People's perceptions about health risks, including their risk of acquiring HIV, are impacted in part by who they see portrayed as at risk in the media. Viewers in these cases are asking themselves "do those portrayed as at risk look like me?" An accurate perception of risk is critical for high-risk populations, who already suffer from a range of health disparities. Yet, to date no study has evaluated the demographic representation of health-related content from social media. The objective of this case study was to apply automated image recognition software to examine the demographic profile of faces in Instagram posts containing the hashtag #HIV (obtained from January 2017 through July 2018) and compare this to the demographic breakdown of those most at risk of a new HIV diagnosis (estimates of incidence of new HIV diagnoses from the 2017 US Centers for Disease Control HIV Surveillance Report). We discovered 26,766 Instagram posts containing #HIV authored in American English with 10,036 (37.5%) containing a detectable human face with a total of 18,227 faces (mean = 1.8, standard deviation [SD] = 1.7). Faces skewed older (47% vs. 11% were 35-39 years old), more female (41% vs. 19%), more white (43% vs. 26%), less black (31% vs 44%), and less Hispanic (13% vs 25%) on Instagram than for new HIV diagnoses. The results were similarly skewed among the subset of #HIV posts mentioning pre-exposure prophylaxis (PrEP). This disparity might lead Instagram users to potentially misjudge their own HIV risk and delay prophylactic behaviors. Social media managers and organic advocates should be encouraged to share images that better reflect at-risk populations so as not to further marginalize these populations and to reduce disparity in risk perception. Replication of our methods for additional diseases, such as cancer, is warranted to discover and address other misrepresentations.

We investigated geographic concentration in elevated risk for a range of postdisaster trajectories of chronic posttraumatic stress symptom (PTSS) and depression symptoms in a longitudinal study (N = 561) of a Hurricane Ike affected population in Galveston and Chambers counties, TX. Using an unadjusted spatial scan statistic, we detected clusters of elevated risk of PTSS trajectories, but not depression trajectories, on Galveston Island. We then tested for predictors of membership in each trajectory of PTSS and depression (e.g., demographic variables, trauma exposure, social support), not taking the geographic nature of the data into account. After adjusting for significant predictors in the spatial scan statistic, we noted that spatial clusters of PTSS persisted and additional clusters of depression trajectories emerged. This is the first study to show that longitudinal trajectories of postdisaster mental health problems may vary depending on the geographic location and the individual- and community-level factors present at these locations. Such knowledge is crucial to identifying vulnerable regions and populations within them, to provide guidance for early responders, and to mitigate mental health consequences through early detection of mental health needs in the population. As human-made disasters increase, our approach may be useful also in other regions in comparable settings worldwide.

INTRODUCTION:Cervical cancer prevention is possible through use of the HPV vaccine and Pap tests, yet the vaccine remains underutilized. METHODS:We obtained publicly-available Twitter data from 2014 using three sampling strategies (top-ranked, simple random sample, and topic model) based on key words related to cervical cancer prevention. We conducted a content analysis of 100 tweets from each of the three samples and examined the extent to which the narratives and frequency of themes differed across samples. RESULTS:Advocacy-related tweets constituted the most prevalent theme to emerge across all three sample types, and were most frequently found in the top-ranked sample. A random sample detected the same themes as topic modeling, but the relative frequency of themes identified from topic modeling fell in-between top-ranked and random samples. DISCUSSION:Variations in themes uncovered by different sampling methods suggest it is useful to qualitatively assess the relative frequency of themes to better understand the breadth and depth of social media conversations about health. CONCLUSIONS:Future studies using social media data should consider sampling methods to uncover a wider breadth of conversations about health on social media.