Importance Twitter has recently been explored as a data source by researchers in multiple fields, though, as a whole, the research remains nascent. Even less is known about using Twitter to study cardiovascular disease. Objective We sought to characterize the volume and content of tweets related to cardiovascular disease, and the characteristics of Twitter users. Design We used the Twitter Application Programming Interface (API) to access a random sample of tweets from July 2009 to February 2015. We filtered approximately 10 billion tweets for keywords related to cardiovascular disease. We included only English tweets originating from US counties. We characterized each tweet relative to estimated user demographics. A random subset of 2,500 tweets was hand-coded for tweet content and modifiers. Setting Twitter, a social media platform Participants Twitter users tweeting about cardiovascular disease Exposures None Main outcomes and measures Our main outcomes included the volume of tweets about cardiovascular disease on Twitter and the content of these tweets. Results Diabetes (n=239,989) and myocardial infarction (269,907) terms were used more frequently than heart failure (9,414) terms. Users tweeting about cardiovascular disease were more likely to be older than the average Twitter population (mean age= 28.68 vs. 25.36, p<0.01), and less likely to be male (47% vs. 49%, p <0.01). Most tweets (94%) were health related. Common themes included tweets related to risk factors (42%), awareness (23%) and management (23%). Conclusions and relevance Twitter offers promise to characterize public understanding and communication about heart disease.

Regional differences in opioid-related topics reflect geographic variation in the content of Twitter discussion about opioids. Analysis of Twitter data also produced topics significantly correlated with opioid overdose death rates. Ongoing analysis of Twitter data could provide a means of identifying emerging trends related to opioids.

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Twitter is one of the largest social networking sites (SNSs) in the world, yet little is known about what cardiovascular health related tweets go viral and what characteristics are associated with retransmission. The current study aims to identify a function of the observable characteristics of cardiovascular tweets, including characteristics of the source, content, and style that predict the retransmission of these tweets. We identified a random sample of 1,251 tweets associated with CVD originating from the United States between 2009 and 2015. Automated coding was conducted on the affect values of the tweets as well as the presence/absence of any URL, mention of another user, question mark, exclamation mark, and hashtag. We hand-coded the tweets’ novelty, utility, theme and source. The count of retweets was positively predicted by message utility, health organization source, and mention of user handle, but negatively predicted by the presence of URL and non-health organization source. Regarding theme, compared to the tweets focusing on risk factor, tweets on treatment and management predicted fewer retweets while supportive tweets predicted more retweets. These findings suggest opportunities for harnessing Twitter to better disseminate cardiovascular educational and supportive information on SNSs.

BackgroundTweets can provide broad, real-time perspectives about health and medical diagnoses that can inform disease surveillance in geographic regions. Less is known, however, about how much individuals post about common health conditions or what they post about.ObjectiveWe sought to collect and analyze tweets from 1 state about high prevalence health conditions and characterize the tweet volume and content.MethodsWe collected 408,296,620 tweets originating in Pennsylvania from 2012-2015 and compared the prevalence of 14 common diseases to the frequency of disease mentions on Twitter. We identified and corrected bias induced due to variance in disease term specificity and used the machine learning approach of differential language analysis to determine the content (words and themes) most highly correlated with each disease.ResultsCommon disease terms were included in 226,802 tweets (174,381 tweets after disease term correction). Posts about breast cancer (39,156/174,381 messages, 22.45%; 306,127/12,702,379 prevalence, 2.41%) and diabetes (40,217/174,381 messages, 23.06%; 2,189,890/12,702,379 prevalence, 17.24%) were overrepresented on Twitter relative to disease prevalence, whereas hypertension (17,245/174,381 messages, 9.89%; 4,614,776/12,702,379 prevalence, 36.33%), chronic obstructive pulmonary disease (1648/174,381 messages, 0.95%; 1,083,627/12,702,379 prevalence, 8.53%), and heart disease (13,669/174,381 messages, 7.84%; 2,461,721/12,702,379 prevalence, 19.38%) were underrepresented. The content of messages also varied by disease. Personal experience messages accounted for 12.88% (578/4487) of prostate cancer tweets and 24.17% (4046/16,742) of asthma tweets. Awareness-themed tweets were more often about breast cancer (9139/39,156 messages, 23.34%) than asthma (1040/16,742 messages, 6.21%). Tweets about risk factors were more often about heart disease (1375/13,669 messages, 10.06%) than lymphoma (105/4927 messages, 2.13%).ConclusionsTwitter provides a window into the Web-based visibility of diseases and how the volume of Web-based content about diseases varies by condition. Further, the potential value in tweets is in the rich content they provide about individuals’ perspectives about diseases (eg, personal experiences, awareness, and risk factors) that are not otherwise easily captured through traditional surveys or administrative data.

Background The quality of care in labor and delivery is traditionally measured through the Hospital Consumer Assessment of Healthcare Providers and Systems but less is known about the experiences of care reported by patients and caregivers on online sites that are more easily accessed by the public. Objective The aim of this study was to generate insight into the labor and delivery experience using hospital reviews on Yelp. Methods We identified all Yelp reviews of US hospitals posted online from May 2005 to March 2017. We used a machine learning tool, latent Dirichlet allocation, to identify 100 topics or themes within these reviews and used Pearson r to identify statistically significant correlations between topics and high (5-star) and low (1-star) ratings. Results A total of 1569 hospitals listed in the American Hospital Association directory had at least one Yelp posting, contributing a total of 41,095 Yelp reviews. Among those hospitals, 919 (59%) had at least one Yelp rating for labor and delivery services (median of 9 reviews), contributing a total of 6523 labor and delivery reviews. Reviews concentrated among 5-star (n=2643, 41%) and 1-star reviews (n=1934, 30%). Themes strongly associated with favorable ratings included the following: top-notch care (r=0.45, P<.001), describing staff as comforting (r=0.52, P<.001), the delivery experience (r=0.46, P<.001), modern and clean facilities (r=0.44, P<.001), and hospital food (r=0.38, P<.001). Themes strongly correlated with 1-star labor and delivery reviews included complaints to management (r=0.30, P<.001), a lack of agency among patients (r=0.47, P<.001), and issues with discharging from the hospital (r=0.32, P<.001). Conclusions Online review content about labor and delivery can provide meaningful information about patient satisfaction and experiences. Narratives from these reviews that are not otherwise captured in traditional surveys can direct efforts to improve the experience of obstetrical care.

Background: Tweets can provide broad, real time perspectives about health and medical diagnoses that can inform disease surveillance in geographic regions. Less is known however about how much individuals post about common health conditions or what they post about. Objective:We sought to collect and analyze tweets from one state about high prevalence health conditions and characterize tweet volume and content.Methods: We collected 408,296,620 tweets originating in Pennsylvania from 2012-2015 and compared the prevalence of 14 common diseases to the frequency of disease mentions on Twitter. We identified and corrected bias induced due to variance in disease term specificity and used the machine learning approach of differential language analysis to determine the content (words and themes) most highly correlated with each disease.Results: Common disease terms were included in 226,802 tweets. Posts about breast cancer (22.5% messages, 2.4% prevalence) and diabetes (23.1% messages, 17.2% prevalence) were overrepresented on Twitter relative to disease prevalence, while hypertension (9.9% messages, 36.3% prevalence), COPD (0.9% messages, 8.5% prevalence), and heart disease (7.8% messages, 19.4% prevalence) were underrepresented. The content of messages also varied by disease. Personal experience messages accounted for 12% of prostate cancer tweets and 24% of asthma tweets. Awareness themed tweets were more often about breast cancer (23%) than asthma (6%). Tweets about risk factors were more often about heart disease (10%) than lymphoma (2%).Conclusions: Twitter provides a window into the online visibility of diseases and how the volume of online content about diseases varies by condition. Further, the potential value in tweets is in the rich content they provide about individuals' perspective about diseases (e.g. personal experiences, awareness, risk factors) that are not otherwise easily captured through traditional surveys or administrative data.

BACKGROUND The quality of care in labor and delivery is traditionally measured through the Hospital Consumer Assessment of Healthcare Providers and Systems but less is known about the experiences of care reported by patients and caregivers on online sites that are more easily accessed by the public. OBJECTIVE The aim of this study was to generate insight into the labor and delivery experience using hospital reviews on Yelp. METHODS We identified all Yelp reviews of US hospitals posted online from May 2005 to March 2017. We used a machine learning tool, latent Dirichlet allocation, to identify 100 topics or themes within these reviews and used Pearson <i>r</i> to identify statistically significant correlations between topics and high (5-star) and low (1-star) ratings. RESULTS A total of 1569 hospitals listed in the American Hospital Association directory had at least one Yelp posting, contributing a total of 41,095 Yelp reviews. Among those hospitals, 919 (59%) had at least one Yelp rating for labor and delivery services (median of 9 reviews), contributing a total of 6523 labor and delivery reviews. Reviews concentrated among 5-star (n=2643, 41%) and 1-star reviews (n=1934, 30%). Themes strongly associated with favorable ratings included the following: top-notch care (<i>r</i>=0.45, <i>P</i>&lt;.001), describing staff as comforting (<i>r</i>=0.52, <i>P</i>&lt;.001), the delivery experience (<i>r</i>=0.46, <i>P</i>&lt;.001), modern and clean facilities (<i>r</i>=0.44, <i>P</i>&lt;.001), and hospital food (<i>r</i>=0.38, <i>P</i>&lt;.001). Themes strongly correlated with 1-star labor and delivery reviews included complaints to management (<i>r</i>=0.30, <i>P</i>&lt;.001), a lack of agency among patients (<i>r</i>=0.47, <i>P</i>&lt;.001), and issues with discharging from the hospital (<i>r</i>=0.32, <i>P</i>&lt;.001). CONCLUSIONS Online review content about labor and delivery can provide meaningful information about patient satisfaction and experiences. Narratives from these reviews that are not otherwise captured in traditional surveys can direct efforts to improve the experience of obstetrical care.