Dimensions of Misinformation About the HPV Vaccine on Instagram: Content and Network Analysis of Social Media Characteristics

Philip M Massey, Matthew D Kearney, Michael K Hauer, Preethi Selvan, Emmanuel Koku, Amy E Leader, Philip M Massey, Matthew D Kearney, Michael K Hauer, Preethi Selvan, Emmanuel Koku, Amy E Leader

Abstract

Background: The human papillomavirus (HPV) vaccine is a major advancement in cancer prevention and this primary prevention tool has the potential to reduce and eliminate HPV-associated cancers; however, the safety and efficacy of vaccines in general and the HPV vaccine specifically have come under attack, particularly through the spread of misinformation on social media. The popular social media platform Instagram represents a significant source of exposure to health (mis)information; 1 in 3 US adults use Instagram.

Objective: The objective of this analysis was to characterize pro- and anti-HPV vaccine networks on Instagram, and to describe misinformation within the anti-HPV vaccine network.

Methods: From April 2018 to December 2018, we collected publicly available English-language Instagram posts containing hashtags #HPV, #HPVVaccine, or #Gardasil using Netlytic software (n=16,607). We randomly selected 10% of the sample and content analyzed relevant posts (n=580) for text, image, and social media features as well as holistic attributes (eg, sentiments, personal stories). Among antivaccine posts, we organized elements of misinformation within four broad dimensions: 1) misinformation theoretical domains, 2) vaccine debate topics, 3) evidence base, and 4) health beliefs. We conducted univariate, bivariate, and network analyses on the subsample of posts to quantify the role and position of individual posts in the network.

Results: Compared to provaccine posts (324/580, 55.9%), antivaccine posts (256/580, 44.1%) were more likely to originate from individuals (64.1% antivaccine vs 25.0% provaccine; P<.001) and include personal narratives (37.1% vs 25.6%; P=.003). In the antivaccine network, core misinformation characteristics included mentioning #Gardasil, purporting to reveal a lie (ie, concealment), conspiracy theories, unsubstantiated claims, and risk of vaccine injury. Information/resource posts clustered around misinformation domains including falsification, nanopublications, and vaccine-preventable disease, whereas personal narrative posts clustered around different domains of misinformation, including concealment, injury, and conspiracy theories. The most liked post (6634 likes) in our full subsample was a positive personal narrative post, created by a non-health individual; the most liked post (5604 likes) in our antivaccine subsample was an informational post created by a health individual.

Conclusions: Identifying characteristics of misinformation related to HPV vaccine on social media will inform targeted interventions (eg, network opinion leaders) and help sow corrective information and stories tailored to different falsehoods.

Keywords: HPV, human papillomavirus; cancer; health communication; public health; social media; vaccination.

Conflict of interest statement

Conflicts of Interest: None declared.

©Philip M Massey, Matthew D Kearney, Michael K Hauer, Preethi Selvan, Emmanuel Koku, Amy E Leader. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.12.2020.

Figures

Figure 1
Figure 1
Two-mode visualization (n=580 posts; neutral posts excluded). Includes image, caption, and social media characteristics. Variables colored by type of characteristic. Sized by likes (mean=145.8; median=21; maximum=6634). Top two posts with the most likes are indicated. Symbol shapes represent post source. Color represents node type. Rim color indicates post context. Yellow = social media features. Light blue = image characteristics. Dark blue = caption text characteristics. Red = antivaccine. Green = provaccine. Black rim = personal narrative. White rim = information/resource. Circle = general group. Square = general individual. Triangle = health group. Diamond = health individual.
Figure 2
Figure 2
Antivaccine network visualization (n=256 posts). Variables colored by type of characteristic. Sized by likes (mean=220.9; median=27; maximum=5604). Top three posts with the most likes are indicated. Includes image, type of misinformation, and social media characteristics. Symbol shapes represent post source. Color represents node type. Yellow = social media features. Light blue = image characteristics. Dark blue = type of misinformation. Black = personal narrative. White = information/resource. Circle = general group. Square = general individual. Triangle = health group. Diamond = health individual.

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