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      Researching Mental Health Disorders in the Era of Social Media: Systematic Review

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          Abstract

          Background

          Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose.

          Objective

          The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research.

          Methods

          We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals.

          Results

          The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis.

          Conclusions

          Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques.

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          Most cited references95

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          Experimental evidence of massive-scale emotional contagion through social networks.

          Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others' positive experiences constitutes a positive experience for people.
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            Sentiment strength detection in short informal text

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              Tastes, ties, and time: A new social network dataset using Facebook.com

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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
                June 2017
                29 June 2017
                : 19
                : 6
                : e228
                Affiliations
                [1] 1 Department of Informatics King's College London London United Kingdom
                [2] 2 Primary Care and Public Health Sciences King’s College London London United Kingdom
                [3] 3 Departamento de Psicología Básica Universidad Autónoma de Madrid Madrid Spain
                Author notes
                Corresponding Author: Akkapon Wongkoblap akkapon.wongkoblap@ 123456kcl.ac.uk
                Author information
                http://orcid.org/0000-0002-9355-1981
                http://orcid.org/0000-0001-8421-816X
                http://orcid.org/0000-0002-8308-2886
                Article
                v19i6e228
                10.2196/jmir.7215
                5509952
                28663166
                ede9b165-ada4-4f2f-b1f9-a2b8377a97eb
                ©Akkapon Wongkoblap, Miguel A Vadillo, Vasa Curcin. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 29.06.2017.

                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
                : 22 December 2016
                : 6 February 2017
                : 14 March 2017
                : 27 April 2017
                Categories
                Review
                Review

                Medicine
                mental health,mental disorders,social networking,artificial intelligence,machine learning,public health informatics,depression,anxiety,infodemiology

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