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.

 

#HashtagActivism | The MIT Press

“How marginalized groups use Twitter to advance counter-narratives, preempt political spin, and build diverse networks of dissent.

Read the full book for free on MIT Press Direct: https://doi.org/10.7551/mitpress/10858.001.0001

The power of hashtag activism became clear in 2011, when #IranElection served as an organizing tool for Iranians protesting a disputed election and offered a global audience a front-row seat to a nascent revolution. Since then, activists have used a variety of hashtags, including #JusticeForTrayvon, #BlackLivesMatter, #YesAllWomen, and #MeToo to advocate, mobilize, and communicate. In this book, Sarah Jackson, Moya Bailey, and Brooke Foucault Welles explore how and why Twitter has become an important platform for historically disenfranchised populations, including Black Americans, women, and transgender people. They show how marginalized groups, long excluded from elite media spaces, have used Twitter hashtags to advance counternarratives, preempt political spin, and build diverse networks of dissent.

The authors describe how such hashtags as #MeToo, #SurvivorPrivilege, and #WhyIStayed have challenged the conventional understanding of gendered violence; examine the voices and narratives of Black feminism enabled by #FastTailedGirls, #YouOKSis, and #SayHerName; and explore the creation and use of #GirlsLikeUs, a network of transgender women. They investigate the digital signatures of the “new civil rights movement”—the online activism, storytelling, and strategy-building that set the stage for #BlackLivesMatter—and recount the spread of racial justice hashtags after the killing of Michael Brown in Ferguson, Missouri, and other high-profile incidents of killings by police. Finally, they consider hashtag created by allies, including #AllMenCan and #CrimingWhileWhite….”

Why not use “Twitter” of core clinical journals for rapid dissemination of medical information during the COVID-19 pandemic? | SpringerLink

“We have informed the residents at our institution to share information from well-known scientific journals, such as New England Journal of Medicine, Lancet, Journal of the American Medical Association, British Medical Journal, American Journal of Obstetrics and Gynecology, Obstetrics and Gynecology, and BJOG: An International Journal of Obstetrics and Gynaecology on Twitter. In this way, residents can not only obtain information on COVID-19, but also benefit from discussions on the latest medical advances and learn more general information in the field….”

Why not use “Twitter” of core clinical journals for rapid dissemination of medical information during the COVID-19 pandemic? | SpringerLink

“We have informed the residents at our institution to share information from well-known scientific journals, such as New England Journal of Medicine, Lancet, Journal of the American Medical Association, British Medical Journal, American Journal of Obstetrics and Gynecology, Obstetrics and Gynecology, and BJOG: An International Journal of Obstetrics and Gynaecology on Twitter. In this way, residents can not only obtain information on COVID-19, but also benefit from discussions on the latest medical advances and learn more general information in the field….”

In the open: TXTmob and Twitter · Commonplace

“For our first case study, we will look into the collaborative roots of Twitter in the open source code of TXTmob. We foreground this retrospective glance with an original account of its creation by TXTmob founder Tad Hirsch and an excerpt from Sasha Costanza-Chock’s Design Justice (The MIT Press, 2020), which you can purchase here, or read the OA edition here….” 

Does Tweeting Improve Citations? One-Year Results from the TSSMN Prospective Randomized Trial – ScienceDirect

Abstract:  Background

The Thoracic Surgery Social Media Network (TSSMN) is a collaborative effort of leading journals in cardiothoracic surgery to highlight publications via social media. This study aims to evaluate the 1-year results of a prospective randomized social media trial to determine the effect of tweeting on subsequent citations and non-traditional bibliometrics.

Methods

A total of 112 representative original articles were randomized 1:1 to be tweeted via TSSMN or a control (non-tweeted) group. Measured endpoints included citations at 1-year compared to baseline, as well as article-level metrics (Altmetric score) and Twitter analytics. Independent predictors of citations were identified through univariable and multivariable regression analyses.

Results

When compared to control articles, tweeted articles achieved significantly greater increase in Altmetric scores (Tweeted 9.4±5.8 vs. Non-Tweeted 1.0±1.8, p<0.001), Altmetric score percentiles relative to articles of similar age from each respective journal (Tweeted 76.0±9.1%ile vs. Non-Tweeted 13.8±22.7%ile, p<0.001), with greater change in citations at 1 year (Tweeted +3.1±2.4 vs. Non-Tweeted +0.7±1.3, p<0.001). Multivariable analysis showed that independent predictors of citations were randomization to tweeting (OR 9.50; 95%CI 3.30-27.35, p<0.001), Altmetric score (OR 1.32; 95%CI 1.15-1.50, p<0.001), open-access status (OR 1.56; 95%CI 1.21-1.78, p<0.001), and exposure to a larger number of Twitter followers as quantified by impressions (OR 1.30, 95%CI 1.10-1.49, p<0.001).

Conclusions

One-year follow-up of this TSSMN prospective randomized trial importantly demonstrates that tweeting results in significantly more article citations over time, highlighting the durable scholarly impact of social media activity.