Allegation of scientific misconduct increases Twitter attention

Authors : Lutz Bornmann, Robin Haunschild

The web-based microblogging system Twitter is a very popular altmetrics source for measuring the broader impact of science. In this case study, we demonstrate how problematic the use of Twitter data for research evaluation can be, even though the aspiration of measurement is degraded from impact to attention measurement.

We collected the Twitter data for the paper published by Yamamizu et al. (2017). An investigative committee found that the main figures in the paper are fraudulent.

URL : https://arxiv.org/abs/1802.00606

The counting house: measuring those who count. Presence of Bibliometrics, Scientometrics, Informetrics, Webometrics and Altmetrics in the Google Scholar Citations, ResearcherID, ResearchGate, Mendeley & Twitter

Authors : Alberto Martin-Martin, Enrique Orduna-Malea, Juan M. Ayllon, Emilio Delgado Lopez-Cozar

Following in the footsteps of the model of scientific communication, which has recently gone through a metamorphosis (from the Gutenberg galaxy to the Web galaxy), a change in the model and methods of scientific evaluation is also taking place.

A set of new scientific tools are now providing a variety of indicators which measure all actions and interactions among scientists in the digital space, making new aspects of scientific communication emerge.

In this work we present a method for capturing the structure of an entire scientific community (the Bibliometrics, Scientometrics, Informetrics, Webometrics, and Altmetrics community) and the main agents that are part of it (scientists, documents, and sources) through the lens of Google Scholar Citations.

Additionally, we compare these author portraits to the ones offered by other profile or social platforms currently used by academics (ResearcherID, ResearchGate, Mendeley, and Twitter), in order to test their degree of use, completeness, reliability, and the validity of the information they provide.

A sample of 814 authors (researchers in Bibliometrics with a public profile created in Google Scholar Citations was subsequently searched in the other platforms, collecting the main indicators computed by each of them.

The data collection was carried out on September, 2015. The Spearman correlation was applied to these indicators (a total of 31) , and a Principal Component Analysis was carried out in order to reveal the relationships among metrics and platforms as well as the possible existence of metric cluster.

URL : https://arxiv.org/abs/1602.02412

Scholars on Twitter: who and how many are they?

Authors :  Rodrigo Costas, Jeroen van Honk, Thomas Franssen

In this paper we present a novel methodology for identifying scholars with a Twitter account. By combining bibliometric data from Web of Science and Twitter users identified by Altmetric.com we have obtained the largest set of individual scholars matched with Twitter users made so far.

Our methodology consists of a combination of matching algorithms, considering different linguistic elements of both author names and Twitter names; followed by a rule-based scoring system that weights the common occurrence of several elements related with the names, individual elements and activities of both Twitter users and scholars matched.

Our results indicate that about 2% of the overall population of scholars in the Web of Science is active on Twitter. By domain we find a strong presence of researchers from the Social Sciences and the Humanities. Natural Sciences is the domain with the lowest level of scholars on Twitter.

Researchers on Twitter also tend to be younger than those that are not on Twitter. As this is a bibliometric-based approach, it is important to highlight the reliance of the method on the number of publications produced and tweeted by the scholars, thus the share of scholars on Twitter ranges between 1% and 5% depending on their level of productivity. Further research is suggested in order to improve and expand the methodology.

URL : https://arxiv.org/abs/1712.05667

What do computer scientists tweet? Analyzing the link-sharing practice on Twitter

Authors : Marco Schmitt, Robert Jäschke

Twitter communication has permeated every sphere of society. To highlight and share small pieces of information with possibly vast audiences or small circles of the interested has some value in almost any aspect of social life.

But what is the value exactly for a scientific field? We perform a comprehensive study of computer scientists using Twitter and their tweeting behavior concerning the sharing of web links.

Discerning the domains, hosts and individual web pages being tweeted and the differences between computer scientists and a Twitter sample enables us to look in depth at the Twitter-based information sharing practices of a scientific community.

Additionally, we aim at providing a deeper understanding of the role and impact of altmetrics in computer science and give a glance at the publications mentioned on Twitter that are most relevant for the computer science community.

Our results show a link sharing culture that concentrates more heavily on public and professional quality information than the Twitter sample does. The results also show a broad variety in linked sources and especially in linked publications with some publications clearly related to community-specific interests of computer scientists, while others with a strong relation to attention mechanisms in social media.

This refers to the observation that Twitter is a hybrid form of social media between an information service and a social network service.

Overall the computer scientists’ style of usage seems to be more on the information-oriented side and to some degree also on professional usage. Therefore, altmetrics are of considerable use in analyzing computer science.

URL : What do computer scientists tweet? Analyzing the link-sharing practice on Twitter

DOI : https://doi.org/10.1371/journal.pone.0179630

The unbearable emptiness of tweeting — About journal articles

Authors : Nicolas Robinson-Garcia, Rodrigo Costas, Kimberley Isett, Julia Melkers, Diana Hicks

Enthusiasm for using Twitter as a source of data in the social sciences extends to measuring the impact of research with Twitter data being a key component in the new altmetrics approach. In this paper, we examine tweets containing links to research articles in the field of dentistry to assess the extent to which tweeting about scientific papers signifies engagement with, attention to, or consumption of scientific literature.

The main goal is to better comprehend the role Twitter plays in scholarly communication and the potential value of tweet counts as traces of broader engagement with scientific literature. In particular, the pattern of tweeting to the top ten most tweeted scientific dental articles and of tweeting by accounts is examined.

The ideal that tweeting about scholarly articles represents curating and informing about state-of-the-art appears not to be realized in practice. We see much presumably human tweeting almost entirely mechanical and devoid of original thought, no evidence of conversation, tweets generated by monomania, duplicate tweeting from many accounts under centralized professional management and tweets generated by bots.

Some accounts exemplify the ideal, but they represent less than 10% of tweets. Therefore, any conclusions drawn from twitter data is swamped by the mechanical nature of the bulk of tweeting behavior. In light of these results, we discuss the compatibility of Twitter with the research enterprise as well as some of the financial incentives behind these patterns.

URL : The unbearable emptiness of tweeting — About journal articles

DOI : https://doi.org/10.1371/journal.pone.0183551

Towards an Ethical Framework for Publishing Twitter Data in Social Research: Taking into Account Users’ Views, Online Context and Algorithmic Estimation

Authors : Matthew L Williams, Pete Burnap, Luke Sloan

New and emerging forms of data, including posts harvested from social media sites such as Twitter, have become part of the sociologist’s data diet. In particular, some researchers see an advantage in the perceived ‘public’ nature of Twitter posts, representing them in publications without seeking informed consent.

While such practice may not be at odds with Twitter’s terms of service, we argue there is a need to interpret these through the lens of social science research methods that imply a more reflexive ethical approach than provided in ‘legal’ accounts of the permissible use of these data in research publications.

To challenge some existing practice in Twitter-based research, this article brings to the fore: (1) views of Twitter users through analysis of online survey data; (2) the effect of context collapse and online disinhibition on the behaviours of users; and (3) the publication of identifiable sensitive classifications derived from algorithms.

URL : Towards an Ethical Framework for Publishing Twitter Data in Social Research: Taking into Account Users’ Views, Online Context and Algorithmic Estimation

DOI : http://dx.doi.org/10.1177%2F0038038517708140

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