Online Discussion on #KidneyStones: A Longitudinal Assessment of Activity, Users and Content.
ABSTRACT: INTRODUCTION:Twitter is a popular microblogging platform for the rapid dissemination of information and reciprocal exchange in the urological field. We aimed to assess the activity, users and content of the online discussion, #KidneyStones, on Twitter. METHODS:We investigated the Symplur Signals analytics tool for Twitter data distributed via the #KidneyStones hashtag over a one year period. Activity analysis reflected overall activity and tweet enhancements. We assessed users' geolocations and performed an influencer analysis. Content analysis included the most frequently used words, tweet sentiment and shares for top tweets. RESULTS:3,426 users generated over 10,333 tweets, which were frequently accompanied by links (49%), mentions (30%) and photos (13%). Users came from 106 countries across the globe and were most frequently from North America (63%) and Europe (16%). Individual and organisational healthcare professionals made up 56% of the influencers of the Twitter discussion on #KidneyStones. Besides the words 'kidney' (used 4,045 times) and 'stones' (3,335), 'pain' (1,233), 'urine' (1,158), and 'risk' (1,023) were the most frequently used words. 56% of tweets had a positive sentiment. The median (range) number of shares was 85 (62-587) for the top 10 links, 45.5 (17-94) for the top 10 photos, and 44 (22-95) for the top 10 retweets. CONCLUSION:The rapidly growing Twitter discussion on #KidneyStones engaged multiple stakeholders in the healthcare sector on a global scale and reached both professionals and laypeople. When used effectively and responsibly, the Twitter platform could improve prevention and medical care of kidney stone patients.
Project description:BACKGROUND:There are documented differences in access to health care across the United States. Previous research indicates that Web-based data regarding patient experiences and opinions of health care are available from Twitter. Sentiment analyses of Twitter data can be used to examine differences in patient views of health care across the United States. OBJECTIVE:The objective of our study was to provide a characterization of patient experience sentiments across the United States on Twitter over a 4-year period. METHODS:Using data from Twitter, we developed a set of 4 software components to automatically label and examine a database of tweets discussing patient experience. The set includes a classifier to determine patient experience tweets, a geolocation inference engine for social data, a modified sentiment classifier, and an engine to determine if the tweet is from a metropolitan or nonmetropolitan area in the United States. Using the information retrieved, we conducted spatial and temporal examinations of tweet sentiments at national and regional levels. We examined trends in the time of the day and that of the week when tweets were posted. Statistical analyses were conducted to determine if any differences existed between the discussions of patient experience in metropolitan and nonmetropolitan areas. RESULTS:We collected 27.3 million tweets between February 1, 2013 and February 28, 2017, using a set of patient experience-related keywords; the classifier was able to identify 2,759,257 tweets labeled as patient experience. We identified the approximate location of 31.76% (876,384/2,759,257) patient experience tweets using a geolocation classifier to conduct spatial analyses. At the national level, we observed 27.83% (243,903/876,384) positive patient experience tweets, 36.22% (317,445/876,384) neutral patient experience tweets, and 35.95% (315,036/876,384) negative patient experience tweets. There were slight differences in tweet sentiments across all regions of the United States during the 4-year study period. We found the average sentiment polarity shifted toward less negative over the study period across all the regions of the United States. We observed the sentiment of tweets to have a lower negative fraction during daytime hours, whereas the sentiment of tweets posted between 8 pm and 10 am had a higher negative fraction. Nationally, sentiment scores for tweets in metropolitan areas were found to be more extremely negative and mildly positive compared with tweets in nonmetropolitan areas. This result is statistically significant (P<.001). Tweets with extremely negative sentiments had a medium effect size (d=0.34) at the national level. CONCLUSIONS:This study presents methodologies for a deeper understanding of Web-based discussion related to patient experience across space and time and demonstrates how Twitter can provide a unique and unsolicited perspective from users on the health care they receive in the United States.
Project description:<h4>Objective</h4>To examine public and media response to the draft (October 2011) and finalised (May 2012) recommendations of the United States Preventive Services Task Force (USPSTF) against prostate-specific antigen (PSA) testing via Twitter, a popular social network with over 200 million active users.<h4>Materials and methods</h4>We used a mixed-methods design to analyse posts on Twitter, known as 'tweets'. Using the search term 'prostate cancer', we archived tweets in the 24-h periods following the release of both the draft and the finalised USPSTF recommendations. We recorded tweet rate per h and developed a coding system to assess the type of user and sentiment expressed in tweets and linked articles.<h4>Results</h4>After the draft and finalised USPSTF recommendations were released, 2042 and 5357 tweets focused on the USPSTF report, respectively. The tweet rate nearly doubled within 2 h of both announcements. Fewer than 10% of tweets expressed an opinion about screening, and the majority of these were pro-screening during both periods. By contrast, anti-screening articles were tweeted more frequently in both the draft and finalised study periods. Between the draft and the finalised recommendations, the proportion of anti-screening tweets and anti-screening article links increased (P = 0.03 and P < 0.01, respectively).<h4>Conclusions</h4>There was increased Twitter activity surrounding the USPSTF draft and finalised recommendations. The percentage of anti-screening tweets and articles appeared to increase, perhaps due to the interval public comment period. Despite this, most tweets did not express an opinion, suggesting a missed opportunity in this important arena for advocacy.
Project description:Two code files and one dataset related to Olympic Twitter activity are the foundation for this article. Through Twitter's Spritzer streaming API (Application Programming Interface), we collected over 430 million tweets from May 12th, 2016 to September 12th, 2016 windowing the Rio de Janeiro Olympics and Paralympics. We cleaned and filtered these tweets to contain Olympic-related content. We then analyzed the raw data of 21,218,652 tweets including location data, language, and tweet content to distill the sentiment and emotions of Twitter users pertaining to the Olympic Games Kassens-Noor E. et al., 2019. We generalized the original data set to comply with the Twitter's Terms of Service and Developer agreement, 2018. We present the modified dataset and accompanying code files in this article to suggest using both for further analysis on sentiment and emotions related to the Rio de Janeiro Olympics and for comparative research on imagery and perceptions of other Olympic Games.
Project description:BACKGROUND:Infodemiology is an emerging field of research that utilizes user-generated health-related content, such as that found in social media, to help improve public health. Twitter has become an important venue for studying emerging patterns in health issues such as substance use because it can reflect trends in real-time and display messages generated directly by users, giving a uniquely personal voice to analyses. Over the past year, several states in the United States have passed legislation to legalize adult recreational use of cannabis and the federal government in Canada has done the same. There are few studies that examine the sentiment and content of tweets about cannabis since the recent legislative changes regarding cannabis have occurred in North America. OBJECTIVE:To examine differences in the sentiment and content of cannabis-related tweets by state cannabis laws, and to examine differences in sentiment between the United States and Canada between 2017 and 2019. METHODS:In total, 1,200,127 cannabis-related tweets were collected from January 1, 2017, to June 17, 2019, using the Twitter application programming interface. Tweets then were grouped geographically based on cannabis legal status (legal for adult recreational use, legal for medical use, and no legal use) in the locations from which the tweets came. Sentiment scoring for the tweets was done with VADER (Valence Aware Dictionary and sEntiment Reasoner), and differences in sentiment for states with different cannabis laws were tested using Tukey adjusted two-sided pairwise comparisons. Topic analysis to determine the content of tweets was done using latent Dirichlet allocation in Python, using a Java implementation, LdaMallet, with Gensim wrapper. RESULTS:Significant differences were seen in tweet sentiment between US states with different cannabis laws (P=.001 for negative sentiment tweets in fully illegal compared to legal for adult recreational use states), as well as between the United States and Canada (P=.003 for positive sentiment and P=.001 for negative sentiment). In both cases, restrictive state policy environments (eg, those where cannabis use is fully illegal, or legal for medical use only) were associated with more negative tweet sentiment than less restrictive policy environments (eg, where cannabis is legal for adult recreational use). Six key topics were found in recent US tweet contents: fun and recreation (keywords, eg, love, life, high); daily life (today, start, live); transactions (buy, sell, money); places of use (room, car, house); medical use and cannabis industry (business, industry, company); and legalization (legalize, police, tax). The keywords representing content of tweets also differed between the United States and Canada. CONCLUSIONS:Knowledge about how cannabis is being discussed online, and geographic differences that exist in these conversations may help to inform public health planning and prevention efforts. Public health education about how to use cannabis in ways that promote safety and minimize harms may be especially important in places where cannabis is legal for adult recreational and medical use.
Project description:To investigate factors associated with engagement of U.S. Federal Health Agencies via Twitter. Our specific goals are to study factors related to a) numbers of retweets, b) time between the agency tweet and first retweet and c) time between the agency tweet and last retweet.We collect 164,104 tweets from 25 Federal Health Agencies and their 130 accounts. We use negative binomial hurdle regression models and Cox proportional hazards models to explore the influence of 26 factors on agency engagement. Account features include network centrality, tweet count, numbers of friends, followers, and favorites. Tweet features include age, the use of hashtags, user-mentions, URLs, sentiment measured using Sentistrength, and tweet content represented by fifteen semantic groups.A third of the tweets (53,556) had zero retweets. Less than 1% (613) had more than 100 retweets (mean ?=?284). The hurdle analysis shows that hashtags, URLs and user-mentions are positively associated with retweets; sentiment has no association with retweets; and tweet count has a negative association with retweets. Almost all semantic groups, except for geographic areas, occupations and organizations, are positively associated with retweeting. The survival analyses indicate that engagement is positively associated with tweet age and the follower count.Some of the factors associated with higher levels of Twitter engagement cannot be changed by the agencies, but others can be modified (e.g., use of hashtags, URLs). Our findings provide the background for future controlled experiments to increase public health engagement via Twitter.
Project description:BACKGROUND:With restrictions on movement and stay-at-home orders in place due to the COVID-19 pandemic, social media platforms such as Twitter have become an outlet for users to express their concerns, opinions, and feelings about the pandemic. Individuals, health agencies, and governments are using Twitter to communicate about COVID-19. OBJECTIVE:The aims of this study were to examine key themes and topics of English-language COVID-19-related tweets posted by individuals and to explore the trends and variations in how the COVID-19-related tweets, key topics, and associated sentiments changed over a period of time from before to after the disease was declared a pandemic. METHODS:Building on the emergent stream of studies examining COVID-19-related tweets in English, we performed a temporal assessment covering the time period from January 1 to May 9, 2020, and examined variations in tweet topics and sentiment scores to uncover key trends. Combining data from two publicly available COVID-19 tweet data sets with those obtained in our own search, we compiled a data set of 13.9 million English-language COVID-19-related tweets posted by individuals. We use guided latent Dirichlet allocation (LDA) to infer themes and topics underlying the tweets, and we used VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis to compute sentiment scores and examine weekly trends for 17 weeks. RESULTS:Topic modeling yielded 26 topics, which were grouped into 10 broader themes underlying the COVID-19-related tweets. Of the 13,937,906 examined tweets, 2,858,316 (20.51%) were about the impact of COVID-19 on the economy and markets, followed by spread and growth in cases (2,154,065, 15.45%), treatment and recovery (1,831,339, 13.14%), impact on the health care sector (1,588,499, 11.40%), and governments response (1,559,591, 11.19%). Average compound sentiment scores were found to be negative throughout the examined time period for the topics of spread and growth of cases, symptoms, racism, source of the outbreak, and political impact of COVID-19. In contrast, we saw a reversal of sentiments from negative to positive for prevention, impact on the economy and markets, government response, impact on the health care industry, and treatment and recovery. CONCLUSIONS:Identification of dominant themes, topics, sentiments, and changing trends in tweets about the COVID-19 pandemic can help governments, health care agencies, and policy makers frame appropriate responses to prevent and control the spread of the pandemic.
Project description:Use of data generated through social media for health studies is gradually increasing. Twitter is a short-text message system developed 6 years ago, now with more than 100 million users generating over 300 million Tweets every day. Twitter may be used to gain real-world insights to promote healthy behaviors. The purposes of this paper are to describe a practical approach to analyzing Tweet contents and to illustrate an application of the approach to the topic of physical activity. The approach includes five steps: (1) selecting keywords to gather an initial set of Tweets to analyze; (2) importing data; (3) preparing data; (4) analyzing data (topic, sentiment, and ecologic context); and (5) interpreting data. The steps are implemented using tools that are publically available and free of charge and designed for use by researchers with limited programming skills. Content mining of Tweets can contribute to addressing challenges in health behavior research.
Project description:Background:Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the Centers for Disease Control and Prevention activated its Emergency Operations Center and the World Health Organization released its first situation report about coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment evolved in the early stages of the COVID-19 pandemic has not been described. Methods:We extracted tweets matching hashtags related to COVID-19 from January 14 to 28, 2020 using Twitter's application programming interface. We measured themes and frequency of keywords related to infection prevention practices. We performed a sentiment analysis to identify the sentiment polarity and predominant emotions in tweets and conducted topic modeling to identify and explore discussion topics over time. We compared sentiment, emotion, and topics among the most popular tweets, defined by the number of retweets. Results:We evaluated 126 049 tweets from 53 196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Approximately half (49.5%) of all tweets expressed fear and approximately 30% expressed surprise. In the full cohort, the economic and political impact of COVID-19 was the most commonly discussed topic. When focusing on the most retweeted tweets, the incidence of fear decreased and topics focused on quarantine efforts, the outbreak and its transmission, as well as prevention. Conclusions:Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and potentially target individualized public health messages based on user interest and emotion.
Project description:BACKGROUND:Nowadays, the use of social media is part of daily life, with more and more people, including governments and health organizations, using at least one platform regularly. Social media enables users to interact among large groups of people that share the same interests and suffer the same afflictions. Notably, these channels promote the ability to find and share information about health and medical conditions. OBJECTIVE:This study aimed to characterize the bowel disease (BD) community on Twitter, in particular how patients understand, discuss, feel, and react to the condition. The main questions were as follows: Which are the main communities and most influential users?; Where are the main content providers from?; What are the key biomedical and scientific topics under discussion? How are topics interrelated in patient communications?; How do external events influence user activity?; What kind of external sources of information are being promoted? METHODS:To answer these questions, a dataset of tweets containing terms related to BD conditions was collected from February to August 2018, accounting for a total of 24,634 tweets from 13,295 different users. Tweet preprocessing entailed the extraction of textual contents, hyperlinks, hashtags, time, location, and user information. Missing and incomplete information about the user profiles was completed using different analysis techniques. Semantic tweet topic analysis was supported by a lexicon-based entity recognizer. Furthermore, sentiment analysis enabled a closer look into the opinions expressed in the tweets, namely, gaining a deeper understanding of patients' feelings and experiences. RESULTS:Health organizations received most of the communication, whereas BD patients and experts in bowel conditions and nutrition were among those tweeting the most. In general, the BD community was mainly discussing symptoms, BD-related diseases, and diet-based treatments. Diarrhea and constipation were the most commonly mentioned symptoms, and cancer, anxiety disorder, depression, and chronic inflammations were frequently part of BD-related tweets. Most patient tweets discussed the bad side of BD conditions and other related conditions, namely, depression, diarrhea, and fibromyalgia. In turn, gluten-free diets and probiotic supplements were often mentioned in patient tweets expressing positive emotions. However, for the most part, tweets containing mentions to foods and diets showed a similar distribution of negative and positive sentiments because the effects of certain food components (eg, fiber, iron, and magnesium) were perceived differently, depending on the state of the disease and other personal conditions of the patients. The benefits of medical cannabis for the treatment of different chronic diseases were also highlighted. CONCLUSIONS:This study evidences that Twitter is becoming an influential space for conversation about bowel conditions, namely, patient opinions about associated symptoms and treatments. So, further qualitative and quantitative content analyses hold the potential to support decision making among health-related stakeholders, including the planning of awareness campaigns.
Project description:BACKGROUND:Menthol cigarettes are used disproportionately by African American, female, and adolescent smokers. Twitter is also used disproportionately by minority and younger populations, providing a unique window into conversations reflecting social norms, behavioral intentions, and sentiment toward menthol cigarettes. OBJECTIVE:Our purpose was to identify the content and frequency of conversations about menthol cigarettes, including themes, populations, user smoking status, other tobacco or substances, tweet characteristics, and sentiment. We also examined differences in menthol cigarette sentiment by prevalent categories, which allowed us to assess potential perceptions, misperceptions, and social norms about menthol cigarettes on Twitter. This approach can inform communication about these products, particularly to subgroups who are at risk for menthol cigarette use. METHODS:Through a combination of human and machine classification, we identified 94,627 menthol cigarette-relevant tweets from February 1, 2012 to January 31, 2013 (1 year) from over 47 million tobacco-related messages gathered prospectively from the Twitter Firehose of all public tweets and metadata. Then, 4 human coders evaluated a random sample of 7000 tweets for categories, including sentiment toward menthol cigarettes. RESULTS:We found that 47.98% (3194/6657) of tweets expressed positive sentiment, while 40.26% (2680/6657) were negative toward menthol cigarettes. The majority of tweets by likely smokers (2653/4038, 65.70%) expressed positive sentiment, while 91.2% (320/351) of nonsmokers and 71.7% (91/127) of former smokers indicated negative views. Positive views toward menthol cigarettes were predominant in tweets that discussed addiction or craving, marijuana, smoking, taste or sensation, song lyrics, and tobacco industry or marketing or tweets that were commercial in nature. Negative views toward menthol were more common in tweets about smoking cessation, health, African Americans, women, and children and adolescents-largely due to expression of negative stereotypes associated with these groups' use of menthol cigarettes. CONCLUSIONS:Examinations of public opinions toward menthol cigarettes through social media can help to inform the framing of public communication about menthol cigarettes, particularly in light of potential regulation by the European Union, US Food and Drug Administration, other jurisdictions, and localities.