[2011.09079] Do ‘altmetric mentions’ follow Power Laws? Evidence from social media mention data in Altmetric.com

Abstract:  Power laws are a characteristic distribution that are ubiquitous, in that they are found almost everywhere, in both natural as well as in man-made systems. They tend to emerge in large, connected and self-organizing systems, for example, scholarly publications. Citations to scientific papers have been found to follow a power law, i.e., the number of papers having a certain level of citation x are proportional to x raised to some negative power. The distributional character of altmetrics has not been studied yet as altmetrics are among the newest indicators related to scholarly publications. Here we select a data sample from the altmetrics aggregator this http URL containing records from the platforms Facebook, Twitter, News, Blogs, etc., and the composite variable Alt-score for the period 2016. The individual and the composite data series of ‘mentions’ on the various platforms are fit to a power law distribution, and the parameters and goodness of fit determined using least squares regression. The log-log plot of the data, ‘mentions’ vs. number of papers, falls on an approximately linear line, suggesting the plausibility of a power law distribution. The fit is not very good in all cases due to large fluctuations in the tail. We show that fit to the power law can be improved by truncating the data series to eliminate large fluctuations in the tail. We conclude that altmetric distributions also follow power laws with a fairly good fit over a wide range of values. More rigorous methods of determination may not be necessary at present.

 

Full article: Pharmaceutical industry-authored preprints: scientific and social media impact

Abstract

Aim: Non–peer-reviewed manuscripts posted as preprints can be cited in peer-reviewed articles which has both merits and demerits. International Committee of Medical Journal Editors guidelines mandate authors to declare preprints at the time of manuscript submission. We evaluated the trends in pharma-authored research published as preprints and their scientific and social media impact by analyzing citation rates and altmetrics.

Research design and methods: We searched EuroPMC, PrePubMed bioRxiv and MedRxiv for preprints submitted by authors affiliated with the top 50 pharmaceutical companies from inception till June 15, 2020. Data were extracted and analyzed from the search results. The number of citations for the preprint and peer-reviewed versions (if available) were compiled using the Publish or Perish software (version 1.7). Altmetric score was calculated using the “Altmetric it” online tool. Statistical significance was analyzed by Wilcoxon rank-sum test.

Results: A total of 498 preprints were identified across bioRxiv (83%), PeerJ (5%), F1000Research (6%), Nature Proceedings (3%), Preprint.org (3%), Wellcome Open Research preprint (0.2%) and MedRxiv (0.2%) servers. Roche, Sanofi and Novartis contributed 56% of the retrieved preprints. The median number of citations for the included preprints was 0 (IQR =1, Min-Max =0-45). The median number of citations for the published preprints and unpublished preprints was 0 for both (IQR =1, Min-Max =0-25 and IQR =1, Min-Max =0-45, respectively; P?=?.091). The median Altmetric score of the preprints was 4 (IQR =10.5, Min-Max =0-160).

Conclusion: Pharma-authored research is being increasingly published as preprints and is also being cited in other peer-reviewed publications and discussed in social media.

An altmetric attention advantage for open access books in the humanities and social sciences | SpringerLink

Abstract:  The last decade has seen two significant phenomena emerge in research communication: the rise of open access (OA) publishing, and the easy availability of evidence of online sharing in the form of altmetrics. There has been limited examination of the effect of OA on online sharing for journal articles, and little for books. This paper examines the altmetrics of a set of 32,222 books (of which 5% are OA) and a set of 220,527 chapters (of which 7% are OA) indexed by the scholarly database Dimensions in the Social Sciences and Humanities. Both OA books and chapters have significantly higher use on social networks, higher coverage in the mass media and blogs, and evidence of higher rates of social impact in policy documents. OA chapters have higher rates of coverage on Wikipedia than their non-OA equivalents, and are more likely to be shared on Mendeley. Even within the Humanities and Social Sciences, disciplinary differences in altmetric activity are evident. The effect is confirmed for chapters, although sampling issues prevent the strong conclusion that OA facilitates extra attention at the whole book level, the apparent OA altmetrics advantage suggests that the move towards OA is increasing social sharing and broader impact.

 

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.

Limit Less in social media | Institute of Physics

“The use of social media is now a common part of most of our lives. It’s not just young people who are consuming more social media content. Their parents and friends are increasing their consumption too.

Social media – just like traditional media – shapes our ideas and influences the decisions we make.

This is why it’s important that the physics-related content on social media platforms informs rather than misleads. It should challenge stereotypes rather than perpetuate them. We must also ensure that the people talking about physics on social media platforms are more diverse….

What the IOP wants to see

Social media platforms should actively promote accurate physics-based content that represents a more diverse range of physicists.
Social media must decouple genuine physics content from fake news and conspiracy theories.
Social media influencers should support our campaign by working with a diverse range of physicists to promote their content.
The IOP wants more physicists in industry and academia to become active in social media, demonstrating more diversity.
More people who studied physics and have pursued other careers should use social media to tell people about the opportunities that were opened up to them by studying physics.
Companies should encourage and support their employees who are physicists to take an active role in engaging the public through social media.
Social media users are provided with tools to identify bad physics content and to challenge it on different platforms.”

 

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.

 

The financial market of ideas: A theory of academic social media – Alessandro Delfanti, 2020

Abstract:  Millions of scholars use academic social media to share their work and construct themselves as legitimate and productive workers. An analysis of Academia.edu updates ideas about science as a ‘marketplace of ideas’. Scholarly communication via social media is best conceptualized as a ‘financial market of ideas’ through which academic value is assigned to publications and researchers. Academic social media allow for the inclusion of scholarly objects such as preprint articles, which exceed traditional accounting systems in scholarly communication. Their functioning is based on a valorization of derived qualities, as their algorithms analyze social interactions on the platform rather than the content of scholarship. They are also oriented toward the future in their use of data analytics to predict research outcomes.