Social distancing has been one of the primary mitigation strategies in the United States to control the spread of novel coronavirus disease (COVID-19) and can be viewed as a multi-faceted public health measure. Using Twitter data, 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 the most amplified tweets among social distancing facets. We analyzed a total of 259,529 unique tweets containing "coronavirus" from 115,485 unique users between January 23, 2020 and March 24, 2020 that were identified by the Twitter API as English and U.S.-based. Tweets containing specified keywords (determined a priori) were grouped into six social distancing facets: implementation, purpose, social disruption, adaptation, positive emotions, and negative emotions. Tweets about social disruptiveness were most retweeted, and implementation tweets were most favorited. Social distancing tweets became overall more prevalent in the U.
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.
Village health workers (VHWs) serve as an integral health resource for many resource limited nations, including the Kingdom of Bhutan. As such, we aimed to identify community perceptions as well as utilization rates and types with relation to VHWs based on the urban-rural divide. Our team conducted a randomized survey of 429 community members in 14 villages within the Western region of Bhutan. Our findings indicate VHWs in rural communities are requested for their services twice as much as their urban counterparts. More specifically, urban VHWs are utilized 2.5 times more for general community services, while rural VHWs are utilized more for accessing medications. Additionally, our research indicates a need to increase training of VHWs as well as overall program promotion relating to the specific services that VHWs can provide. These investigations indicate the importance of differentially allocating resources, programming, and training based on the urban-rural divide.
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.
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