Number Of Tweets Research Articles

Conflicting attitudes: Analyzing social media data to understand the early discourse on COVID-19 passports

In several countries, vaccine passports are being encouraged to hasten the return to some form of normalcy amidst the COVID-19 pandemic. Vaccine passport is a digital or paper document that may serve as proof of the COVID-19 vaccine, thereby allowing entry to public venues, sporting events, air travel, and unrestricted access to other facilities. This study explores how the COVID-19 passport is being discussed and perceived on Twitter and the prominent entities involved in the early discourse on the issue. Twitter messages were theoretically analyzed for Health Belief Model (HBM) and Theory of Planned Behavior (TPB) variables, as well as message source, engagement, and attitudes towards vaccination certificates. Using quantitative content analysis, tweets were coded on nine dimensions: account type, tweeter profile, tweet content, tweet modality, attitude, self-efficacy, perceived barriers, benefits, and action cues. Most of the tweets originated from personal accounts, followed by media organizations, media-related personalities, politicians, and the travel industry. A significant number of tweets were from male Twitter users. Our analysis revealed that most tweeters had a favorable attitude towards the COVID-19 passports. Unfavorable attitudes toward the COVID-19 passport were based on reasons such as a lack of common standard or consensus, and personal freedoms & human rights. Tweets highlighting the benefits of COVID-19 passports cited travel as the primary reason. Based on a combination of technical, legal, and ethical practices, our study offers a set of vital recommendations for governments, health organizations, and businesses that may help stimulate the acceptance of vaccine passports.

Sentiment Analysis of Tweets to Gain Insights into the 2016 US Election

Social media use is at an all-time historic high for the United States, so we considered one popular social media platform, Twitter, and tried to see if we could predict how a group of people felt about an issue by only using posts from social media. For our research, we looked at tweets that focused on the 2016 United States presidential election. Using these tweets, we tried to find a correlation between tweet sentiment and the election results. We wrote a program to collect tweets that mentioned one of the two candidates, then sorted the tweets by state and developed a sentiment algorithm to see which candidate the tweet favored, or if it was neutral. After collecting the data from Twitter and comparing it to the results of the Electoral College, we found that Twitter sentiments corresponded with 66.7% of the actual outcome of the Electoral College. The overall sentiment of all tweets collected leaned more positively towards Donald Trump than it did for Hillary Clinton. Using the data that was collected, we also looked at how different geographical locations affected a candidate’s popularity, analyzed what issues were most prevalent in tweets, and looked at the ratio of a state’s population versus the number of tweets gathered.

NLP analysis in social media using big data technologies

For timely and efficient reactions to disasters, collecting vital and right information is essential. In recent decades, social media platforms such as Twitter, Facebook, Linkedin, Instagram have become valuable sources of information in disaster times. However, the reliability, volume, and velocity of information remain a major concern; this is particular about information issued from disaster locations. This paper proposes an approach for tracking the location of people in danger during times of disaster. The procedure is based on the Twitter application programming interface (API) by using natural language processing (NLP) and big data tools. A number of tweets were analyzed and an accuracy of 86.11% was actualized.

Twitter communication of the UK public on dental health and care during a COVID lockdown: "My kingdom for a dentist".

The COVID-19 pandemic forced a UK-wide closure of dental services. An understanding of public concerns about dental care was urgently needed to inform careful resumption of paused dental services. To describe public concerns about dental care during lockdown. Framework analysis of relevant Twitter posts identified collected using the Awario tool. Of 1863 tweets manually screened for eligibility, 285 were relevant, as they contained views expressed by the public. The number of tweets by country were proportionate to the population size. The key views expressed in tweets focused on: 'oral health impact' ('oral health and self-care', 'types of dental problems', 'managing symptoms at home', 'views on consequences of delaying treatment') and 'dental service or care provision' ('views on managing dental care response', 'experiences with access to dental care'). The impact of COVID-19 on dental services raised many physical and mental health concerns for the public, highlighting their importance. Online profiles and social media communication platforms can be used to provide convenient, and timely information on public perceptions of dental care.

Assessment of Antipsychotic Medications on Social Media: Machine Learning Study.

Background: Antipsychotic medications are the first-line treatment for schizophrenia. However, non-adherence is frequent despite its negative impact on the course of the illness. In response, we aimed to investigate social media posts about antipsychotics to better understand the online environment in this regard.Methods: We collected tweets containing mentions of antipsychotic medications posted between January 1st 2019 and October 31st 2020. The content of each tweet and the characteristics of the users were analyzed as well as the number of retweets and likes generated.Results: Twitter users, especially those identified as patients, showed an interest in antipsychotic medications, mainly focusing on the topics of sexual dysfunction and sedation. Interestingly, paliperidone, despite being among one of the newest antipsychotics, accounted for a low number of tweets and did not generate much interest. Conversely, retweet and like ratios were higher in those tweets asking for or offering help, in those posted by institutions and in those mentioning cognitive complaints. Moreover, health professionals did not have a strong presence in tweet postings, nor did medical institutions. Finally, trivialization was frequently observed.Conclusion: This analysis of tweets about antipsychotic medications provides insights into experiences and opinions related to this treatment. Twitter user perspectives therefore constitute a valuable input that may help to improve clinicians' knowledge of antipsychotic medications and their communication with patients regarding this treatment.

Going rogue: what scientists can learn about Twitter communication from "alt" government accounts.

The inauguration of President Trump in the United States led to the active restriction of science communication from federal agencies, resulting in the creation of many unofficial “alt” Twitter accounts to maintain communication. Alt accounts had many followers (e.g., 15 accounts had > 100,000) and received a large amount of media attention, making them ideal for better understanding how differences in messaging can affect public engagement with science on microblogging platforms. We analyzed tweets produced by alt and corresponding official agency accounts to compare the two groups and determine if specific features of a tweet made them more likely to be retweeted or liked to help the average scientist potentially reach a broader audience on Twitter. We found adding links, images, hashtags, and mentions, as well as expressing angry and annoying sentiments all increased retweets and likes. Evidence-based terms such as “peer-review” had high retweet rates but linking directly to peer-reviewed publications decreased attention compared to popular science websites. Word choice and attention did not reflect official or alt account types, indicating topic is more important than source. The number of tweets generated and attention received by alt accounts has decreased since their creation, demonstrating the importance of timeliness in science communication on social media. Together our results show potential pathways for scientists to increase efficacy in Twitter communications.

Identification of affective valence of Twitter generated sentiments during the COVID-19 outbreak.

This study aims to conduct text mining of affective valence of the sentiments generated on social media during the COVID-19 and measure their association with different outcomes of the disease. 50,000 tweets per day over 23 days during the pandemic were extracted using the VADER sentiment analysis tool. Overall, tweets could effectively be classified in terms of polarity, i.e., “positive,” “negative” and “neutral” sentiments. Furthermore, on a day-to-day basis, the study identified a positive and significant relationship between COVID-19-related (a) global infections and negative tweets, (b) global deaths and negative tweets, (c) recoveries and negative tweets, and (d) recoveries and positive tweets. No significant association could be found between (e) infections and positive tweets and (f) deaths and positive tweets. Furthermore, the statistical analysis also indicated that the daily distribution of tweets based on polarity generates three distinct and significantly different numbers of tweets per category, i.e., positive, negative and neutral. As per the results generated through sentiment analysis of tweets in this study, the emergence of “positive” tweets in such a gloomy pandemic scenario shows the inherent resilience of humans. The significant association between news of COVID-19 recoveries and positive tweets seems to hint at a more optimistic scenario whenever the pandemic finally comes to an end or is controlled. Such public reactions—for good—have the potential to go viral and influence several others, especially those who are classified as “neutral” or fence-sitters.

Sentiment Analysis on USA vs. New Zealand on Health and Safety Mandates During Early Stages of COVID-19 Pandemic.

The Coronavirus pandemic has surprised the world and social media was extremely used to express frustrations and development of the cases found. Social media tools, such as Twitter, show a comparable impact with the number of tweets related to COVID-19 indicating remarkable development in a limited ability to focus time. The purpose of this paper is to investigate the impact of Coronavirus on the United States of America (USA) and New Zealand (NZ), and how that is reflected in a sentiment analysis through the examination of American and New Zealand tweets. We have gathered tweets from a March 2020 - August 2020 and used sentiment extraction on the tweets. The major finding of this sentiment extraction is the fact that the overall average sentiment over the 5-month period stayed in a negative range in the USA and NZ. This paper aims to analyze these trends, identify patterns, and determine whether these trends were caused by the COVID-19 pandemic or outside sources. One trend that was analyzed was the spike of COVID-19 results in relation to the number of protests occurring in the USA.

Twitter conversations reveal issue salience of aviation in the broader context of climate change

Growing concern about the climate crisis comes with increased scrutiny of flying as an emission-intensive activity. This study conceptualises a modern communication system and draws on social media data to examine pre-COVID-19 (September to December 2019) public conversations about aviation and climate change. The analysis of >326,000 Twitter posts reveals that issue salience can be considerable at times, especially during key events such as United Nation climate summits and for users from countries such as Portugal, Puerto Rico, Qatar, France and Spain. Topic modelling identified five key frames: economy and industry, public opinion, fairness and conflict, consequences and responsibility. The fairness and conflict frame recorded the largest number of tweets, and was noteworthy for its negative sentiment. It also had the highest retweet rate and the densest network in terms of average users per community. The responsibility frame was interesting for its positive sentiment, and perhaps the opportunity for aviation stakeholders to contribute proactively to the conversations by sharing climate action success stories. Given the importance of public opinion (voters and consumers), and the link between media activity and policy decisions, the ongoing monitoring of issue salience, frames and peer groups related to flying and climate change seems beneficial.

#Twitterimpact: The Impact of Oral Praesentation Tweets at Gynecologic Society Scientific Meetings on Journal Publication

<h3>Study Objective</h3> This study aims to examine the use of Twitter at leading gynecologic surgery society meetings and the impact of Twitter activity on ultimate journal acceptance. <h3>Design</h3> We examined oral presentations at the national conferences of the American Urogynecologic Society (AUGS), the Society of Gynecologic Surgeons (SGS), and the American Association of Gynecologic Laparoscopists (AAGL) from 2017-2019. We accessed the final program on each society's webpage to determine the oral presentations. We excluded video presentations. We then queried PubMed, Google, and GoogleScholar for the same oral presentation title and author names to determine whether the paper had been published. If the paper was published, the name of the journal and Altmetric scores were collected. Twitter profiles were determined based on a Google Search of the presenter or authors' names and the word "Twitter." We then queried Twitter using the collected profiles and hashtags pertaining to each conference (e.g., #SGS2019, #AUGSIUGA2019) to find related tweets. Presentation characteristics were analyzed using descriptive statistics. Comparisons were made using Kruskal-Wallis, Chi-square and Spearman Correlation tests. <h3>Setting</h3> N/A. <h3>Patients or Participants</h3> N/A. <h3>Interventions</h3> N/A. <h3>Measurements and Main Results</h3> Final review included 1,020 oral presentations (361 at AUGS, 131 at SGS, 528 at AAGL). Publication rate was compared between tweeted vs. non-tweeted oral presentations (Table 1). 144 articles with tweets had a statistically significant acceptance rate of 56.2% compared to 42.4% in 876 non-tweeted articles (p= 0.003). The Altmetric score showed a small but significant spearman correlation coefficient of 0.17 with number of tweets (p= <0.001). Tweeted presentations had an average Altmetric score of 6.3 (SD=17.3) compared to 3.7 (SD=17.8) in non-tweeted presentations (p value <0.001). <h3>Conclusion</h3> Tweeted oral presentations when compared to non-tweeted oral presentations at national gynecologic meetings are associated with higher rates of journal publication and Altmetric scores.

Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati

PurposeTwitter is gaining popularity as a microblogging and social networking service to discuss various social issues. Coronavirus disease 2019 (COVID-19) has become a global pandemic and is discussed worldwide. Social media is an instant platform to deliberate various dimensions of COVID-19. The purpose of the study is to explore and analyze the public sentiments related to COVID-19 vaccines across the Twitter messages (positive, neutral, and negative) and the impact tweets make across digital social circles.Design/methodology/approachTo fetch the vaccine-related posts, a manual examination of randomly selected 500 tweets was carried out to identify the popular hashtags relevant to the vaccine conversation. It was found that the hashtags “covid19vaccine” and “coronavirusvaccine” were the two popular hashtags used to discuss the communications related to COVID-19 vaccines. 23,575 global tweets available in public domain were retrieved through “Twitter Application Programming Interface” (API), using “Orange Software”, an open-source machine learning, data visualization and data mining toolkit. The study was confined to the tweets posted in English language only. The default data cleaning and preprocessing techniques available in the “Orange Software” were applied to the dataset, which include “transformation”, “tokenization” and “filtering”. The “Valence Aware Dictionary for sEntiment Reasoning” (VADER) tool was used for classification of tweets to determine the tweet sentiments (positive, neutral and negative) as well as the degree of sentiments (compound score also known as sentiment score). To assess the influence/impact of tweets account wise (verified and unverified) and sentiment wise (positive, neutral, and negative), the retweets and likes, which offer a sort of reward or acknowledgment of tweets, were used.FindingsA gradual decline in the number of tweets over the time is observed. Majority (11,205; 47.52%) of tweets express positive sentiments, followed by neutral (7,948; 33.71%) and negative sentiments (4,422; 18.75%), respectively. The study also signifies a substantial difference between the impact of tweets tweeted by verified and unverified users. The tweets related to verified users have a higher impact both in terms of retweets (65.91%) and likes (84.62%) compared to the tweets tweeted by unverified users. Tweets expressing positive sentiments have the highest impact both in terms of likes (mean = 10.48) and retweets (mean = 3.07) compared to those that express neutral or negative sentiments.Research limitations/implicationsThe main limitation of the study is that the sentiments of the people expressed over one single social platform, that is, Twitter have been studied which cannot generalize the global public perceptions. There can be a variation in the results when the datasets from other social media platforms will be studied.Practical implicationsThe study will help to know the people's sentiments and beliefs toward the COVID-19 vaccines. Sentiments that people hold about the COVID-19 vaccines are studied, which will help health policymakers understand the polarity (positive, negative, and neutral) of the tweets and thus see the public reaction and reflect the types of information people are exposed to about vaccines. The study can aid the health sectors to intensify positive messages and eliminate negative messages for an enhanced vaccination uptake. The research can also help design more operative vaccine-advocating communication by customizing messages using the obtained knowledge from the sentiments and opinions about the vaccines.Originality/valueThe paper focuses on an essential aspect of COVID-19 vaccines and how people express themselves (positively, neutrally and negatively) on Twitter.

Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter

Twitter is one of the social media that is currently in great demand by internet users. The number of tweets circulating on Twitter is not yet known whether these tweets contain more positive, negative, and neutral opinions. For that we need a system that can process data by applying sentiment analysis. This study uses the Naive Bayes Classifier (NBC) method to analyze the level of sentiment towards data carried out by crawling on Twitter. The data studied as a simple case study uses only 8 tweet data which is divided into 5 training data and 3 test data. The data is processed using the preprocessing stage, then classified using the NBC method, the calculation of performance uses confusion matrix techniques. This study resulted in a structured exposure to the process and results of NBC implementation and performance testing using the confusion matrix which obtained 82% accuracy, 93% precision, and 52% recall. However, these results are more focused on ease explaining for each stage and process in more detail, not on the numbers obtained. Research with larger data will be carried out later by developing a computer-based application system.

COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter.

The understanding of the public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. To understand the public response, there is a need to explore public opinion. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. Twitter can provide a great opportunity to understand public opinion regarding COVID-19 vaccines. The current study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets to provide a wider perspective on the COVID-19 vaccine. This study identifies the sentiment of tweets using a machine learning rule-based approach, discovers major topics, explores temporal trend and compares topics of negative and non-negative tweets using statistical tests, and discloses top topics of tweets having negative and non-negative sentiment. Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021. We found Twitter users have discussed a wide range of topics from vaccination sites to the 2020 U.S. election between November 2020 and February 2021. The findings show that there was a significant difference between tweets having negative and non-negative sentiment regarding the weight of most topics. Our results also indicate that the negative and non-negative tweets had different topic priorities and focuses. This research illustrates that Twitter data can be used to explore public opinion regarding the COVID-19 vaccine.

Exploring the synergy between nano-influencers and sports community: behavior mapping through machine learning

PurposeThe paper aims to explore the influence of cybersecurity on the semantic orientation of the sports consumers. Focusing on both sport and esports, this study finds the social media factors contributing in the sentiment formation and commenting behavior on Twitter and proposes a scheme for attitude modulation through identification of highly engaged nano-influencers.Design/methodology/approachExperimental design was used as the research methodology. Data mining from Twitter using RStudio software was conducted using the keyword “cybersecurity” during the time of pandemic. Final corpus of 31,891 tweets were considered for the study. Initial sentiment analysis has been conducted to explore the consumer's emotional inclination towards cybersecurity. Further through generalized equation modeling the impact of social media attributes over the consumer's posting behavior has been analyzed.FindingsThe research findings reveal that users are inherently positive towards cybersecurity adoption in sports and the factors such as number of tweets, number of positive words contained in these tweets and the authenticity of the information source boost the pre-established tweeting behavior. However, the influx of information from non-organizational sources such as trending topics and discussions have negative impact over the users.Originality/valueThis study is first to explore the role of nano-influencers as communication moderators over digital social platforms. This study offers a new understanding of key contributing attributes of sentiments formation over social media and offers a scheme of selection of nano-influencers to modulate the pre-established sentiments of the users. Finally, the current study offers valuable insights into social media engagements and selection of nano-influencers for practicing marketing managers.

Gaps in Public Awareness About BRCA and Genetic Testing in Prostate Cancer: Social Media Landscape Analysis.

BackgroundGenetic testing, particularly for BRCA1/2, is increasingly important in prostate cancer (PCa) care, with impact on PCa management and hereditary cancer risk. However, the extent of public awareness and online discourse on social media is unknown, and presents opportunities to identify gaps and enhance population awareness and uptake of advances in PCa precision medicine.ObjectiveThe objective of this study was to characterize activity and engagement across multiple social media platforms (Twitter, Facebook, and YouTube) regarding BRCA and genetic testing for PCa compared with breast cancer, which has a long history of public awareness, advocacy, and prominent social media presence.MethodsThe Symplur Signals online analytics platform was used to obtain metrics for tweets about (1) #BRCA and #breastcancer, (2) #BRCA and #prostatecancer, (3) #genetictesting and #breastcancer, and (4) #genetictesting and #prostatecancer from 2016 to 2020. We examined the total number of tweets, users, and reach for each hashtag, and performed content analysis for a subset of tweets. Facebook and YouTube were queried using analogous search terms, and engagement metrics were calculated.ResultsDuring a 5-year period, there were 10,005 tweets for #BRCA and #breastcancer, versus 1008 tweets about #BRCA and #prostatecancer. There were also more tweets about #genetictesting and #breastcancer (n=1748), compared with #genetic testing and #prostatecancer (n=328). Tweets about genetic testing (12,921,954) and BRCA (75,724,795) in breast cancer also had substantially greater reach than those about PCa (1,463,777 and 4,849,905, respectively). Facebook groups and pages regarding PCa and BRCA/genetic testing had fewer average members, new members, and new posts, as well as fewer likes and followers, compared with breast cancer. Facebook videos had more engagement than YouTube videos across both PCa and breast cancer content.ConclusionsThere is substantially less social media engagement about BRCA and genetic testing in PCa compared with breast cancer. This landscape analysis provides insights into strategies for leveraging social media platforms to increase public awareness about PCa germline testing, including use of Facebook to share video content and Twitter for discussions with health professionals.

National Leaders’ Usage of Twitter in Response to COVID-19: A Sentiment Analysis

Twitter is a powerful tool for world leaders to disseminate public health information and to reach citizens. While Twitter, like other platforms, affords world leaders the opportunity to rapidly present information to citizens, the discourse is often politically framed. In this study, we analysed how leaders’ of the Five Eyes intelligence sharing group use Twitter to frame the COVID-19 virus. Specifically, four research questions were explored: 1) How frequently did each leader tweet about COVID-19 in 2020? 2) Which frames emerged from tweet content of each leader regarding COVID-19? 3) What was the overall tweet valence of each leader regarding COVID-19? and 4) To what extent can leaders’ future tweets be predicted by the data? We used natural language processing (NLP) and conducted sentiment analysis via Python to identify frames and to compare the leaders’ messaging. Results showed that of the leaders, President Trump tweeted the most, with Prime Minister Morrison posting the least number of tweets. The majority of each leaders’ tweets were positive, while President Trump had the most negative tweets. Predictive modelling of tweet behavior was highly accurate.

Making it (net)work: a social network analysis of “fertility” in Twitter before and during the COVID-19 pandemic

ObjectiveTo characterize activity, text sentiment, and online community characteristics regarding “fertility” on Twitter before and during the COVID-19 pandemic using social network analysis.DesignCross-sectional analysis.SettingPublicly available Twitter data.Patient(s)Not applicable.Intervention(s)Not applicable.Main Outcome Measure(s)Number of users (vertices); edges (connections, defined as unique and total); self-loops (tweet without connection to another user); connected components (groups of users communicating back and forth frequently); maximum vertices in a connected component (largest group size); maximum and average geodesic distance (number of tweets to connect two users in the network); graph density; positive and negative sentiment tweets; and top 5 hashtags and top 5 word pairs.Result(s)There were 1426 unique users and 401 groups in the pre-COVID-19 data compared to 1492 unique users and 453 groups in the during COVID-19 data. There was no difference in the number of total connections (96.8% [1381/1426] vs. 96.0% [1433/1492]) or self-loops (20.0% [286/1426] vs. 22.1% [329/1492]) before and during the COVID-19 pandemic. The percentage of unique connections per user decreased during COVID-19 (91.6% [1381/1508] pre-COVID-19 vs. 83.3% [1433/1720] during COVID-19). The average and maximum distance between users in the community increased during COVID-19 (maximum: 5 pre-COVID-19, 8 during COVID-19; average 1.95 pre-COVID-19, 2.43 during COVID-19). The percentage of positive sentiments per total number of tweets increased during COVID-19 (58.1% pre-COVID-19 [773/1331] vs. 64.3% [1198/1863] during COVID-19). The top 5 hashtags changed during COVID-19 to include COVID-19. The top word pairs changed from “family, hereditary; parents, children” to “fertility, treatment; healthcare, decisions.”Conclusion(s)Despite the challenge to the fertility community amidst the COVID-19 pandemic, the overall Twitter sentiment regarding fertility was more positive during than before the pandemic. Top hashtags and word pairs changed to reflect the emergence of COVID-19 and the unique healthcare decision-making challenges faced. While the character, the number of users, and the total connections remained constant, the number of unique connections and the distance between users changed to reflect more self-broadcasting and less tight connections.

Comparing the Digital Footprint of Pulmonary and Critical Care Conferences on Twitter.

Background: Pulmonary and critical care societies, including the American Thoracic Society, the American College of Chest Physicians, and the Society of Critical Care Medicine have large memberships that gather at academic conference events, attracting thousands of attendees.Objective: With the growth of social media use among pulmonary and critical care clinicians, our goal was to examine the Twitter presence and digital footprint of these three major medical society conferences.Methods: We used Symplur Signals (Symplur, LLC) to track the tweets and most active participants of the 2017–2019 annual conferences of American Thoracic Society, American College of Chest Physicians, and the Society of Critical Care Medicine. Attendance records of participants were obtained from each society.Results: During the study period, there was growth in the number of tweets, participants, and impressions for all three society conferences. Across all conferences, the amount of original content generated was less than the retweets, which comprised 50–72% of all tweets. Individuals physically attending each conference were more likely to post original content than those not in attendance (53–68% vs. 32–47%). For each society and at each meeting, clinicians made up the largest group of participants (44–60%), and most (59–82%) were physicians. A small cohort of participants was responsible for a large share of the tweets, with more than half of the participants at each conference for each society tweeting only once and only between 5–8% of participants tweeting more than 10 times. Seventy-eight individuals tweeted more than 100 times at one or more of the conferences. There was significant overlap in this group, with 32 of these individual participants tweeting more than 100 times at two or more of these conferences.Conclusion: Growth in conference digital footprints is largely due to increased activity by a small group of prolific participants that attend conferences by multiple academic societies. Original content makes up the smallest proportion of posts, suggesting that amplification of content is more prevalent than posting of original content. In a postpandemic environment, engagement of users producing original content may be even more important for medical societies.

LBA01-03 ACADEMIC UROLOGY TWEET PERFORMANCE DASHBOARD

You have accessJournal of UrologyLate-breaking Abstract I - Benign1 Sep 2021LBA01-03 ACADEMIC UROLOGY TWEET PERFORMANCE DASHBOARD Emily Manning, Adam Calaway, Justin Dubin, Stacy Loeb, Mohit Sindhani, Alexander Kutikov, Kirtishri Mishra, Lee Ponksy, and Laura Bukavina Emily ManningEmily Manning More articles by this author , Adam CalawayAdam Calaway More articles by this author , Justin DubinJustin Dubin More articles by this author , Stacy LoebStacy Loeb More articles by this author , Mohit SindhaniMohit Sindhani More articles by this author , Alexander KutikovAlexander Kutikov More articles by this author , Kirtishri MishraKirtishri Mishra More articles by this author , Lee PonksyLee Ponksy More articles by this author , and Laura BukavinaLaura Bukavina More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002148.03AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The COVID-19 pandemic has limited in-person opportunities such as away rotations and residency interviews within academic urology. Twitter may serve as a vital tool mitigating these barriers and expanding the field. Our study aims to re-evaluate Twitter usage among academic urology programs in 2020 compared to 2009-2019. METHODS: We evaluated 83,000 tweets from 113/133 U.S. academic urology program twitter accounts during 2009-2020 through the Application Programming Interface on April 2, 2021. Text patterns were interpreted as positive, neutral, or negative via sentiment analysis. Tweets, number of followers, @mentions, hashtags, and account creations were analyzed. We captured word cloud (visualization of most frequent words) and trigram (most frequent three-word combinations) analysis. RESULTS: Our data revealed that 2020 depicted a record number of tweets (22,544) and account creations (23). For all programs, the median (IQR) number of tweets and followers was 1,748 (872-3,051) and 2,201 (1,509-3,956). The University of North Carolina (8,707) tweeted the most. However, Johns Hopkins (5,365) had the most followers. The most frequently mentioned account in both 2009-2019 and 2020 was the American Urological Association (@americanurological). @uro_res and @uroresidency ranked higher in 2020 than previously. Compared to 2009-2019, positive sentiment increased by 3%, while negative decreased by 2%. Word cloud analysis identified urology and resident as the most prominent words in 2020, shifting from urology and cancer (2009-2019). Trigram analysis revealed 2009-2019 tweets pertained around (risk, prostate, cancer) whereas 2020 tweets revolved around (virtual, open, house), suggesting a context shift to program recruitment. CONCLUSIONS: Our novel findings reveal that urology programs are capitalizing Twitter to promote residency recruitment and education via increased tweets and account creations, which will have far-reaching implications on urology's future. Source of Funding: No funding © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e1171-e1171 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Emily Manning More articles by this author Adam Calaway More articles by this author Justin Dubin More articles by this author Stacy Loeb More articles by this author Mohit Sindhani More articles by this author Alexander Kutikov More articles by this author Kirtishri Mishra More articles by this author Lee Ponksy More articles by this author Laura Bukavina More articles by this author Expand All Advertisement Loading ...

Changes in Public Response Associated With Various COVID-19 Restrictions in Ontario, Canada: Observational Infoveillance Study Using Social Media Time Series Data.

BackgroundNews media coverage of antimask protests, COVID-19 conspiracies, and pandemic politicization has overemphasized extreme views but has done little to represent views of the general public. Investigating the public’s response to various pandemic restrictions can provide a more balanced assessment of current views, allowing policy makers to craft better public health messages in anticipation of poor reactions to controversial restrictions.ObjectiveUsing data from social media, this infoveillance study aims to understand the changes in public opinion associated with the implementation of COVID-19 restrictions (eg, business and school closures, regional lockdown differences, and additional public health restrictions, such as social distancing and masking).MethodsCOVID-19–related tweets in Ontario (n=1,150,362) were collected based on keywords between March 12 and October 31, 2020. Sentiment scores were calculated using the VADER (Valence Aware Dictionary and Sentiment Reasoner) algorithm for each tweet to represent its negative to positive emotion. Public health restrictions were identified using government and news media websites. Dynamic regression models with autoregressive integrated moving average errors were used to examine the association between public health restrictions and changes in public opinion over time (ie, collective attention, aggregate positive sentiment, and level of disagreement), controlling for the effects of confounders (ie, daily COVID-19 case counts, holidays, and COVID-19–related official updates).ResultsIn addition to expected direct effects (eg, business closures led to decreased positive sentiment and increased disagreements), the impact of restrictions on public opinion was contextually driven. For example, the negative sentiment associated with business closures was reduced with higher COVID-19 case counts. While school closures and other restrictions (eg, masking, social distancing, and travel restrictions) generated increased collective attention, they did not have an effect on aggregate sentiment or the level of disagreement (ie, sentiment polarization). Partial (ie, region-targeted) lockdowns were associated with better public response (ie, higher number of tweets with net positive sentiment and lower levels of disagreement) compared to province-wide lockdowns.ConclusionsOur study demonstrates the feasibility of a rapid and flexible method of evaluating the public response to pandemic restrictions using near real-time social media data. This information can help public health practitioners and policy makers anticipate public response to future pandemic restrictions and ensure adequate resources are dedicated to addressing increases in negative sentiment and levels of disagreement in the face of scientifically informed, but controversial, restrictions.