Pre-exposure Prophylaxis (PrEP) Information on Instagram: Content Analysis

Abstract

Background There is still an HIV epidemic in the United States, which is a substantial issue for populations bearing a disproportionate burden of HIV infections. Daily oral pre-exposure prophylaxis (PrEP) has proven to be safe and effective in reducing HIV acquisition risk. However, studies document that PrEP awareness/usage is low. There is also limited understanding of social media platforms, such as Instagram, as PrEP information sources. Objective Given the paucity of research on PrEP-related Instagram posts and popularity of this social media platform, the purpose of this research is to describe the source characteristics, image types, and textual contents of PrEP-related posts on Instagram. Methods Using Crowdtangle Search, a public insights tool owned/operated by Facebook, we retrieved publicly accessible and English-language-only Instagram posts for the 12-month period preceding April 22, 2020, using the following terms: Truvada or “pre-exposure prophylaxis” or #truvada or #truvadaprep or #truvadawhore or #truvadaforprep. We employed a qualitative coding methodology to manually extract information from posts. Using a pretested codebook, we performed content analysis on 250 posts, examining message and source characteristics (ie, organization type [eg, government, news] and individual type [eg, physician]), including information about PrEP (eg, how it works, cost), and indicated users. Frequencies and percentages were calculated for all categorical variables. A Chi-square test was conducted to determine differences between source types on a variety of message characteristics. Results Three-quarters of the posts (193/250, 77.2%) were posted by organizations. Of the 250 posts reviewed, approximately two-thirds (174/250, 69.6%) included a photograph, more than half (142/250, 56.8%) included an infographic, and approximately one-tenth (30/250, 12%) included a video. More than half defined PrEP (137/250, 54.8%), but fewer posts promoted PrEP use, explained how PrEP works, and included information on the effectiveness of PrEP or who can use it. The most commonly hashtagged populations among posts were men who have sex with men (MSM), but not necessarily bisexual men. Few posts contained race-/ethnicity-related hashtags (11/250, 4.4%). Fewer posts contained transgender-associated tags (eg, #transgirl; 5/250, 2%). No posts contained tags related to heterosexuals or injection drug users. We found statistical differences between source types (ie, individual versus organization). Specifically, posts from organizations more frequently contained information about who can use PrEP, whereas posts from individuals more frequently contained information describing adverse effects. Conclusions This study is among the first to review Instagram for PrEP-related content, and it answers the National AIDS Strategy’s call for a clearer articulation of the science surrounding HIV risk/prevention through better understanding of the current public information environment. This study offers a snapshot of how PrEP is being discussed (and by whom) on one of the most popular social media platforms and provides a foundation for developing and implementing PrEP promotion interventions on Instagram.

HIV Pre-Exposure Prophylaxis (PrEP) among men who have sex with men: peer communication, engagement and social networks

“…Only one of the 48 interviewees found out about PrEP through publicity strategies implemented by health institutions and seven received informa-tion from specialist health services when they began PEP. In addition, there was no mention of government social media campaigns addressing sexual health and HIV prevention, corroborating the findings in the literature 25 .…”

“…The findings show that social media influencers are more engaged in sharing and disseminating information than traditional institutions. Indeed, HIV prevention content on social media has drawn increasing interest from researchers precisely because of the power this medium has to reach a wide range of different population groups 13,25,26 . However, it is not clear whether the considerable increase in the content shared by peers, in other words, people who do not formally represent traditional health institutions, is a direct effect of government neglect of this problem and general dismantling of health policies or a result of mass migration to virtual media in increasingly well-organized hyperconnected networks 27 .…”

A Profilaxia Pré-Exposição ao HIV (PrEP) entre homens que fazem sexo com homens: comunicação, engajamento e redes sociais de pares

“…Dos 48 entrevistados, somente um soube da PrEP por meio de estratégia de divulgação oficial de instituição de saúde e sete tiveram contato com informações em idas aos serviços de saúde especializados para iniciar o protocolo de PEP. Adicionalmente, não houve qualquer menção a campanhas virtuais realizadas por instituições governamentais em quaisquer temas relacionados à saúde sexual e prevenção à infecção pelo HIV, corroborando com achados da literatura 25 .…”

“…Observamos maior participação de influenciadores digitais no compartilhamento e disseminação de informações que levam ao uso de PrEP em oposição à ausência das instituições tradicionais. De fato, os conteúdos de prevenção ao HIV veiculados por meio das plataformas digitais tem sido cada vez mais estudados justamente pelo poder de alcance em diferentes populações 13,25,26 . Porém, não se sabe ao certo se o expressivo aumento de conteúdos produzidos por pares, ou seja, por pessoas que não representam formalmente as instituições tradicionais de saúde seria o efeito direto do descaso e do desmonte das políticas de saúde como um todo, ou se é o re-sultado da migração para o âmbito social virtual cada vez mais organizado em redes hiper conectadas 27 .…”

COVID-19 Mask Usage and Social Distancing in Social Media Images: Large-scale Deep Learning Analysis

“…Realizing the potential of social media in understanding such events, in this study, we used social media images from Instagram, a popular image-sharing social media platform, which has been used by researchers to study different public health emergencies [ 24 ]. Computer vision–based classifiers are necessary to check if a person is wearing a mask from an image.…”