This study investigates the extent to which scholarly tweets of scientific papers are engaged with by Twitter users through four types of user engagement behaviors, i.e., liking, retweeting,... Show moreThis study investigates the extent to which scholarly tweets of scientific papers are engaged with by Twitter users through four types of user engagement behaviors, i.e., liking, retweeting, quoting, and replying. Based on a sample consisting of 7 million scholarly tweets of Web of Science papers, our results show that likes is the most prevalent engagement metric, covering 44% of scholarly tweets, followed by retweets (36%), whereas quotes and replies are only present for 9% and 7% of all scholarly tweets, respectively. From a disciplinary point of view, scholarly tweets in the field of Social Sciences and Humanities are more likely to trigger user engagement over other subject fields. The presence of user engagement is more associated with other Twitter-based factors (e.g., number of mentioned users in tweets and number of followers of users) than with science-based factors (e.g., citations and Mendeley readers of tweeted papers). Building on these findings, this study sheds light on the possibility to apply user engagement metrics in measuring deeper levels of Twitter reception of scholarly information. Show less
The data re-collection for tweets from data snapshots is a common methodological step in Twitter-based research. Understanding better the volatility of tweets over time is important for validating... Show moreThe data re-collection for tweets from data snapshots is a common methodological step in Twitter-based research. Understanding better the volatility of tweets over time is important for validating the reliability of metrics based on Twitter data. We tracked a set of 37,918 original scholarly tweets mentioning COVID-19-related research daily for 56 days and captured the reasons for the changes in their availability over time. Results show that the proportion of unavailable tweets increased from 1.6 to 2.6% in the time window observed. Of the 1,323 tweets that became unavailable at some point in the period observed, 30.5% became available again afterwards. “Revived” tweets resulted mainly from the unprotecting, reactivating, or unsuspending of users' accounts. Our findings highlight the importance of noting this dynamic nature of Twitter data in altmetric research and testify to the challenges that this poses for the retrieval, processing, and interpretation of Twitter data about scientific papers. Show less
The increasing popularity of Twitter in both scholarly communication and public engagement with science has triggered widespread Twitter interactions around scientific information, thus giving rise... Show moreThe increasing popularity of Twitter in both scholarly communication and public engagement with science has triggered widespread Twitter interactions around scientific information, thus giving rise to the emergence of scholarly Twitter metrics which aim to measure and characterize Twitter interactions related to scholarly objects. For the sake of more advanced scholarly Twitter metrics, the overarching aim of this PhD dissertation is to characterize diverse forms of Twitter interactions around scientific papers to understand in greater-depth the Twitter reception of scientific information and improve scholarly Twitter metrics. To this end, this dissertation starts with large-scale analyses of how many and how fast scientific papers are mentioned on Twitter in comparison with other types of social media metric data sources, to unravel the broadness and speed of Twitter presence of scientific papers. Then, focusing on scholarly tweets of scientific papers per se, this dissertation investigates the characteristics of diverse user interaction behaviors around scholarly tweets, shedding light on their potential value in developing more advanced indicators for measuring deeper levels of Twitter reception of scientific information. Finally, based on the main findings, this dissertation further discusses the possibility to approach a more fine-grained indicator system of scholarly Twitter metrics. Show less
This study investigated the stability of Twitter counts of scientific publications over time. For this, we conducted an analysis of the availability statuses of over 2.6 million Twitter mentions... Show moreThis study investigated the stability of Twitter counts of scientific publications over time. For this, we conducted an analysis of the availability statuses of over 2.6 million Twitter mentions received by the 1,154 most tweeted scientific publications recorded by Altmetric.com up to October 2017. The results show that of the Twitter mentions for these highly tweeted publications, about 14.3% had become unavailable by April 2019. Deletion of tweets by users is the main reason for unavailability, followed by suspension and protection of Twitter user accounts. This study proposes two measures for describing the Twitter dissemination structures of publications: Degree of Originality (i.e., the proportion of original tweets received by an article) and Degree of Concentration (i.e., the degree to which retweets concentrate on a single original tweet). Twitter metrics of publications with relatively low Degree of Originality and relatively high Degree of Concentration were observed to be at greater risk of becoming unstable due to the potential disappearance of their Twitter mentions. In light of these results, we emphasize the importance of paying attention to the potential risk of unstable Twitter counts, and the significance of identifying the different Twitter dissemination structures when studying the Twitter metrics of scientific publications. Show less
Huang, T.; Wang, T.A.; Zheng, Y.; Ellervik, C.; Li, X.; Gao, M.; ... ; BIRTH-GENE BIG StudyWorking Grp 2019
IMPORTANCE Observational studies have shown associations of birth weight with type 2 diabetes (T2D) and glycemic traits, but it remains unclear whether these associations represent causal... Show moreIMPORTANCE Observational studies have shown associations of birth weight with type 2 diabetes (T2D) and glycemic traits, but it remains unclear whether these associations represent causal associations.OBJECTIVE To test the association of birth weight with T2D and glycemic traits using a mendelian randomization analysis.DESIGN, SETTING, AND PARTICIPANTS This mendelian randomization study used a genetic risk score for birth weight that was constructed with 7 genome-wide significant single-nucleotide polymorphisms. The associations of this score with birth weight and T2D were tested in a mendelian randomization analysis using study-level data. The association of birth weight with T2D was tested using both study-level data (7 single-nucleotide polymorphisms were used as an instrumental variable) and summary-level data from the consortia (43 single-nucleotide polymorphismswere used as an instrumental variable). Data from 180 056 participants from 49 studies were included.MAIN OUTCOMES AND MEASURES Type 2 diabetes and glycemic traits.RESULTS This mendelian randomization analysis included 49 studies with 41 155 patients with T2D and 80 008 control participants from study-level data and 34 840 patients with T2D and 114 981 control participants from summary-level data. Study-level data showed that a 1-SD decrease in birth weight due to the genetic risk score was associated with higher risk of T2D among all participants (odds ratio [OR], 2.10; 95% CI, 1.69-2.61; P=4.03 x 10-5), among European participants (OR, 1.96; 95% CI, 1.42-2.71; P=.04), and among East Asian participants (OR, 1.39; 95% CI, 1.18-1.62; P=.04). Similar results were observed from summary-level analyses. In addition, each 1-SD lower birth weight was associated with 0.189 SD higher fasting glucose concentration (beta=0.189; SE=0.060; P=.002), but not with fasting insulin, 2-hour glucose, or hemoglobin A1c concentration.CONCLUSIONS AND RELEVANCE In this study, a genetic predisposition to lower birth weight was associated with increased risk of T2D and higher fasting glucose concentration, suggesting genetic effects on retarded fetal growth and increased diabetes risk that either are independent of each other or operate through alterations of integrated biological mechanisms. Show less
In this study the velocity of 12 Altmetric.com data sources in disseminating newly published research outputs is investigated. The Velocity Index is proposed to make a comparison of velocity among... Show moreIn this study the velocity of 12 Altmetric.com data sources in disseminating newly published research outputs is investigated. The Velocity Index is proposed to make a comparison of velocity among Altmetric.com data sources across document types and subject fields. Some altmetric posts accumulated very fast within the first few days after publication, such as Reddit, Twitter, News, and Facebook, while posts of Policy documents, Wikipedia, Q&A, and Peer review with low Velocity Index values accrued relatively slowly. Most data sources’ velocity degree also change by document types and subject fields. The velocity of most data sources confronted with the type of Review is lower than the overall and Article, while Editorial Material and Letter are higher. In general, most altmetric data sources show higher velocity values in the fields of Multidisciplinary Journals and Natural Sciences. Show less
