Managing HIV During the COVID-19 Pandemic: A Study of Help-Seeking Behaviors on a Social Media Forum.

Although numerous editorials claim the COVID-19 pandemic has disproportionately impacted vulnerable populations, particularly those affected by HIV, these claims have received limited empirical evaluation. We analyzed posts to Reddit's r/HIVAIDS from January 3, 2012 through April 30, 2022 to (a) assess changes in the volume of posts during the pandemic and (b) determine the needs of HIV affected communities. There were cumulatively 100% (95%CI: 75-126) more posts than expected since the US declared a pandemic emergency. The most prevalent themes in these posts were for obtaining an HIV + diagnosis (representing 34% (95%CI:29-40) of all posts), seeking HIV treatment (20%; 95%CI:16-25), finding psychosocial support (16%; 95%CI:12-20), and tracking disease progression (8%; 95%CI:5-11). Discussions about PrEP and PEP were the least common, representing less than 6% of all posts each. Social media has increasingly become an important health resource for vulnerable populations seeking information, advice, and support. Public health organizations should recognize how the lay public uses social media and collaborate with social media companies to ensure that the needs of help-seekers on these platforms are met.

Using Large Language Models to Support Content Analysis: A Case Study of ChatGPT for Adverse Event Detection.

This study explores the potential of using large language models to assist content analysis by conducting a case study to identify adverse events (AEs) in social media posts. The case study compares ChatGPT's performance with human annotators' in detecting AEs associated with delta-8-tetrahydrocannabinol, a cannabis-derived product. Using the identical instructions given to human annotators, ChatGPT closely approximated human results, with a high degree of agreement noted: 94.4% (9436/10,000) for any AE detection (Fleiss κ=0.95) and 99.3% (9931/10,000) for serious AEs (κ=0.96). These findings suggest that ChatGPT has the potential to replicate human annotation accurately and efficiently. The study recognizes possible limitations, including concerns about the generalizability due to ChatGPT's training data, and prompts further research with different models, data sources, and content analysis tasks. The study highlights the promise of large language models for enhancing the efficiency of biomedical research.

News coverage of the E-cigarette, or Vaping, product use Associated Lung Injury (EVALI) outbreak and internet searches for vaping cessation.

BACKGROUND: In the latter half of 2019, an outbreak of pulmonary disease in the USA resulted in 2807 hospitalisations and 68 deaths, as of 18 February 2020. Given the severity of the outbreak, we assessed whether articles during the outbreak era more frequently warned about the dangers of vaping and whether internet searches for vaping cessation increased. METHODS: Using Tobacco Watcher, a media monitoring platform that automatically identifies and categorises news articles from sources across the globe, we obtained all articles that (a) discussed the outbreak and (b) primarily warned about the dangers of vaping. We obtained internet search trends originating from the USA that mentioned 'quit' or 'stop' and 'e cig(s),' 'ecig(s),' 'e-cig(s),' 'e cigarette(s),' 'e-cigarette(s),' 'electronic cigarette(s),' 'vape(s),' 'vaping' or 'vaper(s)' from Google Trends (eg, 'how do I quit vaping?'). All data were obtained from 1 January 2014 to 18 February 2020 and ARIMA models were used with historical trends to forecast the ratio of observed to expected search volumes during the outbreak era. RESULTS: News of the vaping-induced pulmonary disease outbreak was first reported on 25 July 2019 with 195 articles, culminating in 44 512 articles by 18 February 2020. On average, news articles warning about the dangers of vaping were 130% (95% prediction interval (PI): -15 to 417) and searches for vaping cessation were 76% (95% PI: 28 to 182) higher than expected levels for the days during the period when the sources of the outbreak were unknown (25 July to 27 September 2019). News and searches stabilised just after the US Centers for Disease Control and Prevention reported that a primary source of the outbreak was an additive used in marijuana vapes on 27 September 2019. In sum, there were 12 286 articles archived in Tobacco Watcher primarily warning about the dangers of vaping and 1 025 000 cessation searches following the outbreak. CONCLUSION: The vaping-induced pulmonary disease outbreak spawned increased coverage about the dangers of vaping and internet searches for vaping cessation. Resources and strategies that respond to this elevated interest should become a priority among public health leaders.

Facebook Pages, the "Disneyland" Measles Outbreak, and Promotion of Vaccine Refusal as a Civil Right, 2009-2019.

Objectives. To understand changes in how Facebook pages frame vaccine opposition.Methods. We categorized 204 Facebook pages expressing vaccine opposition, extracting public posts through November 20, 2019. We analyzed posts from October 2009 through October 2019 to examine if pages' content was coalescing.Results. Activity in pages promoting vaccine choice as a civil liberty increased in January 2015, April 2016, and January 2019 (t[76] = 11.33 [P < .001]; t[46] = 7.88 [P < .001]; and t[41] = 17.27 [P < .001], respectively). The 2019 increase was strongest in pages mentioning US states (t[41] = 19.06; P < .001). Discussion about vaccine safety decreased (rs[119] = -0.61; P < .001) while discussion about civil liberties increased (rs[119] = 0.33; Py < .001]). Page categories increasingly resembled one another (civil liberties: rs[119] = -0.50 [P < .001]; alternative medicine: rs[84] = -0.77 [P < .001]; conspiracy theories: rs[119] = -0.46 [P < .001]; morality: rs[106] = -0.65 [P < .001]; safety and efficacy: rs[119] = -0.46 [P < .001]).Conclusions. The "Disneyland" measles outbreak drew vaccine opposition into the political mainstream, followed by promotional campaigns conducted in pages framing vaccine refusal as a civil right. Political mobilization in state-focused pages followed in 2019.Public Health Implications. Policymakers should expect increasing attempts to alter state legislation associated with vaccine exemptions, potentially accompanied by fiercer lobbying from specific celebrities.

Adapting and Extending a Typology to Identify Vaccine Misinformation on Twitter.

Objectives. To adapt and extend an existing typology of vaccine misinformation to classify the major topics of discussion across the total vaccine discourse on Twitter.Methods. Using 1.8 million vaccine-relevant tweets compiled from 2014 to 2017, we adapted an existing typology to Twitter data, first in a manual content analysis and then using latent Dirichlet allocation (LDA) topic modeling to extract 100 topics from the data set.Results. Manual annotation identified 22% of the data set as antivaccine, of which safety concerns and conspiracies were the most common themes. Seventeen percent of content was identified as provaccine, with roughly equal proportions of vaccine promotion, criticizing antivaccine beliefs, and vaccine safety and effectiveness. Of the 100 LDA topics, 48 contained provaccine sentiment and 28 contained antivaccine sentiment, with 9 containing both.Conclusions. Our updated typology successfully combines manual annotation with machine-learning methods to estimate the distribution of vaccine arguments, with greater detail on the most distinctive topics of discussion. With this information, communication efforts can be developed to better promote vaccines and avoid amplifying antivaccine rhetoric on Twitter.

#HIV: Alignment of HIV-Related Visual Content on Instagram with Public Health Priorities in the US.

Instagram, with more than 1 billion monthly users, is the go-to social media platform to chronicle one's life via images, but how are people using the platform to present visual content about HIV? We analyzed public Instagram posts containing the hashtag "#HIV" (because they are self-tagged as related to HIV) between January 2017 and July 2018. We described the prevalence of co-occurring hashtags and explored thematic concepts in the images using automated image recognition and topic modeling. Twenty-eight percent of all #HIV posts included hashtags focused on awareness, followed by LGBTQ (24.5%) and living with HIV (17.9%). However, specific strategies were rarely cited, including testing (10.8%), treatment (10.3%), PrEP (6.2%) and condoms (4.1%). Image analyses revealed 44.5% of posts included infographics followed by people (21.3%) thereby humanizing HIV and stigmatized populations and promoting community mobilization. Novel content such as the handwriting image-theme (3.8%) where posters shared their HIV test results appeared. We discuss how this visual content aligns with public health priorities to reduce HIV in the US and the novel, organic messages that public health could help amplify.

The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets.

BACKGROUND: Social distancing is an important component of the response to the COVID-19 pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads and "flattens the curve" so that the medical system is better equipped to treat infected individuals. However, it remains unclear how the public will respond to these policies as the pandemic continues. OBJECTIVE: The aim of this study is to present the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We used public geolocated Twitter data to measure how much users travel in a given week. METHODS: We collected 469,669,925 tweets geotagged in the United States from January 1, 2019, to April 27, 2020. We analyzed the aggregated mobility variance of a total of 3,768,959 Twitter users at the city and state level from the start of the COVID-19 pandemic. RESULTS: We found a large reduction (61.83%) in travel in the United States after the implementation of social distancing policies. However, the variance by state was high, ranging from 38.54% to 76.80%. The eight states that had not issued statewide social distancing orders as of the start of April ranked poorly in terms of travel reduction: Arkansas (45), Iowa (37), Nebraska (35), North Dakota (22), South Carolina (38), South Dakota (46), Oklahoma (50), Utah (14), and Wyoming (53). We are presenting our findings on the internet and will continue to update our analysis during the pandemic. CONCLUSIONS: We observed larger travel reductions in states that were early adopters of social distancing policies and smaller changes in states without such policies. The results were also consistent with those based on other mobility data to a certain extent. Therefore, geolocated tweets are an effective way to track social distancing practices using a public resource, and this tracking may be useful as part of ongoing pandemic response planning.

Quantifying Public Interest in Police Reforms by Mining Internet Search Data Following George Floyd's Death.

BACKGROUND: The death of George Floyd while in police custody has resurfaced serious questions about police conduct that result in the deaths of unarmed persons. OBJECTIVE: Data-driven strategies that identify and prioritize the public's needs may engender a public health response to improve policing. We assessed how internet searches indicative of interest in police reform changed after Mr Floyd's death. METHODS: We monitored daily Google searches (per 10 million total searches) that included the terms "police" and "reform(s)" (eg, "reform the police," "best police reforms," etc) originating from the United States between January 1, 2010, through July 5, 2020. We also monitored searches containing the term "police" with "training," "union(s)," "militarization," or "immunity" as markers of interest in the corresponding reform topics. RESULTS: The 41 days following Mr Floyd's death corresponded with the greatest number of police "reform(s)" searches ever recorded, with 1,350,000 total searches nationally. Searches increased significantly in all 50 states and Washington DC. By reform topic, nationally there were 1,220,000 total searches for "police" and "union(s)"; 820,000 for "training"; 360,000 for "immunity"; and 72,000 for "militarization." In terms of searches for all policy topics by state, 33 states searched the most for "training," 16 for "union(s)," and 2 for "immunity." States typically in the southeast had fewer queries related to any police reform topic than other states. States that had a greater percentage of votes for President Donald Trump during the 2016 election searched more often for police "union(s)" while states favoring Secretary Hillary Clinton searched more for police "training." CONCLUSIONS: The United States is at a historical juncture, with record interest in topics related to police reform with variability in search terms across states. Policy makers can respond to searches by considering the policies their constituencies are searching for online, notably police training and unions. Public health leaders can respond by engaging in the subject of policing and advocating for evidence-based policy reforms.

Characteristics of X (Formerly Twitter) Community Notes Addressing COVID-19 Vaccine Misinformation.

This study evaluated the topics, accuracy, and credibility of X (formerly Twitter) Community Notes addressing COVID-19 vaccination.

Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate.

OBJECTIVES: To understand how Twitter bots and trolls ("bots") promote online health content. METHODS: We compared bots' to average users' rates of vaccine-relevant messages, which we collected online from July 2014 through September 2017. We estimated the likelihood that users were bots, comparing proportions of polarized and antivaccine tweets across user types. We conducted a content analysis of a Twitter hashtag associated with Russian troll activity. RESULTS: Compared with average users, Russian trolls (χ2(1) = 102.0; P < .001), sophisticated bots (χ2(1) = 28.6; P < .001), and "content polluters" (χ2(1) = 7.0; P < .001) tweeted about vaccination at higher rates. Whereas content polluters posted more antivaccine content (χ2(1) = 11.18; P < .001), Russian trolls amplified both sides. Unidentifiable accounts were more polarized (χ2(1) = 12.1; P < .001) and antivaccine (χ2(1) = 35.9; P < .001). Analysis of the Russian troll hashtag showed that its messages were more political and divisive. CONCLUSIONS: Whereas bots that spread malware and unsolicited content disseminated antivaccine messages, Russian trolls promoted discord. Accounts masquerading as legitimate users create false equivalency, eroding public consensus on vaccination. Public Health Implications. Directly confronting vaccine skeptics enables bots to legitimize the vaccine debate. More research is needed to determine how best to combat bot-driven content.

Can a selfie promote public engagement with skin cancer?

Social media may provide new opportunities to promote skin cancer prevention, but research to understand this potential is needed. In April of 2015, Kentucky native Tawny Willoughby (TW) shared a graphic skin cancer selfie on Facebook that subsequently went viral. We examined the volume of comments and shares of her original Facebook post; news volume of skin cancer from Google News; and search volume for skin cancer Google queries. We compared these latter metrics after TWs announcement against expected volumes based on forecasts of historical trends. TWs skin cancer story was picked up by the media on May 11, 2015 after the social media post had been shared approximately 50,000 times. All search queries for skin cancer increased 162% (95% CI 102 to 320) and 155% (95% CI 107 to 353) on May 13th and 14th, when news about TW's skin cancer selfie was at its peak, and remained higher through May 17th. Google searches about skin cancer prevention and tanning were also significantly higher than expected volumes. In practical terms, searches reached near-record levels - i.e., May 13th, 14th and 15th were respectively the 6th, 8th, and 40th most searched days for skin cancer since January 1, 2004 when Google began tracking searches. We conclude that an ordinary person's social media post caught the public's imagination and led to significant increases in public engagement with skin cancer prevention. Digital surveillance methods can rapidly detect these events in near real time, allowing public health practitioners to engage and potentially elevate positive effects.

Vaccine Images on Twitter: Analysis of What Images are Shared.

BACKGROUND: Visual imagery plays a key role in health communication; however, there is little understanding of what aspects of vaccine-related images make them effective communication aids. Twitter, a popular venue for discussions related to vaccination, provides numerous images that are shared with tweets. OBJECTIVE: The objectives of this study were to understand how images are used in vaccine-related tweets and provide guidance with respect to the characteristics of vaccine-related images that correlate with the higher likelihood of being retweeted. METHODS: We collected more than one million vaccine image messages from Twitter and characterized various properties of these images using automated image analytics. We fit a logistic regression model to predict whether or not a vaccine image tweet was retweeted, thus identifying characteristics that correlate with a higher likelihood of being shared. For comparison, we built similar models for the sharing of vaccine news on Facebook and for general image tweets. RESULTS: Most vaccine-related images are duplicates (125,916/237,478; 53.02%) or taken from other sources, not necessarily created by the author of the tweet. Almost half of the images contain embedded text, and many include images of people and syringes. The visual content is highly correlated with a tweet's textual topics. Vaccine image tweets are twice as likely to be shared as nonimage tweets. The sentiment of an image and the objects shown in the image were the predictive factors in determining whether an image was retweeted. CONCLUSIONS: We are the first to study vaccine images on Twitter. Our findings suggest future directions for the study and use of vaccine imagery and may inform communication strategies around vaccination. Furthermore, our study demonstrates an effective study methodology for image analysis.

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

BACKGROUND: Racial 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. OBJECTIVE: The 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. METHODS: We 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. RESULTS: Findings 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. CONCLUSIONS: This 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.

Breast Cancer Screening and Social Media: a Content Analysis of Evidence Use and Guideline Opinions on Twitter.

There is ongoing debate regarding the best mammography screening practices. Twitter has become a powerful tool for disseminating medical news and fostering healthcare conversations; however, little work has been done examining these conversations in the context of how users are sharing evidence and discussing current guidelines for breast cancer screening. To characterize the Twitter conversation on mammography and assess the quality of evidence used as well as opinions regarding current screening guidelines, individual tweets using mammography-related hashtags were prospectively pulled from Twitter from 5 November 2015 to 11 December 2015. Content analysis was performed on the tweets by abstracting data related to user demographics, content, evidence use, and guideline opinions. Standard descriptive statistics were used to summarize the results. Comparisons were made by demographics, tweet type (testable claim, advice, personal experience, etc.), and user type (non-healthcare, physician, cancer specialist, etc.). The primary outcomes were how users are tweeting about breast cancer screening, the quality of evidence they are using, and their opinions regarding guidelines. The most frequent user type of the 1345 tweets was "non-healthcare" with 323 tweets (32.5%). Physicians had 1.87 times higher odds (95% CI, 0.69-5.07) of providing explicit support with a reference and 11.70 times higher odds (95% CI, 3.41-40.13) of posting a tweet likely to be supported by the scientific community compared to non-healthcare users. Only 2.9% of guideline tweets approved of the guidelines while 14.6% claimed to be confused by them. Non-healthcare users comprise a significant proportion of participants in mammography conversations, with tweets often containing claims that are false, not explicitly backed by scientific evidence, and in favor of alternative "natural" breast cancer prevention and treatment. Furthermore, users appear to have low approval and confusion regarding screening guidelines. These findings suggest that more efforts are needed to educate and disseminate accurate information to the general public regarding breast cancer prevention modalities, emphasizing the safety of mammography and the harms of replacing conventional prevention and treatment modalities with unsubstantiated alternatives.

The Charlie Sheen Effect on Rapid In-home Human Immunodeficiency Virus Test Sales.

One in eight of the 1.2 million Americans living with human immunodeficiency virus (HIV) are unaware of their positive status, and untested individuals are responsible for most new infections. As a result, testing is the most cost-effective HIV prevention strategy and must be accelerated when opportunities are presented. Web searches for HIV spiked around actor Charlie Sheen's HIV-positive disclosure. However, it is unknown whether Sheen's disclosure impacted offline behaviors like HIV testing. The goal of this study was to determine if Sheen's HIV disclosure was a record-setting HIV prevention event and determine if Web searches presage increases in testing allowing for rapid detection and reaction in the future. Sales of OraQuick rapid in-home HIV test kits in the USA were monitored weekly from April 12, 2014, to April 16, 2016, alongside Web searches including the terms "test," "tests," or "testing" and "HIV" as accessed from Google Trends. Changes in OraQuick sales around Sheen's disclosure and prediction models using Web searches were assessed. OraQuick sales rose 95% (95% CI, 75-117; p < 0.001) of the week of Sheen's disclosure and remained elevated for 4 more weeks (p < 0.05). In total, there were 8225 more sales than expected around Sheen's disclosure, surpassing World AIDS Day by a factor of about 7. Moreover, Web searches mirrored OraQuick sales trends (r = 0.79), demonstrating their ability to presage increases in testing. The "Charlie Sheen effect" represents an important opportunity for a public health response, and in the future, Web searches can be used to detect and act on more opportunities to foster prevention behaviors.

Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance.

We present a machine learning-based methodology capable of providing real-time ("nowcast") and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system. Our main contribution consists of combining multiple influenza-like illnesses (ILI) activity estimates, generated independently with each data source, into a single prediction of ILI utilizing machine learning ensemble approaches. Our methodology exploits the information in each data source and produces accurate weekly ILI predictions for up to four weeks ahead of the release of CDC's ILI reports. We evaluate the predictive ability of our ensemble approach during the 2013-2014 (retrospective) and 2014-2015 (live) flu seasons for each of the four weekly time horizons. Our ensemble approach demonstrates several advantages: (1) our ensemble method's predictions outperform every prediction using each data source independently, (2) our methodology can produce predictions one week ahead of GFT's real-time estimates with comparable accuracy, and (3) our two and three week forecast estimates have comparable accuracy to real-time predictions using an autoregressive model. Moreover, our results show that considerable insight is gained from incorporating disparate data streams, in the form of social media and crowd sourced data, into influenza predictions in all time horizons.

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge.

BACKGROUND: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. METHODS: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). RESULTS: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. CONCLUSION: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts.