Original Tweets Research Articles

Cyberactivism and Social and Political Participation Regarding the War in Ukraine: Analysis of Social Interactions in Spanish on Twitter

To evaluate and understand the dynamics of citizen participation in social networks concerning the war in Ukraine and delve into the phenomenon of cyberactivism, this study focuses on the conversation generated in Spanish around the conflict on Twitter. The research analyses 1,138,747 original tweets to investigate the general characteristics of the conversation, the user interaction patterns, and the creation and structure of communities and to determine the connecting factors. The study employs machine learning and artificial intelligence techniques via the Graphext data analytics tool. The conversation volume is very high, but the network of interactions is characterized by being unstructured and dominated by white noise, with disjointed interactions setting the tone of the conversation. Verified accounts and emotionality stand out as factors of connection and interconnection in the network. This case study highlights a form of social and political participation oriented towards visibility and information about the cause at a level of deliberation and debate that shows empathy and sympathy in a context close to slacktivism.

Tweeting and retweeting scientific articles: implications for altmetrics

Despite differences in extent of engagement of users, original tweets and retweets to scientific publications are considered as equal events. Current research investigates quantifiable differences between tweets and retweets from an altmetric point of view. Twitter users, text, and media content of two datasets, one containing 742 randomly selected tweets and retweets (371 each) and another with 5898 tweets and retweets (about 3000 each), all linking to scientific articles published on PLoS ONE, were manually categorized. Results from analyzing the proportions of tweets and retweets indicated that academic and individual accounts produce majority of original tweets (34% and 55%, respectively) and posted significantly larger proportion of retweets (41.5 and 81%). Bot accounts, on the other hand, had posted significantly more original tweets (20%) than retweets (2%). Natural communication sentences prevailed in retweets and tweets (63% vs. 45%) as well as images (41.5% vs. 23%), both showing a significant rise in usage overtime. Overall, the findings suggest that the attention scientific articles receive on Twitter may have more to do with human interaction and inclusion of visual content in the tweets, than the significance of or genuine interest towards the research results.

How Does an Online Mental Health Community on Twitter Empower Diverse Population Levels and Groups? A Qualitative Analysis of #BipolarClub.

Social media, including online health communities (OHCs), are widely used among both healthy people and those with health conditions. Platforms like Twitter (recently renamed X) have become powerful tools for online mental health communities (OMHCs), enabling users to exchange information, express feelings, and socialize. Recognized as empowering processes, these activities could empower mental health consumers, their families and friends, and society. However, it remains unclear how OMHCs empower diverse population levels and groups. This study aimed to develop an understanding of how empowerment processes are conducted within OMHCs on Twitter by identifying members who shape these communities, detecting the types of empowerment processes aligned with the population levels and groups outlined in Strategy 1 of the Integrated People-Centred Health Services (IPCHS) framework by the World Health Organization (WHO), and clarifying members' involvement tendencies in these processes. We conducted our analysis on a Twitter OMHC called #bipolarclub. We captured 2068 original tweets using its hashtag #bipolarclub between December 19, 2022, and January 15, 2023. After screening, 547 eligible tweets by 182 authors were analyzed. Using qualitative content analysis, community members were classified by examining the 182 authors' Twitter profiles, and empowerment processes were identified by analyzing the 547 tweets and categorized according to the WHO's Strategy 1. Members' tendencies of involvement were examined through their contributions to the identified processes. The analysis of #bipolarclub community members unveiled 5 main classifications among the 182 members, with the majority classified as individual members (n=138, 75.8%), followed by health care-related members (n=39, 21.4%). All members declared that they experience mental health conditions, including mental health and general practitioner members, who used the community as consumers and peers rather than for professional services. The analysis of 547 tweets for empowerment processes revealed 3 categories: individual-level processes (6 processes and 2 subprocesses), informal carer processes (1 process for families and 1 process for friends), and society-level processes (1 process and 2 subprocesses). The analysis also demonstrated distinct involvement tendencies among members, influenced by their identities, with individual members engaging in self-expression and family awareness support and health care-related members supporting societal awareness. The examination of the #bipolarclub community highlights the capability of Twitter-based OMHCs to empower mental health consumers (including those from underserved and marginalized populations), their families and friends, and society, aligning with the WHO's empowerment agenda. This underscores the potential benefits of leveraging Twitter for such objectives. This pioneering study is the very first to analyze how a single OMHC can empower diverse populations, offering various health care stakeholders valuable guidance and aiding them in developing consumer-oriented empowerment programs using such OMHCs. We also propose a structured framework that classifies empowerment processes in OMHCs, inspired by the WHO's Strategy 1 (IPCHS framework).

The Effect of Economic Ties on Digital Diplomacy

Summary The article examines how economic ties between host and guest countries affect the emotional valence in the social media content published by digital diplomats. Strong economic ties will lead digital diplomats to adopt a positive tone because such ties raise the potential costs of verbal aggressiveness online. A positive emotional valence on social media also serves to cultivate good public perceptions of the guest and its economic activities. To evaluate these claims, the article analyses 53,601 original tweets published by 88 Chinese diplomatic missions on Twitter from 2014 to 2020. It finds that economic ties have a strong positive effect on the tone adopted by digital diplomats. As the host’s trade dependence on China increases, Chinese diplomatic missions are more likely to adopt a positive tone on Twitter, especially when talking about politics and business. This research contributes to the study of how countries use social media to conduct diplomacy.

Neurosurgery Residency Programs on Twitter (X): Current Use and Future Potential

Study DesignCross-sectional study ObjectivesTwitter (X) is increasingly utilized by medical residency programs to boost engagement and potentially enhance ranking. This study aims to evaluate the role of Twitter (X) in neurosurgery residency programs, assessing their online activity, followership, and content to identify current practices and potential improvements. MethodsWe identified 97/123 neurosurgery residency programs in the United States with Twitter accounts. Active accounts, posting in 2022 and 2023, were matched with the American Association of Neurological Surgeons (AANS) Neurosurgical Residency Training Program Directory. Tweets from January 2022 through June 2023 were categorized as original or retweets and further subcategorized based on content. Descriptive and correlation analyses were conducted. ResultsTwitter (X) accounts were found for 78.8% of accredited neurosurgery programs (n=97/123), with 68.3% active as of July 2023 (n=84/123). All active accounts amassed 167,068 followers. Among 5,612 tweets identified, 2,764 were original content and 2,848 were retweets. Tweets primarily focused on resident or program information (64.3%), research publications (32.0%), and conference participation (9.4%). Only 12.3% (n=341) of original tweets contained public medical information. A significant correlation (r = 0.493, p <0.001) was observed between Twitter (X) following and program ranking. ConclusionsThe majority of US neurosurgery residency programs actively use Twitter (X), primarily to showcase academic achievements and aid in recruitment. The correlation between Twitter following and program ranking suggests that online engagement reflects program visibility and impact, underscoring the importance of social media in program outreach and strategic management for maximizing program benefits.

What is said about #donateliver or #liverdonor? Reflexive thematic analysis of Twitter (X) posts from 2012 to 2022

BackgroundThere is sustained interest in understanding the perspectives of liver transplant recipients and living donors, with several qualitative studies shedding light on this emotionally charged subject. However, these studies have relied primarily on traditional semi-structured interviews, which, while valuable, come with inherent limitations. Consequently, there remains a gap in our comprehension of the broader public discourse surrounding living liver donation. This study aims to bridge this gap by delving into public conversations related to living liver donation through a qualitative analysis of Twitter (now X) posts, offering a fresh perspective on this critical issue.MethodsTo compile a comprehensive dataset, we extracted original tweets containing the hashtags “#donateliver” OR “#liverdonor”, all posted in English from January 1, 2012, to December 31, 2022. We then selected tweets from individual users whose Twitter (X) accounts featured authentic human names, ensuring the credibility of our data. Employing Braun and Clarke’s reflexive thematic analysis approach, the study investigators read and analysed the included tweets, identifying two main themes and six subthemes. The Health Policy Triangle framework was applied to understand the roles of different stakeholders involved in the discourse and suggest areas for policy improvement.ResultsA total of 361 unique tweets from individual users were analysed. The major theme that emerged was the persistent shortage of liver donors, underscoring the desperation faced by individuals in need of life-saving liver transplants and the urgency of addressing the organ shortage problem. The second theme delved into the experiences of liver donors post-surgery, shedding light on a variety of aspects related to the transplantation process, including the visibility of surgical scars, and the significance of returning to physical activity and exercise post-surgery.ConclusionThe multifaceted experiences of individuals involved in the transplantation process, both recipients and donors, should be further studied in our efforts to improve the critical shortage of liver donors.

Impoliteness in Twitter Discourse: a Case Study of Replies to Donald Trump and Greta Thunberg

Twitter (X) is a popular social media platform that allows users to express their opinions and interact on various topics, including politics. However, Twitter can provide a space for impolite and aggressive language use, especially when the issues are controversial or polarizing. This study analyzes the replies to two controversial and similar tweets, namely Donald Trump’s tweet to Greta Thunberg and Greta Thunberg’s tweet to Donald Trump. Ninety-seven tweets that impolitely took issue with the original tweets were collected and coded for their moral order themes and pragmatic functions. Culpeper’s (2011) impoliteness framework was consulted as a threshold to include or exclude reply tweets in the data analysis. The results show that the replies invoked moral order expectations in three overarching categories in the responses to both parties: age-appropriate behavior, respect and manners, and concern for the common good. As far as the pragmatic functions were concerned, criticism of personal characteristics, criticism of supporters, criticism of relatives (to Trump only), praise of the opposing party, directing, mockery, and ideology denial (to Thunberg only) were performed. The study discusses how users took offense through language in a highly polarized political context.

Statin Twitter: Human and Automated Bot Contributions, 2010 to 2022.

Many individuals eligible for statin therapy decline treatment, often due to fear of adverse effects. Misinformation about statins is common and drives statin reluctance, but its prevalence on social media platforms, such as Twitter (now X) remains unclear. Social media bots are known to proliferate medical misinformation, but their involvement in statin-related discourse is unknown. This study examined temporal trends in volume, author type (bot or human), and sentiment of statin-related Twitter posts (tweets). We analyzed original tweets with statin-related terms from 2010 to 2022 using a machine learning-derived classifier to determine the author's bot probability, natural language processing to assign each tweet a negative or positive sentiment, and manual qualitative analysis to identify statin skepticism in a random sample of all tweets and in highly influential tweets. We identified 1 155 735 original statin-related tweets. Bots produced 333 689 (28.9%), humans produced 699 876 (60.6%), and intermediate probability accounts produced 104 966 (9.1%). Over time, the proportion of bot tweets decreased from 47.8% to 11.3%, and human tweets increased from 43.6% to 79.8%. The proportion of negative-sentiment tweets increased from 27.8% to 43.4% for bots and 30.9% to 38.4% for humans. Manually coded statin skepticism increased from 8.0% to 19.0% for bots and from 26.0% to 40.0% for humans. Over the past decade, humans have overtaken bots as generators of statin-related content on Twitter. Negative sentiment and statin skepticism have increased across all user types. Twitter may be an important forum to combat statin-related misinformation.

A novel fine-grained rumor detection algorithm with attention mechanism

Rumors circulating on social media platforms have consistently represented a substantial threat to societal security and stability. Both academia and the industry have dedicated heightened focus to addressing the issue of rumor detection. Recent research has made significant progress in using deep neural networks to model the textual content and propagation structure of rumors. However, these methods model rumor-related features at a coarse-grained level and do not take full advantage of the various contextual information associated with rumors. In this paper, we propose a new model called Hybrid Rumor Detection Model with Co-Attention Mechanisms(CoAHRD), which utilizes the original tweet content, social context information, and user information for rumor detection. First, we use the Fine-Grained Feature Learning(FGFL) algorithm to extract fine-grained features from the tweets. Based on this, we use the Graph Convolutional Network(GCN) to learn the Multi-relation graph propagation structure features of rumors. Next, we combine FGFL features with temporal encoding information to model the temporal structure of rumors. Then, we introduce a User Feature-Based Co-Attention Network(UFCoAN) to learn the tendency of different users to spread rumors. Finally, we fuse the above features through a fully connected layer and perform rumor detection. Extensive experiments on two publicly available datasets, PHEME and TWITTER15, show that our method outperforms the current mainstream methods. In particular, in terms of accuracy, our model improves by 0.9% and 1.6% over the best baseline method on the Twitter15 and PHEME datasets, respectively.

Who are the best voluntary disseminators on social networking sites? An exploration based on panel data from twitter

PurposeUsers' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.Design/methodology/approachVia Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.FindingsPreference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.Originality/valueThis work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

Public perception on active aging after COVID-19: an unsupervised machine learning analysis of 44,343 posts.

To analyze public perceptions of active aging in China on mainstream social media platforms to determine whether the "14th Five Year Plan for the Development of the Aging Career and Older Adult Care System" issued by the CPC in 2022 has fully addressed public needs. The original tweets posted on Weibo between January 1, 2020, and June 30, 2022, containing the words "aging" or "old age" were extracted. A bidirectional encoder representation from transformers (BERT)-based model was used to generate themes related to this perception. A qualitative thematic analysis and an independent review of the theme labels were conducted by the researchers. The findings indicate that public perceptions revolved around four themes: (1) health prevention and protection, (2) convenient living environments, (3) cognitive health and social integration, and (4) protecting the rights and interests of the older adult. Our study found that although the Plan aligns with most of these themes, it lacks clear planning for financial security and marital life.

BERT for Twitter Sentiment Analysis: Achieving High Accuracy and Balanced Performance

The Bidirectional Encoder Representations from Transformers (BERT) model is used in this work to analyse sentiment on Twitter data. A Kaggle dataset of manually annotated and anonymized COVID-19-related tweets was used to refine the model. Location, tweet date, original tweet content, and sentiment labels are all included in the dataset. When compared to the Multinomial Naive Bayes (MNB) baseline, BERT's performance was assessed, and it achieved an overall accuracy of 87% on the test set. The results indicated that for negative feelings, the accuracy was 0.93, the recall was 0.84, and the F1-score was 0.88; for neutral sentiments, the precision was 0.86, the recall was 0.78, and the F1-score was 0.82; and for positive sentiments, the precision was 0.82, the recall was 0.94, and the F1-score was 0.88. The model's proficiency with the linguistic nuances of Twitter, including slang and sarcasm, was demonstrated. This study also identifies the flaws of BERT and makes recommendations for future research paths, such as the integration of external knowledge and alternative designs.

Cross sectional study of Twitter (X) use among academic anesthesiology departments in the United States.

Twitter (recently renamed X) is used by academic anesthesiology departments as a social media platform for various purposes. We hypothesized that Twitter (X) use would be prevalent among academic anesthesiology departments and that the number of tweets would vary by region, physician faculty size, and National Institutes of Health (NIH) research funding rank. We performed a descriptive study of Twitter (X) use by academic anesthesiology departments (i.e. those with a residency program) in 2022. Original tweets were collected using a Twitter (X) analytics tool. Summary statistics were reported for tweet number and content. The median number of tweets was compared after stratifying by region, physician faculty size, and NIH funding rank. Among 166 academic anesthesiology departments, there were 73 (44.0%) that had a Twitter (X) account in 2022. There were 3,578 original tweets during the study period and the median number of tweets per department was 21 (25th-75th = 0, 75) with most tweets (55.8%) announcing general departmental news and a smaller number highlighting social events (12.5%), research (11.1%), recruiting (7.1%), DEI activities (5.2%), and trainee experiences (4.1%). There was no significant difference in the median number of tweets by region (P = 0.81). The median number of tweets differed significantly by physician faculty size (P<0.001) with larger departments tweeting more and also by NIH funding rank (P = 0.005) with highly funded departments tweeting more. In 2022, we found that less than half of academic anesthesiology departments had a Twitter (X) account, and the median number of annual tweets per account was relatively low. Overall, Twitter (X) use was less common than anticipated among academic anesthesiology departments and most tweets focused on promotion of departmental activities or individual faculty. There may be opportunities for more widespread and effective use of Twitter (X) by academic anesthesiology departments including education about anesthesiology as a specialty.

Exploring Discussions About Virtual Reality on Twitter to Inform Brain Injury Rehabilitation: Content and Network Analysis.

Virtual reality (VR) use in brain injury rehabilitation is emerging. Recommendations for VR development in this field encourage end user engagement to determine the benefits and challenges of VR use; however, existing literature on this topic is limited. Data from social networking sites such as Twitter may further inform development and clinical practice related to the use of VR in brain injury rehabilitation. This study collected and analyzed VR-related tweets to (1) explore the VR tweeting community to determine topics of conversation and network connections, (2) understand user opinions and experiences of VR, and (3) identify tweets related to VR use in health care and brain injury rehabilitation. Publicly available tweets containing the hashtags #virtualreality and #VR were collected up to twice weekly during a 6-week period from July 2020 to August 2020 using NCapture (QSR International). The included tweets were analyzed using mixed methods. All tweets were coded using inductive content analysis. Relevant tweets (ie, coded as "VR in health care" or "talking about VR") were further analyzed using Dann's content coding. The biographies of users who sent relevant tweets were examined descriptively. Tweet data networks were visualized using Gephi computational analysis. A total of 260,715 tweets were collected, and 70,051 (26.87%) were analyzed following eligibility screening. The sample comprised 33.68% (23,596/70,051) original tweets and 66.32% (46,455/70,051) retweets. Content analysis generated 10 main categories of original tweets related to VR (ie, advertising and promotion, VR content, talking about VR, VR news, general technology, VR industry, VR live streams, VR in health care, VR events, and VR community). Approximately 4.48% (1056/23,596) of original tweets were related to VR use in health care, whereas 0.19% (45/23,596) referred to VR in brain injury rehabilitation. In total, 14.86% (3506/23,596) of original tweets featured commentary on user opinions and experiences of VR applications, equipment, and software. The VR tweeting community comprised a large network of 26,001 unique Twitter users. Users that posted tweets related to "VR in health care" (2124/26,001, 8.17%) did not form an interconnected VR network, whereas many users "talking about VR" (3752/26,001, 14.43%) were connected within a central network. This study provides valuable data on community-based experiences and opinions related to VR. Tweets showcased various VR applications, including in health care, and identified important user-based considerations that can be used to inform VR use in brain injury rehabilitation (eg, technical design, accessibility, and VR sickness). Limited discussions and small user networks related to VR in brain injury rehabilitation reflect the paucity of literature on this topic and the potential underuse of this technology. These findings emphasize that further research is required to understand the specific needs and perspectives of people with brain injuries and clinicians regarding VR use in rehabilitation.

Twitter as a Mechanism of Knowledge Translation in Health Professions Education: An Exploratory Content Analysis.

Social media may facilitate knowledge sharing within health professions education (HPE), but whether and how it is used as a mechanism of knowledge translation (KT) is not understood. This exploratory study aimed to ascertain what content has been shared on Twitter using #MedEd and how it is used as a mechanism of KT. Symplur was used to identify all tweets tagged with #MedEd between March 2021 - March 2022. A directed content analysis and multiple cycles of coding were employed. 18,000 tweets were identified, of which 478 were included. Studies sharing high quality HPE information; relating to undergraduate, postgraduate, or continuing education; referring to an evidence source; and posted in English or French were included. Diverse content was shared using #MedEd, including original tweets, links to peer-reviewed articles, and visual media. Tweets shared information about new educational approaches; system, clinical, or educational research outcomes; and measurement tools. #MedEd appears to be a mechanism of diffusion (n = 296 tweets) and dissemination (n = 164 tweets). It is less frequently used for knowledge exchange (n = 13 tweets) and knowledge synthesis (n = 5 tweets). No tweets demonstrated the ethically sound application of knowledge. It is challenging to determine whether and how #MedEd is used to promote the uptake of knowledge into HPE or if it is even possible for Twitter to serve these purposes. Further studies exploring how health professions educators use the knowledge gained from Twitter to inform their educational or clinical practices are recommended.

Abstract 15459: Statin Twitter: Human and "Bot" Contributions, 2010-2022

Background: Half of patients with ASCVD do not take statin medications despite proven efficacy and safety. Misinformation about statins drives skepticism about their use. Social media “bots” are known to proliferate misinformation, but involvement in statin-related discourse is unknown. Objectives: 1) to examine temporal trends in volume and sentiment of statin-related Twitter posts (tweets); 2) to compare statin-related content generated by humans and bots Methods: We queried Twitter for all tweets with statin-related terms from 2010-2022. We used a machine learning-derived classifier to determine tweet authorship as bot, human, or intermediate, and natural language processing to assign a sentiment score to each tweet. Manual qualitative analysis was performed on a randomly selected sample of all tweets and highly influential tweets to quantify statin skepticism. Results: We identified 1,155,735 original tweets (excluding retweets) with statin-related terms, the most common being “statin” (n=832,627, 72%) and “Lipitor” (n=197,015, 17%). Bots produced 333,689 (29%), humans produced 699,876 (61%), and intermediate accounts produced 104,966 (9.1%). Over the study period, the proportion of bot tweets decreased from 48% to 11%, human tweets increased from 44% to 80%, and intermediate tweets decreased from 7.7% to 6.7%. The proportion of negative sentiment tweets increased from 28% to 43% for bots, 31% to 38% for humans, and 29% to 40% for intermediate accounts. Including retweets, negative sentiment increased from 30% to 45% across all tweets. In a qualitative analysis of highly influential tweets, statin skepticism increased from 7% to 29% for bots and 25% to 36% for humans. Conclusions: Since 2010, humans have overtaken bots as generators of statin-related content on Twitter. Negative sentiment and statin skepticism have increased, especially among highly influential tweets. Misinformation interventions should focus on human users and influential bots.

The impact of a dedicated social media strategy on enhancing surgical education

PurposeSocial media (SoMe) is increasingly important in surgical education and may be necessary in the current learning environment. Whilst expanding in use and applications, few studies detail the impact of SoMe on measurable outcomes. The goal of this study was to quantify the impact of a dedicated SoMe strategy on engagement metrics for surgical research.MethodsA retrospective review of a peer-reviewed surgical journal’s Twitter microblog platform (@ColorectalDis) was performed from 6/2015 to 4/2021. A formal SoMe strategy was introduced in September 2018. Data were stratified into 2 time periods: pre-intervention (6-2015 to 9-2018) and post-SoMe intervention (9-2018 to 4-2021). The main outcome was the impact of the SoMe strategy on user engagement with the Twitter platform, journal, and traditional journal metrics. Twitter Analytics and Twitonomy were used to analyse engagement.ResultsFrom conception to analysis, the microblog published 1198 original tweets, generating 5 million impressions and 231,000 engagements. Increased account activity (increased tweets published per month—5.51 vs 28.79; p < 0.01) was associated with significant engagement growth, including new monthly followers (213 vs 38; p < 0.01) and interactions with posted articles (4,096,167 vs 269,152; p < 0.01). Article downloads increased twenty-fold post-SoMe intervention (210,449 vs 10,934; p < 0.01), with significant increases in traditional journal metrics of new subscribers (+11%), article submissions (+24%), and impact factor (+0.9) (all p < 0.01).ConclusionSoMe directly impacts traditional journal metrics in surgical research. By examining the patterns of user engagement between SoMe and journal sites, the growing beneficial impact of a structured social media strategy and SoMe as an educational tool is demonstrated.

“Let the tournament for the woke begin!”: Euro 2020 and the reproduction of Cultural Marxist conspiracies in online criticisms of the “take the knee” protest

ABSTRACT Exploring online criticisms of the “take the knee” protest during “Euro 2020”, this article examines how alt- and far-right conspiracies were both constructed and communicated via the social media platform, Twitter. By providing a novel exploration of alt-right conspiracies during an international football tournament, a qualitative thematic analysis of 1,388 original tweets relating to Euro 2020 was undertaken. The findings reveal how, in criticisms levelled at both “wokeism” and the Black Lives Matter movement, anti-white criticisms of the “take the knee” protest were embroiled in alt-right conspiracies that exposed an assumed Cultural Marxist, “woke agenda” in the tournament’s organisation and mainstream media coverage. In conclusion, it is argued that conspiratorial discourses, associated with the alt-right, provided a framework through which the protest could be understood. This emphasises how the significance of conspiracy functions to promote the wider dissemination of alt-right ideology across popular cultural contexts, such as sport.

Community perspectives on spinal cord injury in rugby union: facts and fears

ABSTRACT Introduction The sport of rugby union, henceforth rugby, is associated with a risk of spinal cord injury (SCI). Perceived risks can impact participation. Understanding community perspectives on rugby-related SCI may provide insight for addressing concerns around risk. The aim of this study was to explore community perspectives through social media discussion about SCI in rugby union. Methods Posts about SCI in rugby union were identified using the advanced search function on Twitter. Data (content as original post, retweet, quote tweet and comments) were included if focused on Rugby Union and written in English. Inclusion dates (July 2018 to June 2019) were chosen to capture a period when several SCI (n = 4) occurred in community rugby in Australia. Data were analysed using a thematic approach. Results Four themes were derived from the collected data. The ‘pendulum swing’ relates to the disparate views of rugby, from being overly cautious to too dangerous to play. The ‘role of rugby culture’ described engrained behaviours and attitudes on and off-field toward safety. ‘Media influence’ describes the emotive narrative used when reporting rugby-related SCI. ‘After the injury’ looks at expressions of sympathy and inspiration. These findings showed how individuals’ views of SCI were influenced through rugby culture, trust in governing bodies and the news media. Conclusion By ‘listening in’ to community views, their most pertinent safety concerns can be addressed. Both facts and fears on rugby-related SCI were evident, and these extreme views can be balanced with evidence-based education and sensible risk management.

Who tweets scientific publications? A large‐scale study of tweeting audiences in all areas of research

AbstractThe purpose of this study is to investigate the validity of tweets about scientific publications as an indicator of societal impact by measuring the degree to which the publications are tweeted beyond academia. We introduce methods that allow for using a much larger and broader data set than in previous validation studies. It covers all areas of research and includes almost 40 million tweets by 2.5 million unique tweeters mentioning almost 4 million scientific publications. We find that, although half of the tweeters are external to academia, most of the tweets are from within academia, and most of the external tweets are responses to original tweets within academia. Only half of the tweeted publications are tweeted outside of academia. We conclude that, in general, the tweeting of scientific publications is not a valid indicator of the societal impact of research. However, publications that continue being tweeted after a few days represent recent scientific achievements that catch attention in society. These publications occur more often in the health sciences and in the social sciences and humanities.