Total Number Of Tweets Research Articles

Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation.

BackgroundThe Centers for Disease Control and Prevention (CDC) tracks influenza-like illness (ILI) using information on patient visits to health care providers through the Outpatient Influenza-like Illness Surveillance Network (ILINet). As participation in this system is voluntary, the composition, coverage, and consistency of health care reports vary from state to state, leading to different measures of ILI activity between regions. The degree to which these measures reflect actual differences in influenza activity or systematic differences in the methods used to collect and aggregate the data is unclear.ObjectiveThe objective of our study was to qualitatively and quantitatively compare national and region-specific ILI activity in the United States across 4 surveillance data sources—CDC ILINet, Flu Near You (FNY), athenahealth, and HealthTweets.org—to determine whether these data sources, commonly used as input in influenza modeling efforts, show geographical patterns that are similar to those observed in CDC ILINet’s data. We also compared the yearly percentage of FNY participants who sought health care for ILI symptoms across geographical areas.MethodsWe compared the national and regional 2018-2019 ILI activity baselines, calculated using noninfluenza weeks from previous years, for each surveillance data source. We also compared measures of ILI activity across geographical areas during 3 influenza seasons, 2015-2016, 2016-2017, and 2017-2018. Geographical differences in weekly ILI activity within each data source were also assessed using relative mean differences and time series heatmaps. National and regional age-adjusted health care–seeking percentages were calculated for each influenza season by dividing the number of FNY participants who sought medical care for ILI symptoms by the total number of ILI reports within an influenza season. Pearson correlations were used to assess the association between the health care–seeking percentages and baselines for each surveillance data source.ResultsWe observed consistent differences in ILI activity across geographical areas for CDC ILINet and athenahealth data. ILI activity for FNY displayed little variation across geographical areas, whereas differences in ILI activity for HealthTweets.org were associated with the total number of tweets within a geographical area. The percentage of FNY participants who sought health care for ILI symptoms differed slightly across geographical areas, and these percentages were positively correlated with CDC ILINet and athenahealth baselines.ConclusionsOur findings suggest that differences in ILI activity across geographical areas as reported by a given surveillance system may not accurately reflect true differences in the prevalence of ILI. Instead, these differences may reflect systematic collection and aggregation biases that are particular to each system and consistent across influenza seasons. These findings are potentially relevant in the real-time analysis of the influenza season and in the definition of unbiased forecast models.

Stroke Survivors on Twitter: Sentiment and Topic Analysis From a Gender Perspective.

BackgroundStroke is the worldwide leading cause of long-term disabilities. Women experience more activity limitations, worse health-related quality of life, and more poststroke depression than men. Twitter is increasingly used by individuals to broadcast their day-to-day happenings, providing unobtrusive access to samples of spontaneously expressed opinions on all types of topics and emotions.ObjectiveThis study aimed to consider the raw frequencies of words in the collection of tweets posted by a sample of stroke survivors and to compare the posts by gender of the survivor for 8 basic emotions (anger, fear, anticipation, surprise, joy, sadness, trust and disgust); determine the proportion of each emotion in the collection of tweets and statistically compare each of them by gender of the survivor; extract the main topics (represented as sets of words) that occur in the collection of tweets, relative to each gender; and assign happiness scores to tweets and topics (using a well-established tool) and compare them by gender of the survivor.MethodsWe performed sentiment analysis based on a state-of-the-art lexicon (National Research Council) with syuzhet R package. The emotion scores for men and women were first subjected to an F-test and then to a Wilcoxon rank sum test. We extended the emotional analysis, assigning happiness scores with the hedonometer (a tool specifically designed considering Twitter inputs). We calculated daily happiness average scores for all tweets. We created a term map for an exploratory clustering analysis using VosViewer software. We performed structural topic modelling with stm R package, allowing us to identify main topics by gender. We assigned happiness scores to all the words defining the main identified topics and compared them by gender.ResultsWe analyzed 800,424 tweets posted from August 1, 2007 to December 1, 2018, by 479 stroke survivors: Women (n=244) posted 396,898 tweets, and men (n=235) posted 403,526 tweets. The stroke survivor condition and gender as well as membership in at least 3 stroke-specific Twitter lists of active users were manually verified for all 479 participants. Their total number of tweets since 2007 was 5,257,433; therefore, we analyzed the most recent 15.2% of all their tweets. Positive emotions (anticipation, trust, and joy) were significantly higher (P<.001) in women, while negative emotions (disgust, fear, and sadness) were significantly higher (P<.001) in men in the analysis of raw frequencies and proportion of emotions. Happiness mean scores throughout the considered period show higher levels of happiness in women. We calculated the top 20 topics (with percentages and CIs) more likely addressed by gender and found that women’s topics show higher levels of happiness scores.ConclusionsWe applied two different approaches—the Plutchik model and hedonometer tool—to a sample of stroke survivors’ tweets. We conclude that women express positive emotions and happiness much more than men.

871: It’s time for maternal-fetal medicine researchers to Tweet

Twitter is the most popular social media among the scientists for the purpose of work-related contents, following scientific discussions and exchanging scientific ideas by widely sharing a research result in just a few short sentences (nature.com). We sought to examine the popularity of such practice among the Maternal-Fetal Medicine researchers. All the first authors of the published abstracts in the abstract book of the 38th Annual Pregnancy Meeting of the Society for Maternal-Fetal Medicine (2018 SMFM) were evaluated for activity on Twitter and the total number of Tweets. The same evaluation was done for the top 10 ranked overall scientific journals and obstetrics and gynecology journals, separately using Scientific Journal Ranking (SJR) system accessible at www.scimagojr.com/journalrank.php . Descriptive statistics are reported as median [range] and n (%). Total of 1014 abstracts were presented by 681 first authors. Median number of abstracts per each first author was 1 [1-8]. 10/10 (100%) of the overall top 10 scientific journals were active on Twitter by sharing research related materials. This rate was 7/10 (70%) for top Obstetrics and Gynecology journals. Interestingly, only 3.7% of presenters in the 38th Annual Pregnancy Meeting (2018 SMFM) were using Twitter (Table). Our study shows that although scientific societies and journals are commonly using social media as an effective means for developing ideas, initiating collaborations, disseminating scientific contents and outreach (Figure), Twitter is not yet popular as expected among Maternal-Fetal Medicine researchers. Twitter is also extremely useful in conference settings to keep up-to-date on what is happening at a moment in the scientific community.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Tweets or nighttime lights: Comparison for preeminence in estimating socioeconomic factors

Nighttime lights (NTL) imagery is one of the most commonly used tools to quantitatively study socioeconomic systems over large areas. In this study we aim to use location-based social media big data to challenge the primacy of NTL imagery on estimating socioeconomic factors. Geo-tagged tweets posted in the contiguous United States in 2013 were retrieved to produce a tweet image with the same spatial resolution of the NTL imagery (i.e., 0.00833° × 0.00833°). Sum tweet (the total number of tweets) and sum light (summed DN value of the NTL image) of each state or county were obtained from the tweets and the NTL images, respectively, to estimate three important socioeconomic factors: personal income, electric power consumption, and fossil fuel carbon dioxide emissions. Results show that sum tweet is a better measure of personal income and electric power consumption while carbon dioxide emissions can be more accurately estimated by sum light. We further exploited that African-Americans adults are more likely than White seniors to post geotagged tweets in the US, yet did not find any significant correlations between proportions of the subpopulations and the estimation accuracy of the socioeconomic factors. Existence of saturated pixels and blooming effects and failure to remove gas flaring reduce quality of NTL imagery in estimating socioeconomic factors, however, such problems are nonexistent in the tweet images. This study reveals that the number of geo-tagged tweets has great potential to be deemed as a substitute of brightness of NTL to assess socioeconomic factors over large geographic areas.

Examining the Use of a Social Media Campaign to Increase Engagement for the American Heart Association 2017 Resuscitation Science Symposium.

BackgroundThe Resuscitation Science Symposium (ReSS) is the dedicated international forum for resuscitation science at the American Heart Association's Scientific Sessions. In an attempt to increase curated content and social media presence during ReSS 2017, the Journal of the American Heart Association (JAHA) coordinated an inaugural social media campaign.Methods and ResultsBefore ReSS, 8 resuscitation science professionals were recruited from a convenience sample of attendees at ReSS 2017. Each blogger was assigned to either a morning or an afternoon session, responsible for “live tweeting” with the associated hashtags #ReSS17 and #AHA17. Twitter analytics from the 8 bloggers were collected from November 10 to 13, 2017. The primary outcome was Twitter impressions. Secondary outcomes included Twitter engagement and Twitter engagement rate. In total, 8 bloggers (63% male) generated 591 tweets that garnered 261 050 impressions, 8013 engagements, 928 retweets, 1653 likes, 292 hashtag clicks, and a median engagement rate of 2.4%. Total engagement, likes, and hashtag clicks were highest on day 2; total impressions were highest on day 3, and retweets were highest on day 4. Total impressions were highly correlated with the total number of tweets (r=0.87; P=0.005) and baseline number of Twitter followers for each blogger (r=0.78; P=0.02).ConclusionIn this inaugural social media campaign for the 2017 American Heart Association ReSS, the degree of online engagement with this content by end users was quite good when evaluated by social media standards. Benchmarks for end‐user interactions in the scientific community are undefined and will require further study.

Monitoring Freshman College Experience Through Content Analysis of Tweets: Observational Study.

BackgroundFreshman experiences can greatly influence students’ success. Traditional methods of monitoring the freshman experience, such as conducting surveys, can be resource intensive and time consuming. Social media, such as Twitter, enable users to share their daily experiences. Thus, it may be possible to use Twitter to monitor students’ postsecondary experience.ObjectiveOur objectives were to (1) describe the proportion of content posted on Twitter by college students relating to academic studies, personal health, and social life throughout the semester; and (2) examine whether the proportion of content differed by demographics and during nonexam versus exam periods.MethodsBetween October 5 and December 11, 2015, we collected tweets from 170 freshmen attending the University of California Los Angeles, California, USA, aged 18 to 20 years. We categorized the tweets into topics related to academic, personal health, and social life using keyword searches. Mann-Whitney U and Kruskal-Wallis H tests examined whether the content posted differed by sex, ethnicity, and major. The Friedman test determined whether the total number of tweets and percentage of tweets related to academic studies, personal health, and social life differed between nonexam (weeks 1-8) and final exam (weeks 9 and 10) periods.ResultsParticipants posted 24,421 tweets during the fall semester. Academic-related tweets (n=3433, 14.06%) were the most prevalent during the entire semester, compared with tweets related to personal health (n=2483, 10.17%) and social life (n=1646, 6.74%). The proportion of academic-related tweets increased during final-exam compared with nonexam periods (mean rank 68.9, mean 18%, standard error (SE) 0.1% vs mean rank 80.7, mean 21%, SE 0.2%; Z=–2.1, P=.04). Meanwhile, the proportion of tweets related to social life decreased during final exams compared with nonexam periods (mean rank 70.2, mean 5.4%, SE 0.01% vs mean rank 81.8, mean 7.4%, SE 0.01%; Z=–4.8, P<.001). Women tweeted more often than men during both nonexam (mean rank 95.8 vs 76.8; U=2876, P=.02) and final-exam periods (mean rank 96.2 vs 76.2; U=2832, P=.01). The percentages of academic-related tweets were similar between ethnic groups during nonexam periods (P>.05). However, during the final-exam periods, the percentage of academic tweets was significantly lower among African Americans than whites (χ24=15.1, P=.004). The percentages of tweets related to academic studies, personal health, and social life were not significantly different between areas of study during nonexam and exam periods (P>.05).ConclusionsThe results suggest that the number of tweets related to academic studies and social life fluctuates to reflect real-time events. Student’s ethnicity influenced the proportion of academic-related tweets posted. The findings from this study provide valuable information on the types of information that could be extracted from social media data. This information can be valuable for school administrators and researchers to improve students’ university experience.

Predicting The Cricket Match Outcome Using Crowd Opinions On Social Networks: A Comparative Study Of Machine Learning Methods

Social media has become a platform of first choice where one can express his/her feelings with freedom. The sports and matches being played are also discussed on social media such as Twitter. In this article, efforts are made to investigate the feasibility of using collective knowledge obtained from microposts posted on Twitter to predict the winner of a Cricket match. For predictions, we use three different methods that depend on the total number of tweets before the game for each team, fans sentiments toward each team and fans score predictions on Twitter. By combining these three methods, we classify winning team prediction in a Cricket game before the start of game. Our results are promising enough to be used for winning team forecast. Furthermore, the effectiveness of supervised learning algorithms is evaluated where Support Vector Machine (SVM) has shown advantage over other classifiers.

“Birds of a Feather”: An Institutional Approach to Canadian National Sport Organizations’ Social-Media Use

The purpose of this study was to examine sport organizations’ social-media activity using an institutional approach, specifically, to investigate the main themes emanating from Canadian national sport organizations’ (CNSOs) social-media communication and the similarities and differences in social-media use between the CNSOs. An exploratory qualitative thematic analysis was conducted on 8 CNSOs’ Twitter accounts ranging from 346 to 23,925 followers, with the number of tweets varying from 219 to 17,186. Thematic analysis indicated that CNSOs generally used tweeting for promoting, reporting, and informing purposes. Despite the organizations’ differing characteristics regarding seasonality of the sport, Twitter-follower count, total number of tweets, and whether the content was original or retweeted, themes were generally consistent across the various organizations. Coercive, mimetic, and normative isomorphic pressures help explain these similarities and offer reasons for a lack of followership growth by the less salient CNSOs. Implications for research and practice are provided.

Re-imagining Television Audience Research: Tracing Viewing Patterns on Twitter

Re-imagining Television Audience Research: Tracing Viewing Patterns on Twitter

Metropolises in the Twittersphere: An Informetric Investigation of Informational Flows and Networks

Information flows on social media platforms are able to show trends and user interests as well as connections between users. In this paper, we present a method how to analyze city related networks on the social media platform Twitter based on the user content. Forty million tweets have been downloaded via Twitter’s REST API (application programming interface) and Twitter’s Streaming API. The investigation focuses on two aspects: firstly, trend detection has been done to analyze 31 informational world cities, according the user activity, popularity of shared websites and topics defined by hashtags. Secondly, a hint of how connected informational cities are to each other is given by creating a clustered network based on the number of connections between different city pairs. Tokyo, New York City, London and Paris clearly lead the ranking of the most active cities if compared by the total number of tweets. The investigation shows that Twitter is very frequently used to share content from other services like Instagram or YouTube. The most popular topics in tweets reveal great differences between the cities. In conclusion, the investigation shows that social media services like Twitter also can be a mirror of the society they are used in and bring to light information flows of connected cities in a global network. The presented method can be applied in further research to analyze information flows regarding specific topics and/or geographical locations.