Trends and Characteristics of #HIVPrevention Tweets Posted Between 2014 and 2019: Retrospective Infodemiology Study.

BACKGROUND: Twitter is becoming an increasingly important avenue for people to seek information about HIV prevention. Tweets about HIV prevention may reflect or influence current norms about the acceptability of different HIV prevention methods. Therefore, it may be useful to empirically investigate trends in the level of attention paid to different HIV prevention topics on Twitter over time. OBJECTIVE: The primary objective of this study was to investigate temporal trends in the frequency of tweets about different HIV prevention topics on Twitter between 2014 and 2019. METHODS: We used the Twitter application programming interface to obtain English-language tweets employing #HIVPrevention between January 1, 2014, and December 31, 2019 (n=69,197, globally). Using iterative qualitative content analysis on samples of tweets, we developed a keyword list to categorize the tweets into 10 prevention topics (eg, condom use, preexposure prophylaxis [PrEP]) and compared the frequency of tweets mentioning each topic over time. We assessed the overall change in the proportions of #HIVPrevention tweets mentioning each prevention topic in 2019 as compared with 2014 using chi-square and Fisher exact tests. We also conducted descriptive analyses to identify the accounts posting the most original tweets, the accounts retweeted most frequently, the most frequently used word pairings, and the spatial distribution of tweets in the United States compared with the number of state-level HIV cases. RESULTS: PrEP (13,895 tweets; 20.08% of all included tweets) and HIV testing (7688, 11.11%) were the most frequently mentioned topics, whereas condom use (2941, 4.25%) and postexposure prophylaxis (PEP; 823, 1.19%) were mentioned relatively less frequently. The proportions of tweets mentioning PrEP (327/2251, 14.53%, in 2014, 5067/12,971, 39.1%, in 2019; P≤.001), HIV testing (208/2251, 9.24%, in 2014, 2193/12,971, 16.91% in 2019; P≤.001), and PEP (25/2251, 1.11%, in 2014, 342/12,971, 2.64%, in 2019; P≤.001) were higher in 2019 compared with 2014, whereas the proportions of tweets mentioning abstinence, condom use, circumcision, harm reduction, and gender inequity were lower in 2019 compared with 2014. The top retweeted accounts were mostly UN-affiliated entities; celebrities and HIV advocates were also represented. Geotagged #HIVPrevention tweets in the United States between 2014 and 2019 (n=514) were positively correlated with the number of state-level HIV cases in 2019 (r=0.81, P≤.01). CONCLUSIONS: Twitter may be a useful source for identifying HIV prevention trends. During our evaluation period (2014-2019), the most frequently mentioned prevention topics were PrEP and HIV testing in tweets using #HIVPrevention. Strategic responses to these tweets that provide information about where to get tested or how to obtain PrEP may be potential approaches to reduce HIV incidence.

Brain Tumor Discussions on Twitter (#BTSM): Social Network Analysis.

BACKGROUND: The Brain Tumor Social Media (#BTSM) Twitter hashtag was founded in February 2012 as a disease-specific hashtag for patients with brain tumor. OBJECTIVE: To understand #BTSM's role as a patient support system, we describe user descriptors, growth, interaction, and content sharing. METHODS: We analyzed all tweets containing #BTSM from 2012 to 2018 using the Symplur Signals platform to obtain data and to describe Symplur-defined user categories, tweet content, and trends in use over time. We created a network plot with all publicly available retweets involving #BTSM in 2018 to visualize key stakeholders and their connections to other users. RESULTS: From 2012 to 2018, 59,764 unique users participated in #BTSM, amassing 298,904 tweets. The yearly volume of #BTSM tweets increased by 264.57% from 16,394 in 2012 to 43,373 in 2018 with #BTSM constantly trending in the top 15 list of disease hashtags, as well the top 15 list of tweet chats. Patient advocates generated the most #BTSM tweets (33.13%), while advocacy groups, caregivers, doctors, and researchers generated 7.01%, 4.63%, 3.86%, and 3.37%, respectively. Physician use, although still low, has increased over time. The 2018 network plot of retweets including #BTSM identifies a number of key stakeholders from the patient advocate, patient organization, and medical researcher domains and reveals the extent of their reach to other users. CONCLUSIONS: From its start in 2012, #BTSM has grown exponentially over time. We believe its growth suggests its potential as a global source of brain tumor information on Twitter for patients, advocates, patient organizations as well as health care professionals and researchers.

Defining facets of social distancing during the COVID-19 pandemic: Twitter analysis.

OBJECTIVES: Using Twitter, we aim to (1) define and quantify the prevalence and evolution of facets of social distancing during the COVID-19 pandemic in the US in a spatiotemporal context and (2) examine amplified tweets among social distancing facets. MATERIALS AND METHODS: We analyzed English and US-based tweets containing "coronavirus" between January 23-March 24, 2020 using the Twitter API. Tweets containing keywords were grouped into six social distancing facets: implementation, purpose, social disruption, adaptation, positive emotions, and negative emotions. RESULTS: A total of 259,529 unique tweets were included in the analyses. Social distancing tweets became more prevalent from late January to March but were not geographically uniform. Early facets of social distancing appeared in Los Angeles, San Francisco, and Seattle: the first cities impacted by the COVID-19 outbreak. Tweets related to the "implementation" and "negative emotions" facets largely dominated in combination with topics of "social disruption" and "adaptation", albeit to lesser degree. Social disruptiveness tweets were most retweeted, and implementation tweets were most favorited. DISCUSSION: Social distancing can be defined by facets that respond to and represent certain events in a pandemic, including travel restrictions and rising case counts. For example, Miami had a low volume of social distancing tweets but grew in March corresponding with the rise of COVID-19 cases. CONCLUSION: The evolution of social distancing facets on Twitter reflects actual events and may signal potential disease hotspots. Our facets can also be used to understand public discourse on social distancing which may inform future public health measures.