Literature DB >> 31105910

Who is Saying What on Twitter: An Analysis of Messages with References to HIV and HIV Risk Behavior.

Sophie Lohmann1, Ismini Lourentzou2, Chengxiang Zhai2, Dolores Albarracín1.   

Abstract

This research aimed to determine the nature of social media discussions about HIV. With the goal of conducting a descriptive analysis, we collected almost 1,000 tweets posted February to September 2015. The sample of tweets included keywords related to HIV or behavioral risk factors (e.g., sex, drug use) and was coded for content (e.g., HIV), behavior change strategies, and message source. Seven percent of tweets concerned HIV/AIDS, which were often referred to as jokes or insults. The majority of tweets coded as behavior change attempts involved attitude change strategies. The majority of the tweets (80%) came from private users (vs. organizations). Different types of sources employed different types of behavior change strategies: For instance, private users, compared to experts or organizations, included more strategies to decrease detrimental attitudes (29% versus 6%, p < .001), and also more strategies to counter myths and misinformation (6% versus 1%, p = .008). In summary, tweets related to HIV/AIDS and associated risk factors frequently use the terms in jokes and insults, come largely from private users, and entail attitudinal and informational strategies. Online health campaigns with clear calls to action and corrections of misinformation may make important contributions to social media conversations about HIV/AIDS.

Entities:  

Keywords:  Acquired Immunodeficiency Syndrome; HIV; VIH; actitud; attitude; behavior change; cambio de conducta; communication; comunicación; infección de transmisión sexual; medios sociales; sexually transmitted infections; social media; síndrome de inmunodeficiencia adquirida

Year:  2018        PMID: 31105910      PMCID: PMC6524990          DOI: 10.22201/fpsi.20074719e.2018.1.09

Source DB:  PubMed          Journal:  Acta Investig Psicol        ISSN: 2007-4719


  9 in total

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2.  A test of major assumptions about behavior change: a comprehensive look at the effects of passive and active HIV-prevention interventions since the beginning of the epidemic.

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Journal:  Psychol Bull       Date:  2005-11       Impact factor: 17.737

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Journal:  Psychol Bull       Date:  2006-03       Impact factor: 17.737

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Authors:  Molly E Ireland; H Andrew Schwartz; Qijia Chen; Lyle H Ungar; Dolores Albarracín
Journal:  Health Psychol       Date:  2015-12       Impact factor: 4.267

5.  Action Tweets Linked to Reduced County-Level HIV Prevalence in the United States: Online Messages and Structural Determinants.

Authors:  Molly E Ireland; Qijia Chen; H Andrew Schwartz; Lyle H Ungar; Dolores Albarracin
Journal:  AIDS Behav       Date:  2016-06

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Authors:  Dolores Albarracin; Laura R Glasman
Journal:  Health Psychol Rev       Date:  2016-09

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Authors:  Sean D Young; Caitlin Rivers; Bryan Lewis
Journal:  Prev Med       Date:  2014-02-08       Impact factor: 4.018

Review 8.  Contribution of sexually transmitted infections to the sexual transmission of HIV.

Authors:  Helen Ward; Minttu Rönn
Journal:  Curr Opin HIV AIDS       Date:  2010-07       Impact factor: 4.283

9.  Tweet content related to sexually transmitted diseases: no joking matter.

Authors:  Elia Gabarron; J Artur Serrano; Rolf Wynn; Annie Y S Lau
Journal:  J Med Internet Res       Date:  2014-10-06       Impact factor: 5.428

  9 in total
  2 in total

1.  Identifying HIV-related digital social influencers using an iterative deep learning approach.

Authors:  Cheng Zheng; Wei Wang; Sean D Young
Journal:  AIDS       Date:  2021-05-01       Impact factor: 4.632

Review 2.  [Sexual health information on social media: a systematic scoping review].

Authors:  Nicola Döring; Melisa Conde
Journal:  Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz       Date:  2021-10-14       Impact factor: 1.513

  2 in total

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