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      #Antivaccination on Instagram: A Computational Analysis of Hashtag Activism through Photos and Public Responses

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          Abstract

          A dramatic increase has been registered in the number of social media posts in photo form as well as in hashtag activism. Hashtags, which manifest thoughts and feelings clearly and concisely, originated on Twitter, where the length of a post is limited; their use, however, has expanded into other social media services, including Instagram. Hashtags, which make it easy to find and express support for posts of interest, have been widely used for online activism, although they have been criticized for fostering confirmation bias. Moreover, hashtag activism in photo form has been relatively understudied. This research analyzed Instagram photos with antivaccination hashtags as an example of hashtag activism through photos. In addition, we examined how the photo features were related to public response, which was manifested via engagement and comment sentiment. The results suggest that the photos which were categorized into “text” took the largest share. It was also found that the major way of claiming was to imprint key messages that persuade people not to vaccinate with remarks from professionals on photos and provide a source of supporting information in the post text with hashtags of antivaccine intention. Various photo features showed associations with engagement and comment sentiment, but the directions of correlation were usually the opposite: these results suggest that engagement and comment sentiment are separate domains that reveal different public responses.

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          The OpenCV library

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            Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate

            Objectives. To understand how Twitter bots and trolls (“bots”) promote online health content. Methods. We compared bots’ to average users’ rates of vaccine-relevant messages, which we collected online from July 2014 through September 2017. We estimated the likelihood that users were bots, comparing proportions of polarized and antivaccine tweets across user types. We conducted a content analysis of a Twitter hashtag associated with Russian troll activity. Results. Compared with average users, Russian trolls (χ 2 (1) = 102.0; P  < .001), sophisticated bots (χ 2 (1) = 28.6; P  < .001), and “content polluters” (χ 2 (1) = 7.0; P  < .001) tweeted about vaccination at higher rates. Whereas content polluters posted more antivaccine content (χ 2 (1) = 11.18; P  < .001), Russian trolls amplified both sides. Unidentifiable accounts were more polarized (χ 2 (1) = 12.1; P  < .001) and antivaccine (χ 2 (1) = 35.9; P  < .001). Analysis of the Russian troll hashtag showed that its messages were more political and divisive. Conclusions. Whereas bots that spread malware and unsolicited content disseminated antivaccine messages, Russian trolls promoted discord. Accounts masquerading as legitimate users create false equivalency, eroding public consensus on vaccination. Public Health Implications. Directly confronting vaccine skeptics enables bots to legitimize the vaccine debate. More research is needed to determine how best to combat bot-driven content.
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              The online competition between pro- and anti-vaccination views

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                Author and article information

                Journal
                Int J Environ Res Public Health
                Int J Environ Res Public Health
                ijerph
                International Journal of Environmental Research and Public Health
                MDPI
                1661-7827
                1660-4601
                17 October 2020
                October 2020
                : 17
                : 20
                : 7550
                Affiliations
                [1 ]Division of Media Communication, Hankuk University of Foreign Studies, Seoul 02450, Korea; yunhwankim2@ 123456hufs.ac.kr
                [2 ]Business School, Hankuk University of Foreign Studies, Seoul 02450, Korea; 201501686@ 123456hufs.ac.kr
                [3 ]Division of Applied Mathematical Sciences, Korea University, Seoul 30019, Korea
                Author notes
                [* ]Correspondence: leeyeonju08@ 123456korea.ac.kr ; Tel.: +82-44-860-1311
                Author information
                https://orcid.org/0000-0003-1138-6802
                Article
                ijerph-17-07550
                10.3390/ijerph17207550
                7589874
                33080782
                ab6e8b2a-34fa-48a0-b170-1b4592df723f
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 14 September 2020
                : 15 October 2020
                Categories
                Article

                Public health
                antivaccination,instagram,hashtag activism,microsoft azure cognitive services,engagement,comment sentiment

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