
                Corresponding Author: Yuan Sun d05sunyuan@ 123456zju.edu.cn
                Author information
                http://orcid.org/0000-0002-2238-5429
                http://orcid.org/0000-0002-5771-3373
                http://orcid.org/0000-0002-8139-2418
                http://orcid.org/0000-0003-0587-4105
                http://orcid.org/0000-0001-7004-3549
                http://orcid.org/0000-0002-9021-5595
                http://orcid.org/0000-0003-4585-0888
                http://orcid.org/0000-0003-3713-3264
                http://orcid.org/0000-0001-8291-6132
                http://orcid.org/0000-0003-3608-0244
                http://orcid.org/0000-0002-8659-1870
                Article
                v19i12e414
                10.2196/jmir.9266
                5745354
                29237586
                4c13033d-e64f-4192-bcad-9ef45d2e6a24
                ©Jiang Bian, Yunpeng Zhao, Ramzi G Salloum, Yi Guo, Mo Wang, Mattia Prosperi, Hansi Zhang, Xinsong Du, Laura J Ramirez-Diaz, Zhe He, Yuan Sun. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 13.12.2017.

                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
                : 31 October 2017
                : 15 November 2017
                : 17 November 2017
                : 17 November 2017
                Categories
                Original Paper
                Original Paper

                Medicine
                social media,lynch syndrome,public health surveillance,sentiment analysis
                Medicine
                social media, lynch syndrome, public health surveillance, sentiment analysis

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