Discordance Between Human Papillomavirus Twitter Images and Disparities in Human Papillomavirus Risk and Disease in the United States: Mixed-Methods Analysis

Abstract

BackgroundRacial and ethnic minorities are disproportionately affected by human papillomavirus (HPV)-related cancer, many of which could have been prevented with vaccination. Yet, the initiation and completion rates of HPV vaccination remain low among these populations. Given the importance of social media platforms for health communication, we examined US-based HPV images on Twitter. We explored inconsistencies between the demographics represented in HPV images and the populations that experience the greatest burden of HPV-related disease.ObjectiveThe objective of our study was to observe whether HPV images on Twitter reflect the actual burden of disease by select demographics and determine to what extent Twitter accounts utilized images that reflect the burden of disease in their health communication messages.MethodsWe identified 456 image tweets about HPV that contained faces posted by US users between November 11, 2014 and August 8, 2016. We identified images containing at least one human face and utilized Face++ software to automatically extract the gender, age, and race of each face. We manually annotated the source accounts of these tweets into 3 types as follows: government (38/298, 12.8%), organizations (161/298, 54.0%), and individual (99/298, 33.2%) and topics (news, health, and other) to examine how images varied by message source.ResultsFindings reflected the racial demographics of the US population but not the disease burden (795/1219, 65.22% white faces; 140/1219, 11.48% black faces; 71/1219, 5.82% Asian faces; and 213/1219, 17.47% racially ambiguous faces). Gender disparities were evident in the image faces; 71.70% (874/1219) represented female faces, whereas only 27.89% (340/1219) represented male faces. Among the 11-26 years age group recommended to receive HPV vaccine, HPV images contained more female-only faces (214/616, 34.3%) than males (37/616, 6.0%); the remainder of images included both male and female faces (365/616, 59.3%). Gender and racial disparities were present across different image sources. Faces from government sources were more likely to depict females (n=44) compared with males (n=16). Of male faces, 80% (12/15) of youth and 100% (1/1) of adults were white. News organization sources depicted high proportions of white faces (28/38, 97% of female youth and 12/12, 100% of adult males). Face++ identified fewer faces compared with manual annotation because of limitations with detecting multiple, small, or blurry faces. Nonetheless, Face++ achieved a high degree of accuracy with respect to gender, race, and age compared with manual annotation.ConclusionsThis study reveals critical differences between the demographics reflected in HPV images and the actual burden of disease. Racial minorities are less likely to appear in HPV images despite higher rates of HPV incidence. Health communication efforts need to represent populations at risk better if we seek to reduce disparities in HPV infection.

Social media use and human papillomavirus awareness and knowledge among adults with children in the household: examining the role of race, ethnicity, and gender

“…12 Men and racial minorities were less likely to be featured on Twitter images, despite having equal or more disease burden compared to women and white individuals. 35 In terms of reaching certain key demographics to disseminate vaccine and cancer prevention promotional messages, social media may be a costeffective strategy to reach both targeted and broad groups of individuals.…”

“…42,43 Messages that are easy to understand incorporating faces and voices of the target audience are more likely to be effective than a one-size-fits-all approach to health communication. 35,44 Lack of timely, relevant information may lead users to seek information from less credible sources, which may contribute to vaccine hesitancy. In addition to online channels, healthcare providers are critical in the multi-level efforts to spread correct HPV information.…”

Automated image analysis of instagram posts: Implications for risk perception and communication in public health using a case study of #HIV

“…We assigned the demographic characteristic with the highest probability (e.g., we assigned 'female' for gender if the model returns a corresponding probability of 70% for female and 30% for male). These methods typically achieve 85% accuracy in face recognition, 88% accuracy in gender recognition, 79% accuracy in racial/ethnic recognition, and 93% accuracy for age estimation when age is grouped [10,[38][39][40]. The automated image recognition analyses can be replicated using the documentation provided by Clarifai [37].…”

“…Instagram users intentionally and unintentionally are defining wellness and health for the larger public [22]. However, the limited public health work using Instagram has largely focused on qualitative methods to examine images [23,24], which do not scale with few exceptions [10][11][12].…”

“…These theories garner substantial attention in the evaluation of traditional media (e.g., print or television) [9]. Despite some work on social media [10][11][12], most analyses are heavily focused on written content in part because early social media was primarily text-based and the most well known analytic strategies rely on text analysis [12][13][14]. The omission of image analyses focused on social normative theories is critically important as social media is now the dominant source of media consumption among the public [15] and the fastest growing social media channels focus on user generated images and/or video such as Instagram and YouTube.…”