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      Toward a Mixed-Methods Research Approach to Content Analysis in The Digital Age: The Combined Content-Analysis Model and its Applications to Health Care Twitter Feeds

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          Abstract

          Background

          Twitter’s 140-character microblog posts are increasingly used to access information and facilitate discussions among health care professionals and between patients with chronic conditions and their caregivers. Recently, efforts have emerged to investigate the content of health care-related posts on Twitter. This marks a new area for researchers to investigate and apply content analysis (CA). In current infodemiology, infoveillance and digital disease detection research initiatives, quantitative and qualitative Twitter data are often combined, and there are no clear guidelines for researchers to follow when collecting and evaluating Twitter-driven content.

          Objective

          The aim of this study was to identify studies on health care and social media that used Twitter feeds as a primary data source and CA as an analysis technique. We evaluated the resulting 18 studies based on a narrative review of previous methodological studies and textbooks to determine the criteria and main features of quantitative and qualitative CA. We then used the key features of CA and mixed-methods research designs to propose the combined content-analysis (CCA) model as a solid research framework for designing, conducting, and evaluating investigations of Twitter-driven content.

          Methods

          We conducted a PubMed search to collect studies published between 2010 and 2014 that used CA to analyze health care-related tweets. The PubMed search and reference list checks of selected papers identified 21 papers. We excluded 3 papers and further analyzed 18.

          Results

          Results suggest that the methods used in these studies were not purely quantitative or qualitative, and the mixed-methods design was not explicitly chosen for data collection and analysis. A solid research framework is needed for researchers who intend to analyze Twitter data through the use of CA.

          Conclusions

          We propose the CCA model as a useful framework that provides a straightforward approach to guide Twitter-driven studies and that adds rigor to health care social media investigations. We provide suggestions for the use of the CCA model in elder care-related contexts.

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          Most cited references44

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          Content Analysis : An Introduction to Its Methodology

          Since the publication of the first edition of Content Analysis: An Introduction to Its Methodology, the textual fabric in which contemporary society functions has undergone a radical transformation -- namely, the ongoing information revolution. Two decades ago, content analysis was largely known in journalism and communication research, and, to a lesser extent, in the social and psychological sciences. Today, content analysis has become an efficient alternative to public opinion research -- a method of tracking markets, political leanings, and emerging ideas, a way to settle legal disputes, and an approach to explore individual human minds. The Second Edition of Content Analysis is a definitive sourcebook of the history and core principles of content analysis as well as an essential resource for present and future studies. The book introduces readers to ways of analyzing meaningful matter such as texts, images, voices -- that is, data whose physical manifestations are secondary to the meanings that a particular population of people brings to them.Organized into three parts, the book examines the conceptual and methodological aspects of content analysis and also traces several paths through content analysis protocols.The author has completely revised and updated the Second Edition, integrating new information on computer-aided text analysis. The book also includes a practical guide that incorporates experiences in teaching and how to advise academic and commercial researchers. In addition, Krippendorff clarifies the epistemology and logic of content analysis as well as the methods for achieving its aims. Author Klaus Krippendorff discusses three distinguishing characteristics of contemporary content analysis: that it is fundamentally empirically grounded, exploratory in process, and predictive or inferential in intent; that it transcends traditional notions of symbols, contents, and intents; and that it has been forced to develop a methodology of its own, one that enables researchers to plan, execute, communicate, reproduce, and critically evaluate an analysis independent of the desirability of its results.Intended as a textbook for advanced undergraduate and graduate students across the social sciences, Content Analysis, Second Edition will also be a valuable resource for practitioners in a variety of disciplines.
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            Qualitative content analysis: a guide to paths not taken.

            D. Morgan (1993)
            Counting codes makes qualitative content analysis a controversial approach to analyzing textual data. Several decades ago, mainstream content analysis rejected qualitative content analysis on the grounds that it was not sufficiently quantitative; today, it is often charged with not being sufficiently qualitative. This article argues that qualitative content analysis is distinctively qualitative in both its approach to coding and its interpretations of counts from codes. Rather than argue over whether to do qualitative content analysis, researchers must make informed decisions about when to use it in analyzing qualitative data.
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              Tracking Suicide Risk Factors Through Twitter in the US

              Background: Suicide is a leading cause of death in the United States. Social media such as Twitter is an emerging surveillance tool that may assist researchers in tracking suicide risk factors in real time. Aims: To identify suicide-related risk factors through Twitter conversations by matching on geographic suicide rates from vital statistics data. Method: At-risk tweets were filtered from the Twitter stream using keywords and phrases created from suicide risk factors. Tweets were grouped by state and departures from expectation were calculated. The values for suicide tweeters were compared against national data of actual suicide rates from the Centers for Disease Control and Prevention. Results: A total of 1,659,274 tweets were analyzed over a 3-month period with 37,717 identified as at-risk for suicide. Midwestern and western states had a higher proportion of suicide-related tweeters than expected, while the reverse was true for southern and eastern states. A strong correlation was observed between state Twitter-derived data and actual state age-adjusted suicide data. Conclusion: Twitter may be a viable tool for real-time monitoring of suicide risk factors on a large scale. This study demonstrates that individuals who are at risk for suicide may be detected through social media.
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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 Inc. (Toronto, Canada )
                1439-4456
                1438-8871
                March 2016
                08 March 2016
                : 18
                : 3
                : e60
                Affiliations
                [1] 1Department of Psychology Faculty of Arts and Humanities King Abdulaziz University JeddahSaudi Arabia
                [2] 2Health and Rehabilitation Sciences Graduate Program Faculty of Health Sciences Western University London, ONCanada
                [3] 3School of Health Studies Western University London, ONCanada
                [4] 4School of Occupational Therapy Western University London, ONCanada
                Author notes
                Corresponding Author: Andrew M Johnson ajohnson@ 123456uwo.ca
                Author information
                http://orcid.org/0000-0002-2683-1166
                http://orcid.org/0000-0002-3911-1854
                http://orcid.org/0000-0002-5744-8338
                http://orcid.org/0000-0002-7306-7957
                http://orcid.org/0000-0002-7269-3016
                Article
                v18i3e60
                10.2196/jmir.5391
                4804105
                26957477
                d97e83a3-c217-49c8-b92a-d8e9d5eaa2af
                ©Eradah O Hamad, Marie Y Savundranayagam, Jeffrey D Holmes, Elizabeth Anne Kinsella, Andrew M Johnson. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 08.03.2016.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), 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
                : 28 November 2015
                : 3 January 2016
                : 30 January 2016
                : 4 February 2016
                Categories
                Original Paper
                Original Paper

                Medicine
                health care social media,twitter feeds,health care tweets,mixed methods research,content analysis,coding,computer-aided content analysis,infodemiology,infoveillance,digital disease detection

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