A systematic identification and analysis of scientists on Twitter

Authors : Qing Ke, Yong-Yeol Ahn, Cassidy R. Sugimoto

Metrics derived from Twitter and other social media—often referred to as altmetrics—are increasingly used to estimate the broader social impacts of scholarship. Such efforts, however, may produce highly misleading results, as the entities that participate in conversations about science on these platforms are largely unknown.

For instance, if altmetric activities are generated mainly by scientists, does it really capture broader social impacts of science? Here we present a systematic approach to identifying and analyzing scientists on Twitter.

Our method can identify scientists across many disciplines, without relying on external bibliographic data, and be easily adapted to identify other stakeholder groups in science.

We investigate the demographics, sharing behaviors, and interconnectivity of the identified scientists.

We find that Twitter has been employed by scholars across the disciplinary spectrum, with an over-representation of social and computer and information scientists; under-representation of mathematical, physical, and life scientists; and a better representation of women compared to scholarly publishing.

Analysis of the sharing of URLs reveals a distinct imprint of scholarly sites, yet only a small fraction of shared URLs are science-related. We find an assortative mixing with respect to disciplines in the networks between scientists, suggesting the maintenance of disciplinary walls in social media.

Our work contributes to the literature both methodologically and conceptually—we provide new methods for disambiguating and identifying particular actors on social media and describing the behaviors of scientists, thus providing foundational information for the construction and use of indicators on the basis of social media metrics.

 URL : A systematic identification and analysis of scientists on Twitter

DOI : https://doi.org/10.1371/journal.pone.0175368

“Facebook for Academics”: The Convergence of Self-Branding and Social Media Logic on Academia.edu

Authors : Brooke Erin Duffy, Jefferson Pooley

Given widespread labor market precarity, contemporary workers—especially those in the media and creative industries—are increasingly called upon to brand themselves. Academics, we contend, are experiencing a parallel pressure to engage in self-promotional practices, particularly as universities become progressively more market-driven.

Academia.edu, a paper-sharing social network that has been informally dubbed “Facebook for academics,” has grown rapidly by adopting many of the conventions of popular social media sites.

This article argues that the astonishing uptake of Academia.edu both reflects and amplifies the self-branding imperatives that many academics experience. Drawing on Academia.edu’s corporate history, design decisions, and marketing communications, we analyze two overlapping facets of Academia.edu: (1) the site’s business model and (2) its social affordances.

We contend that the company, like mainstream social networks, harnesses the content and immaterial labor of users under the guise of “sharing.” In addition, the site’s fixation on analytics reinforces a culture of incessant self-monitoring—one already encouraged by university policies to measure quantifiable impact.

We conclude by identifying the stakes for academic life, when entrepreneurial and self-promotional demands brush up against the university’s knowledge-making ideals.

URL : “Facebook for Academics”: The Convergence of Self-Branding and Social Media Logic on Academia.edu

DOI : https://hcommons.org/deposits/item/hc:11561

Interroger le texte scientifique

Auteur/Author : Guillaume Cabanac

Les documents textuels sont des vecteurs d’information familiers et incontournables de notre société de l’information. Avec l’essor des plateformes numériques et des médias sociaux, le texte se décline désormais en pages web, billets de blogs, commentaires, tweets et tags, entre autres. Auparavant consommateurs passifs, les lecteurs se muent à leur tour en producteurs de contenus.

En résultent des échanges interpersonnels qui tissent des réseaux sociaux numériques s’étendant bien au-delà de nos cercles relationnels. Dans ce contexte, nature et format des textes, intentions de leurs auteurs (informer, rediffuser, critiquer, compléter, corriger, etc.), contexte spatio-temporel ainsi que véracité et fraîcheur variables des informations sont autant de subtilités à intégrer dans les modèles de recherche d’information.

La première partie de ce mémoire présente une synthèse de résultats en recherche d’information visant à modéliser ces facteurs pour améliorer la pertinence des recherches sur des corpus textuels, notamment issus de médias sociaux.

Le programme de recherche que je développe vise également à « interroger le texte » pour révéler des informations au sujet de son contenu, de ses auteurs et de ses lecteurs. Le texte scientifique a été choisi comme cible pour la richesse de son contenu et de ses méta- données. Ainsi, la deuxième partie du mémoire synthétise des résultats en scientométrie, terme désignant l’étude quantitative des sciences et de l’innovation.

Il s’est agi de questionner des textes scientifiques et les réseaux sous-jacents (lexique, références, auteurs, institutions, etc.) pour faire émerger des connaissances à forte valeur ajoutée et apporter un éclairage sur la création et la diffusion des savoirs scientifiques.

Les deux volets articulés dans ce mémoire concourent à définir un programme de recherche interdisciplinaire à la croisée de l’informatique, la scientométrie et la sociologie des sciences.

Son ambition consiste à interroger le texte scientifique pour en améliorer l’accès (via la recherche d’information) tout en contribuant à éliciter les ressorts de la genèse et de l’évolution des mondes sociaux et des savoirs en sciences (via la scientométrie).

URL : Interroger le texte scientifique

Alternative location : https://tel.archives-ouvertes.fr/tel-01413878/

Science and Facebook: the same popularity law!

Authors : Zoltán Néda, Levente Varga, Tamás S. Biró

The distribution of scientific citations for publications selected with different rules (author, topic, institution, country, journal, etc.) collapse on a single curve if one plots the citations relative to their mean value.

We find that the distribution of shares for the Facebook posts re-scale in the same manner to the very same curve with scientific citations. This finding suggests that citations are subjected to the same growth mechanism with Facebook popularity measures, being influenced by a statistically similar social environment and selection mechanism.

In a simple master-equation approach the exponential growth of the number of publications and a preferential selection mechanism leads to a Tsallis-Pareto distribution offering an excellent description for the observed statistics.

Based on our model and on the data derived from PubMed we predict that according to the present trend the average citations per scientific publications exponentially relaxes to about 4.

URL : https://arxiv.org/abs/1701.05347

Tracking the Digital Footprints to Scholarly Articles from Social Media

Authors : Xianwen Wang, Zhichao Fang, Xinhui Guo

Scholarly articles are discussed and shared on social media, which generates altmetrics. On the opposite side, what is the impact of social media on the dissemination of scholarly articles and how to measure it? What are the visiting patterns?

Investigating these issues, the purpose of this study is to seek a solution to fill the research gap, specifically, to explore the dynamic visiting patterns directed by social media, and examine the effects of social buzz on the article visits.

Using the unique real referral data of 110 scholarly articles, which are daily updated in a 90-day period, this paper proposes a novel method to make analysis. We find that visits from social media are fast to accumulate but decay rapidly.

Twitter and Facebook are the two most important social referrals that directing people to scholarly articles, the two are about the same and account for over 95% of the total social referral directed visits.

There is synchronism between tweets and tweets resulted visits. Social media and open access are playing important roles in disseminating scholarly articles and promoting public understanding science, which are confirmed quantitatively for the first time with real data in this study.

URL : http://arxiv.org/abs/1608.00798

Characterizing Social Media Metrics of Scholarly Papers: The Effect of Document Properties and Collaboration Patterns

« A number of new metrics based on social media platforms—grouped under the term “altmetrics”—have recently been introduced as potential indicators of research impact. Despite their current popularity, there is a lack of information regarding the determinants of these metrics. Using publication and citation data from 1.3 million papers published in 2012 and covered in Thomson Reuters’ Web of Science as well as social media counts from Altmetric.com, this paper analyses the main patterns of five social media metrics as a function of document characteristics (i.e., discipline, document type, title length, number of pages and references) and collaborative practices and compares them to patterns known for citations. Results show that the presence of papers on social media is low, with 21.5% of papers receiving at least one tweet, 4.7% being shared on Facebook, 1.9% mentioned on blogs, 0.8% found on Google+ and 0.7% discussed in mainstream media. By contrast, 66.8% of papers have received at least one citation. Our findings show that both citations and social media metrics increase with the extent of collaboration and the length of the references list. On the other hand, while editorials and news items are seldom cited, it is these types of document that are the most popular on Twitter. Similarly, while longer papers typically attract more citations, an opposite trend is seen on social media platforms. Finally, contrary to what is observed for citations, it is papers in the Social Sciences and humanities that are the most often found on social media platforms. On the whole, these findings suggest that factors driving social media and citations are different. Therefore, social media metrics cannot actually be seen as alternatives to citations; at most, they may function as complements to other type of indicators. »

URL : Characterizing Social Media Metrics of Scholarly Papers

DOI : 10.1371/journal.pone.0120495