Correlation between Twitter mentions and academic citations in sexual medicine journals | International Journal of Impotence Research

Abstract:  Social media services, especially Twitter, are used as a commonly sharing tool in the scientific world. This widespread use of Twitter would be an effective method in spreading academic publications. So, we aimed to investigate the relationship between Twitter mentions and traditional citations of articles in sexual medicine journals in this study. We reviewed the articles published in seven journals of sexual medicine (2 years after the publication of the articles) between January 2018 and June 2018. In the first half of 2018, 410 articles were extracted. Of these, 352 (85.9%) were original articles, while 58 (14.1%) were review articles. The median number of citations of the articles mentioned at least once on Twitter was 7 (interquartile range: 0–111) for Google Scholar, whereas it was 0 (interquartile range: 0–63) for Scopus, respectively. It was 4 (interquartile range: 0–25) for Google Scholar and 0 (interquartile range: 0–7) for Scopus. The publications mentioned on Twitter were cited more than the non-mentioned publications in the traditional-based citation system (p?<?0.001). A significant relationship between the citation numbers and tweet numbers was also observed (p?<?0.001). Also, in the linear regression model, the tweet numbers (p?<?0.001) and article types (p?<?0.001) were found to be related to the Google Scholar citation numbers. In conclusion, using Twitter as a professional tool in academic life would allow information to be propagated and responded quickly, especially for sexual medicine journals.

 

Altmetric Score Has a Stronger Relationship With Article Citations Than Journal Impact Factor and Open Access Status: A Cross-Sectional Analysis of 4,022 Sports Science Articles | Journal of Orthopaedic & Sports Physical Therapy

Abstract:  Objective

To assess the relationship of individual article citations in the Sport Sciences field to (i) journal impact factor; (ii) each article’s open access status; and (iii) Altmetric score components.

 

Design

Cross-sectional.

 

Methods

We searched the ISI Web of Knowledge InCites Journal Citation Reports database “Sport Sciences” category for the 20 journals with the highest 2-year impact factor in 2018. We extracted the impact factor for each journal and each article’s open access status (yes or no). Between September 2019 and February 2020, we obtained individual citations, Altmetric scores and details of Altmetric components (e.g. number of tweets, Facebook posts, etc.) for each article published in 2017. Linear and multiple regression models were used to assess the relationship between the dependent variable (citation number) and the independent variables article Altmetric score and open access status, and journal impact factor.

 

Results

4,022 articles were included. Total Altmetric score, journal impact factor and open access status, respectively explained 32%, 14%, and 1% of the variance in article citations (when combined, the variables explained 40% of the variance in article citations). The number of tweets related to an article was the Altmetric component that explained the highest proportion of article citations (37%).

 

Conclusion

Altmetric scores in Sports Sciences journals have a stronger relationship with number of citations than does journal impact factor or open access status. Twitter may be the best social media platform to promote a research article as it has a strong relationship with article citations.

Optimizing the use of twitter for research dissemination: The “Three Facts and a Story” Randomized-Controlled Trial – Journal of Hepatology

Abstract:  Background

Published research promoted on twitter reaches more readers. Tweets with graphics are more engaging than those without. Data are limited, however, regarding how to optimize a multimedia tweets for engagement

Methods

The “Three facts and a Story” trial is a randomized-controlled trial comparing a tweet featuring a graphical abstract to paired tweets featuring the personal motivations behind the research and a summary of the findings. Fifty-four studies published by the Journal of Hepatology were randomized at the time of online publication. The primary endpoint was assessed at 28-days from online publication with a primary outcome of full-text downloads from the website. Secondary outcomes included page views and twitter engagement including impressions, likes, and retweets.

Results

Overall, 31 studies received standard tweets and 23 received story tweets. Five studies were randomized to story tweets but crossed over to standard tweets for lack of author participation. Most papers tweeted were original articles (94% standard, 91% story) and clinical topics (55% standard, 61% story). Story tweets were associated with a significant increase in the number of full text downloads, 51 (34-71) versus 25 (13-41), p=0.002. There was also a non-significant increase in the number of page views. Story tweets generated an average of >1,000 more impressions than standard tweets (5,388 vs 4,280, p=0.002). Story tweets were associated with a similar number of retweets, and a non-significant increase in the number of likes.

Conclusion

Tweets featuring the authors and their motivations may increase engagement with published research.

WILL PODCASTING AND SOCIAL MEDIA REPLACE JOURNALS AND TRADITIONAL SCIENCE COMMUNICATION? NO, BUT… | American Journal of Epidemiology | Oxford Academic

Abstract:  The digital world in which we live is changing rapidly. The changing media environment is having a direct impact on traditional forms of communication and knowledge translation in public health and epidemiology. Openly accessible digital media can be used to reach a broader and more diverse audience of trainees, scientists, and the lay public than traditional forms of scientific communication. The new digital landscape for delivering content is vast and new platforms are continuously being added. We focus on several, including Twitter and podcasting and discuss their relevance to epidemiology and science communication. We highlight three key reasons why we think epidemiologists should be engaging with these mediums: 1) science communication, 2) career advancement, 3) development of a community and public service. Other positive and negative consequences of engaging in these forms of new media are also discussed. The authors of this commentary are all engaged in social media and podcasting for scientific communication and in this manuscript, we reflect on our experience with these mediums as tools to advance the field of epidemiology.

 

Can altmetric mentions predict later citations? A test of validity on data from ResearchGate and three social media platforms | Emerald Insight

Abstract:  Purpose

The main purpose of this study is to explore and validate the question “whether altmetric mentions can predict citations to scholarly articles”. The paper attempts to explore the nature and degree of correlation between altmetrics (from ResearchGate and three social media platforms) and citations.

Design/methodology/approach

A large size data sample of scholarly articles published from India for the year 2016 is obtained from the Web of Science database and the corresponding altmetric data are obtained from ResearchGate and three social media platforms (Twitter, Facebook and blog through Altmetric.com aggregator). Correlations are computed between early altmetric mentions and later citation counts, for data grouped in different disciplinary groups.

Findings

Results show that the correlation between altmetric mentions and citation counts are positive, but weak. Correlations are relatively higher in the case of data from ResearchGate as compared to the data from the three social media platforms. Further, significant disciplinary differences are observed in the degree of correlations between altmetrics and citations.

Research limitations/implications

The results support the idea that altmetrics do not necessarily reflect the same kind of impact as citations. However, articles that get higher altmetric attention early may actually have a slight citation advantage. Further, altmetrics from academic social networks like ResearchGate are more correlated with citations, as compared to social media platforms.

Originality/value

The paper has novelty in two respects. First, it takes altmetric data for a window of about 1–1.5 years after the article publication and citation counts for a longer citation window of about 3–4 years after the publication of article. Second, it is one of the first studies to analyze data from the ResearchGate platform, a popular academic social network, to understand the type and degree of correlations.

How is science clicked on Twitter? Click metrics for Bitly short links to scientific publications – Fang – – Journal of the Association for Information Science and Technology – Wiley Online Library

Abstract:  To provide some context for the potential engagement behavior of Twitter users around science, this article investigates how Bitly short links to scientific publications embedded in scholarly Twitter mentions are clicked on Twitter. Based on the click metrics of over 1.1 million Bitly short links referring to Web of Science (WoS) publications, our results show that around 49.5% of them were not clicked by Twitter users. For those Bitly short links with clicks from Twitter, the majority of their Twitter clicks accumulated within a short period of time after they were first tweeted. Bitly short links to the publications in the field of Social Sciences and Humanities tend to attract more clicks from Twitter over other subject fields. This article also assesses the extent to which Twitter clicks are correlated with some other impact indicators. Twitter clicks are weakly correlated with scholarly impact indicators (WoS citations and Mendeley readers), but moderately correlated to other Twitter engagement indicators (total retweets and total likes). In light of these results, we highlight the importance of paying more attention to the click metrics of URLs in scholarly Twitter mentions, to improve our understanding about the more effective dissemination and reception of science information on Twitter.

 

How is science clicked on Twitter? Click metrics for Bitly short links to scientific publications – Fang – – Journal of the Association for Information Science and Technology – Wiley Online Library

Abstract:  To provide some context for the potential engagement behavior of Twitter users around science, this article investigates how Bitly short links to scientific publications embedded in scholarly Twitter mentions are clicked on Twitter. Based on the click metrics of over 1.1 million Bitly short links referring to Web of Science (WoS) publications, our results show that around 49.5% of them were not clicked by Twitter users. For those Bitly short links with clicks from Twitter, the majority of their Twitter clicks accumulated within a short period of time after they were first tweeted. Bitly short links to the publications in the field of Social Sciences and Humanities tend to attract more clicks from Twitter over other subject fields. This article also assesses the extent to which Twitter clicks are correlated with some other impact indicators. Twitter clicks are weakly correlated with scholarly impact indicators (WoS citations and Mendeley readers), but moderately correlated to other Twitter engagement indicators (total retweets and total likes). In light of these results, we highlight the importance of paying more attention to the click metrics of URLs in scholarly Twitter mentions, to improve our understanding about the more effective dissemination and reception of science information on Twitter.

 

Early Indicators of Scientific Impact: Predicting Citations with Altmetrics

Abstract:  Identifying important scholarly literature at an early stage is vital to the academic research community and other stakeholders such as technology companies and government bodies. Due to the sheer amount of research published and the growth of ever-changing interdisciplinary areas, researchers need an efficient way to identify important scholarly work. The number of citations a given research publication has accrued has been used for this purpose, but these take time to occur and longer to accumulate. In this article, we use altmetrics to predict the short-term and long-term citations that a scholarly publication could receive. We build various classification and regression models and evaluate their performance, finding neural networks and ensemble models to perform best for these tasks. We also find that Mendeley readership is the most important factor in predicting the early citations, followed by other factors such as the academic status of the readers (e.g., student, postdoc, professor), followers on Twitter, online post length, author count, and the number of mentions on Twitter, Wikipedia, and across different countries.

 

Sci-Hub Founder Criticises Sudden Twitter Ban Over Over “Counterfeit” Content * TorrentFreak

“Twitter has suspended the account of Sci-Hub, a site that offers a free gateway to paywalled research. The site is accused of violating the counterfeit policy of the social media platform. However, founder Alexandra Elbakyan believes that this is an effort to silence the growing support amidst a high profile court case in India.”

A communication strategy based on Twitter improves article citation rate and impact factor of medical journals – ScienceDirect

[Note even an abstract is OA.] 

“Medical journals use Twitter to optimise their visibility on the scientific community. It is by far the most used social media to share publications, since more than 20% of published articles receive at least one announcement on Twitter (compared to less than 5% of notifications on other social networks) [5] . It was initially described that, within a medical specialty, journals with a Twitter account have a higher impact factor than others and that the number of followers is correlated to the impact factor of the journal [67] . Several observational works showed that the announcement of a medical article publication on Twitter was strongly associated with its citation rate in the following years 891011 . In 2015, among anaesthesia journals, journals with an active and influential Twitter account had an higher journal impact factor and a greater number of article citations than those not embracing social media [12] . A meta-analysis of July 2020 concluded that the presence of an article on social media was probably associated with a higher number of citations [13] . Finally, two randomised studies, published in 2020 and not included in this meta-analysis, also showed that, for a given journal, articles that benefited from exposure on Twitter were 1.5 to 9 times more cited in the year following publication than articles randomised in the “no tweeting” group [1415] 

The majority of these works have only been published very recently and the strategy for using Twitter to optimise the number of citations is now a challenge for all medical journals. Several retrospective studies have looked at the impact of the use of a social media communication strategy by medical journals. They have shown that the introduction of Twitter to communicate as part of this strategy was associated with a higher number of articles consulted, a higher number of citations and shorter delays in citation after publication [1617] . Two studies (including one on anaesthesia journals) showed that journals that used a Twitter account to communicate were more likely to increase their impact factor than those that did not [1218] . Some researchers even suggest that the dissemination of medical information through social media, allowing quick and easy access after the peer-review publication process, may supplant the classical academic medical literature in the future [19] . This evolution has led to the creation of a new type of Editor in several medical journal editorial boards: the social media Editor (sometimes with the creation of a “specialised social media team” to assist him or her) [20] . This medical Editor shares, across a range of social media platforms, new journal articles with the aim of improving dissemination of journal content. Thus, beyond the scientific interest of a given article, which determines its chances of being cited, there is currently a parallel Editorial work consisting in optimising the visibility on Twitter to increase the number of citations and improve the impact factor. Some authors also start to focus on the best techniques for using Twitter and on the best ways to tweet to optimise communication, for example during a medical congress [21] ….”

 

Sharing is caring: an analysis of #FOAMed Twitter posts during the COVID-19 pandemic | Postgraduate Medical Journal

Abstract:  Purpose Free Open Access Medical Education (FOAMed) is a worldwide social media movement designed to accelerate and democratise the sharing of medical knowledge. This study sought to investigate the content shared through FOAMed during the emerging COVID-19 pandemic.

Study design Tweets containing the #FOAMed hashtag posted during a 24-hour period in April 2020 were studied. Included tweets were analysed using the Wiig knowledge management cycle framework (building knowledge, holding knowledge, pooling knowledge and using knowledge).

Results 1379 tweets contained the #FOAMed hashtag, of which 265 met the inclusion criteria and were included in the analysis. Included tweets were posted from 208 distinct users, originated from each world continent and were in five different languages. Three overarching themes were identified: (1) signposting and appraising evidence and guidelines; (2) sharing specialist and technical advice; and (3) personal and social engagement. Among 12 subthemes within these groupings, 11 aligned to one of the four dimensions of the Wiig knowledge management cycle framework, and the other focused on building and managing social networks. Almost 40% of tweets related directly to COVID-19.

Conclusion #FOAMed tweets during the COVID-19 pandemic included a broad range of resources, advice and support. Despite the geographical, language and disciplinary variation of contributing users and the lack of organisational structure uniting them, this social media medical community has been able to construct, share and use emerging technical knowledge through a time of extraordinary challenge and uncertainty for the global medical community.

This article is made freely available for use in accordance with BMJ’s website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.

[2011.11940] Preprints as accelerator of scholarly communication: An empirical analysis in Mathematics

Abstract:  In this study we analyse the key driving factors of preprints in enhancing scholarly communication. To this end we use four groups of metrics, one referring to scholarly communication and based on bibliometric indicators (Web of Science and Scopus citations), while the others reflect usage (usage counts in Web of Science), capture (Mendeley readers) and social media attention (Tweets). Hereby we measure two effects associated with preprint publishing: publication delay and impact. We define and use several indicators to assess the impact of journal articles with previous preprint versions in arXiv. In particular, the indicators measure several times characterizing the process of arXiv preprints publishing and the reviewing process of the journal versions, and the ageing patterns of citations to preprints. In addition, we compare the observed patterns between preprints and non-OA articles without any previous preprint versions in arXiv. We could observe that the “early-view” and “open-access” effects of preprints contribute to a measurable citation and readership advantage of preprints. Articles with preprint versions are more likely to be mentioned in social media and have shorter Altmetric attention delay. Usage and capture prove to have only moderate but stronger correlation with citations than Tweets. The different slopes of the regression lines between the different indicators reflect different order of magnitude of usage, capture and citation data.

 

citizenscience, Twitter, 11/5/2020 4:27:37 AM, 239488

“The graph represents a network of 3,914 Twitter users whose tweets in the requested range contained “citizenscience”, or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 05 November 2020 at 04:07 UTC.

The requested start date was Thursday, 05 November 2020 at 01:01 UTC and the maximum number of days (going backward) was 14.

The maximum number of tweets collected was 7,500.

The tweets in the network were tweeted over the 13-day, 18-hour, 29-minute period from Thursday, 22 October 2020 at 01:42 UTC to Wednesday, 04 November 2020 at 20:11 UTC.

Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.

There is an edge for each “replies-to” relationship in a tweet, an edge for each “mentions” relationship in a tweet, and a self-loop edge for each tweet that is not a “replies-to” or “mentions”.

The graph is directed.

The graph’s vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.

The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm….”