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….”

If I tweet will you cite later? Follow-up on the effect of social media exposure on article downloads and citations | SpringerLink

Abstract:  Objectives

We previously reported that random assignment of scientific articles to a social media exposure intervention did not have an effect on article downloads and citations. In this paper, we investigate whether longer observation time after exposure to a social media intervention has altered the previously reported results.

Methods

For articles published in the International Journal of Public Health between December 2012 and December 2014, we updated article download and citation data for a minimum of 24-month follow-up. We re-analysed the effect of social media exposure on article downloads and citations.

Results

There was no difference between intervention and control group in terms of downloads (p?=?0.72) and citations (p=?0.30) for all papers and when we stratified by open access status.

Conclusions

Longer observation time did not increase the relative differences in the numbers of downloads and citations between papers in the social media intervention group and papers in the control group. Traditional impact metrics based on citations, such as impact factor, may not capture the added value of social media for scientific publications.

Could early tweet counts predict later citation counts? A gender study in Life Sciences and Biomedicine (2014–2016)

Abstract:  In this study, it was investigated whether early tweets counts could differentially benefit female and male (first, last) authors in terms of the later citation counts received. The data for this study comprised 47,961 articles in the research area of Life Sciences & Biomedicine from 2014–2016, retrieved from Web of Science’s Medline. For each article, the number of received citations per year was downloaded from WOS, while the number of received tweets per year was obtained from PlumX. Using the hurdle regression model, I compared the number of received citations by female and male (first, last) authored papers and then I investigated whether early tweet counts could predict the later citation counts received by female and male (first, last) authored papers. In the regression models, I controlled for several important factors that were investigated in previous research in relation to citation counts, gender or Altmetrics. These included journal impact (SNIP), number of authors, open access, research funding, topic of an article, international collaboration, lay summary, F1000 Score and mega journal. The findings showed that the percentage of papers with male authors in first or last authorship positions was higher than that for female authors. However, female first and last-authored papers had a small but significant citation advantage of 4.7% and 5.5% compared to male-authored papers. The findings also showed that irrespective of whether the factors were included in regression models or not, early tweet counts had a weak positive and significant association with the later citations counts (3.3%) and the probability of a paper being cited (21.1%). Regarding gender, the findings showed that when all variables were controlled, female (first, last) authored papers had a small citation advantage of 3.7% and 4.2% in comparison to the male authored papers for the same number of tweets.