ABSTRACT: 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: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:OBJECTIVES:To compare information sharing of over 379 health conditions on Twitter to uncover trends and patterns of online user activities. METHODS:We collected 1.5 million tweets generated by over 450,000 Twitter users for 379 health conditions, each of which was quantified using a multivariate model describing engagement, user and content aspects of the data and compared using correlation and network analysis to discover patterns of user activities in these online communities. RESULTS:We found a significant imbalance in terms of the size of communities interested in different health conditions, regardless of the seriousness of these conditions. Improving the informativeness of tweets by using, for example, URLs, multimedia and mentions can be important factors in promoting health conditions on Twitter. Using hashtags on the contrary is less effective. Social network analysis revealed similar structures of the discussion found across different health conditions. CONCLUSIONS:Our study found variance in activity between different health communities on Twitter, and our results are likely to be of interest to public health authorities and officials interested in the potential of Twitter to raise awareness of public health.
Project description:Background:The use of Twitter hashtags at medical conferences has revolutionized the way healthcare professionals interact and advance their education. We aim to investigate the scope of the Academic Surgical Congress's online reach and engagement through the use of Twitter hashtags #ASC from 2015 to 2019, by analyzing the number of impressions and tweets and retweets. Methods:A cross sectional study of Twitter data through Symplur with the following conference hashtags for the Academic Surgical Congress annual meetings for years 2015-2019: #ASC2015, #ASC2016, #ASC2017, #ASC2018, and #ASC2019. Data on tweets, retweets, users, and impressions was reviewed along with information on the top 10 influencers and the most frequently tweeted links. Symplur Signals software was utilized to extract and assimilate data. Statistical Significance was defined as p < 0.05. Results:Twitter engagement metrics significantly increased from 11,400 to 32,100 from 2015 to 2017 (p < 0.05). However, from 2017 to 2019, there was a significant decline in engagement metrics from 32,100 to 26,100 (p < 0.05). Impressions increased significantly from 13,100 in 2015 to 71,800 impressions in 2019 (p < 0.05). Users grew significantly from 1500 in 2015 to peak at 4600 in 2017 before dropping back to 3300 in 2019 (p < 0.05). The most influential organizations during these years were the organizers of the conference: Association for Academic Surgery and the Society of University Surgeons. Conference attendance progressively increased from approximately 1700 in 2016 to about 2100 in 2019 (p < 0.05). Conclusions:Twitter engagement metrics at the Academic Surgical Congress 2015-2019 has fluctuated, while impressions significantly increased through the years indicating the consistent dissemination of conference content.
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:We examine the relationship between social structure and sentiment through the analysis of a large collection of tweets about the Irish Marriage Referendum of 2015. We obtain the sentiment of every tweet with the hashtags #marref and #marriageref that was posted in the days leading to the referendum, and construct networks to aggregate sentiment and use it to study the interactions among users. Our analysis shows that the sentiment of outgoing mention tweets is correlated with the sentiment of incoming mentions, and there are significantly more connections between users with similar sentiment scores than among users with opposite scores in the mention and follower networks. We combine the community structure of the follower and mention networks with the activity level of the users and sentiment scores to find groups that support voting 'yes' or 'no' in the referendum. There were numerous conversations between users on opposing sides of the debate in the absence of follower connections, which suggests that there were efforts by some users to establish dialogue and debate across ideological divisions. Our analysis shows that social structure can be integrated successfully with sentiment to analyse and understand the disposition of social media users around controversial or polarizing issues. These results have potential applications in the integration of data and metadata to study opinion dynamics, public opinion modelling and polling.
Project description:We examined openly shared substance-related tweets to estimate prevalent sentiment around substance use and identify popular substance use activities. Additionally, we investigated associations between substance-related tweets and business characteristics and demographics at the zip code level.A total of 79,848,992 tweets were collected from 48 states in the continental United States from April 2015-March 2016 through the Twitter API, of which 688,757 were identified as being related to substance use. We implemented a machine learning algorithm (maximum entropy text classifier) to estimate sentiment score for each tweet. Zip code level summaries of substance use tweets were created and merged with the 2013 Zip Code Business Patterns and 2010 US Census Data.Quality control analyses with a random subset of tweets yielded excellent agreement rates between computer generated and manually generated labels: 97%, 88%, 86%, 75% for underage engagement in substance use, alcohol, drug, and smoking tweets, respectively. Overall, 34.1% of all substance-related tweets were classified as happy. Alcohol was the most frequently tweeted substance, followed by marijuana. Regression results suggested more convenience stores in a zip code were associated with higher percentages of tweets about alcohol. Larger zip code population size and higher percentages of African Americans and Hispanics were associated with fewer tweets about substance use and underage engagement. Zip code economic disadvantage was associated with fewer alcohol tweets but more drug tweets.The patterns in substance use mentions on Twitter differ by zip code economic and demographic characteristics. Online discussions have great potential to glorify and normalize risky behaviors. Health promotion and underage substance prevention efforts may include interactive social media campaigns to counter the social modeling of risky behaviors.
Project description:BACKGROUND:The coronavirus disease (COVID-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions, governments, and individuals navigate the pandemic. OBJECTIVE:The aim of this study is to analyze discussions on Twitter related to COVID-19 and to investigate the sentiments toward COVID-19. METHODS:This study applied machine learning methods in the field of artificial intelligence to analyze data collected from Twitter. Using tweets originating exclusively in the United States and written in English during the 1-month period from March 20 to April 19, 2020, the study examined COVID-19-related discussions. Social network and sentiment analyses were also conducted to determine the social network of dominant topics and whether the tweets expressed positive, neutral, or negative sentiments. Geographic analysis of the tweets was also conducted. RESULTS:There were a total of 14,180,603 likes, 863,411 replies, 3,087,812 retweets, and 641,381 mentions in tweets during the study timeframe. Out of 902,138 tweets analyzed, sentiment analysis classified 434,254 (48.2%) tweets as having a positive sentiment, 187,042 (20.7%) as neutral, and 280,842 (31.1%) as negative. The study identified 5 dominant themes among COVID-19-related tweets: health care environment, emotional support, business economy, social change, and psychological stress. Alaska, Wyoming, New Mexico, Pennsylvania, and Florida were the states expressing the most negative sentiment while Vermont, North Dakota, Utah, Colorado, Tennessee, and North Carolina conveyed the most positive sentiment. CONCLUSIONS:This study identified 5 prevalent themes of COVID-19 discussion with sentiments ranging from positive to negative. These themes and sentiments can clarify the public's response to COVID-19 and help officials navigate the pandemic.
Project description:Since 2014, the Society of Critical Care Medicine has encouraged "live-tweeting" through the use of specific hashtags at each annual Critical Care Congress. We describe how the digital footprint of the Society of Critical Care Medicine Congress on Twitter has evolved at a time when social media use at conferences is becoming increasingly popular. Design:We used Symplur Signals (Symplur LLC, Pasadena, CA) to track all tweets containing the Society of Critical Care Medicine Congress hashtag for each annual meeting between 2014 and 2020. We collected data on the number of tweets, tweet characteristics, and impressions (i.e., potential views) for each year and data on the characteristics of the top 100 most actively tweeting users of that Congress. Setting:Twitter. Subjects:Users tweeting with the Critical Care Congress hashtag. Interventions:Not applicable. Measurements and Main Results:The Critical Care Congress digital footprint grew substantially from 2014 to 2020. The 2014 Critical Care Congress included 1,629 tweets by 266 users, compared with 29,657 tweets by 3,551 participants in 2020; average hourly tweets increased from 9.7 to 177. The percentage of tweets with mentions of other users and tweets with visual media increased. Users attending the conference were significantly more likely to compose original tweets, whereas those tweeting from afar were more likely to retweet Critical Care Congress content. There was a yearly increase in content-specific hashtags used in conjunction with Critical Care Congress hashtags (n = 429 in 2014 to n = 22,272 in 2020), most commonly related to pediatrics (18% of all hashtags), mobility/rehab (9%), sepsis (7%) social media (6%), and ICU burnout (1%). Conclusions:There has been significant growth in live-tweeting at the Critical Care Congress, along with the increased use of content-specific hashtags and visual media. This digital footprint is largely driven by a proportion of highly engaged users. As medical conferences transition to completely or partially online platforms, understanding of the digital footprint is crucial for success.
Project description:People with lung cancer and others affected by the condition are using social media to share information and support, but little is known about how these behaviours vary between different platforms. To investigate this, we extracted posts from Twitter (using relevant hashtags), the Lung Cancer Support Group on Facebook and the Macmillan.org.uk lung cancer discussion forum for a single month. Interaction Process Analysis revealed that all three platforms were used more for giving than seeking information, opinion or suggestions. However, interaction types (including sentiment) varied between platforms, reflecting their digital architectures, user-base and inclusion of a moderator. For example, a higher percentage of information-seeking and sentiment marked the Macmillan.org.uk, compared with Twitter and the Facebook Group. Further analysis of the messages using a four-dimensional typology of social support revealed that emotional and informational support types were most prevalent on the Macmillan.org.uk forum, closely followed by the Facebook Group. Contrary to expectations, Twitter posts showed the most companionship support, reflecting the use of hashtags as user-generated signals of community belonging and interests. Qualitative analysis revealed an unanticipated sub-category of spiritual support, which featured uniquely in the Lung Cancer Support Group on Facebook. There was little evidence of trolling or stigma, although some users remarked that lung cancer was unfairly resourced compared with other cancers. These findings provide new insights about how people affected by lung cancer use social media and begin to elucidate the value of different platforms as channels for patient engagement and support, or as potential research data sources.
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.