251 research outputs found
Differences in personal and professional tweets of scholars
Purpose – The purpose of this paper is to show that there were differences in the use of Twitter by
professors at AAU schools. Affordance use differed between the personal and professional tweets
of professors as categorized by turkers. Framing behaviors were described that could impact the
interpretation of tweets by audience members.
Design/methodology/approach – A three phase research design was used that included surveys of
professors, categorization of tweets by workers in Amazon’s Mechanical Turk, and categorization
of tweets by active professors on Twitter.
Findings – There were significant differences found between professors that reported having
a Twitter account, significant differences found between types of Twitter accounts (personal,
professional, or both), and significant differences in the affordances used in personal and professional
tweets. Framing behaviors were described that may assist altmetric researchers in distinguishing
between personal and professional tweets.
Research limitations/implications – The study is limited by the sample population, survey
instrument, low survey response rate, and low Cohen’s κ.
Practical implications – An overview of various affordances found in Twitter is provided and a
novel use of Amazon’s Mechanical Turk for the categorization of tweets is described that can
be applied to future altmetric studies.
Originality/value – This work utilizes a socio-technical framework integrating social and
psychological theories to interpret results from the tweeting behavior of professors and the
interpretation of tweets by workers in Amazon’s Mechanical Turk
Astrophysicists on Twitter: An in-depth analysis of tweeting and scientific publication behavior
This paper analyzes the tweeting behavior of 37 astrophysicists on Twitter
and compares their tweeting behavior with their publication behavior and
citation impact to show whether they tweet research-related topics or not.
Astrophysicists on Twitter are selected to compare their tweets with their
publications from Web of Science. Different user groups are identified based on
tweeting and publication frequency. A moderate negative correlation (p=-0.390*)
is found between the number of publications and tweets per day, while retweet
and citation rates do not correlate. The similarity between tweets and
abstracts is very low (cos=0.081). User groups show different tweeting behavior
such as retweeting and including hashtags, usernames and URLs. The study is
limited in terms of the small set of astrophysicists. Results are not
necessarily representative of the entire astrophysicist community on Twitter
and they most certainly do not apply to scientists in general. Future research
should apply the methods to a larger set of researchers and other scientific
disciplines. To a certain extent, this study helps to understand how
researchers use Twitter. The results hint at the fact that impact on Twitter
can neither be equated with nor replace traditional research impact metrics.
However, tweets and other so-called altmetrics might be able to reflect other
impact of scientists such as public outreach and science communication. To the
best of our knowledge, this is the first in-depth study comparing researchers'
tweeting activity and behavior with scientific publication output in terms of
quantity, content and impact.Comment: 14 pages, 5 figures, 7 table
Tweets as impact indicators: Examining the implications of automated bot accounts on Twitter
This brief communication presents preliminary findings on automated Twitter
accounts distributing links to scientific papers deposited on the preprint
repository arXiv. It discusses the implication of the presence of such bots
from the perspective of social media metrics (altmetrics), where mentions of
scholarly documents on Twitter have been suggested as a means of measuring
impact that is both broader and timelier than citations. We present preliminary
findings that automated Twitter accounts create a considerable amount of tweets
to scientific papers and that they behave differently than common social bots,
which has critical implications for the use of raw tweet counts in research
evaluation and assessment. We discuss some definitions of Twitter cyborgs and
bots in scholarly communication and propose differentiating between different
levels of engagement from tweeting only bibliographic information to discussing
or commenting on the content of a paper.Comment: 9 pages, 4 figures, 1 tabl
Measuring Social Media Activity of Scientific Literature: An Exhaustive Comparison of Scopus and Novel Altmetrics Big Data
This paper measures social media activity of 15 broad scientific disciplines
indexed in Scopus database using Altmetric.com data. First, the presence of
Altmetric.com data in Scopus database is investigated, overall and across
disciplines. Second, the correlation between the bibliometric and altmetric
indices is examined using Spearman correlation. Third, a zero-truncated
negative binomial model is used to determine the association of various factors
with increasing or decreasing citations. Lastly, the effectiveness of altmetric
indices to identify publications with high citation impact is comprehensively
evaluated by deploying Area Under the Curve (AUC) - an application of receiver
operating characteristic. Results indicate a rapid increase in the presence of
Altmetric.com data in Scopus database from 10.19% in 2011 to 20.46% in 2015. A
zero-truncated negative binomial model is implemented to measure the extent to
which different bibliometric and altmetric factors contribute to citation
counts. Blog count appears to be the most important factor increasing the
number of citations by 38.6% in the field of Health Professions and Nursing,
followed by Twitter count increasing the number of citations by 8% in the field
of Physics and Astronomy. Interestingly, both Blog count and Twitter count
always show positive increase in the number of citations across all fields.
While there was a positive weak correlation between bibliometric and altmetric
indices, the results show that altmetric indices can be a good indicator to
discriminate highly cited publications, with an encouragingly AUC= 0.725
between highly cited publications and total altmetric count. Overall, findings
suggest that altmetrics could better distinguish highly cited publications.Comment: 34 Pages, 3 Figures, 15 Table
Astrophysicists’ conversational connections on Twitter
Because Twitter and other social media are increasingly used for analyses based on altmetrics, this research sought to
understand what contexts, affordance use, and social activities influence the tweeting behavior of astrophysicists. Thus, the
presented study has been guided by three research questions that consider the influence of astrophysicists’ activities (i.e.,
publishing and tweeting frequency) and of their tweet construction and affordance use (i.e. use of hashtags, language, and
emotions) on the conversational connections they have on Twitter. We found that astrophysicists communicate with a
variety of user types (e.g. colleagues, science communicators, other researchers, and educators) and that in the ego
networks of the astrophysicists clear groups consisting of users with different professional roles can be distinguished.
Interestingly, the analysis of noun phrases and hashtags showed that when the astrophysicists address the different groups
of very different professional composition they use very similar terminology, but that they do not talk to each other (i.e.
mentioning other user names in tweets). The results also showed that in those areas of the ego networks that tweeted more
the sentiment of the tweets tended to be closer to neutral, connecting frequent tweeting with information sharing activities
rather than conversations or expressing opinions
Social media in scholarly communication : a review of the literature and empirical analysis of Twitter use by SSHRC doctoral award recipients
This report has been commissioned by the Social Sciences and Humanities Research Council (SSHRC) to analyze
the role that social media currently plays in scholarly communication as well as to what extent metrics derived
from social media activity related to scholarly content can be applied in an evaluation context.
Scholarly communication has become more diverse and open with research being discussed, shared and
evaluated online. Social media tools are increasingly being used in the research and scholarly communication
context, as scholars connect on Facebook, LinkedIn and Twitter or specialized platforms such as ResearchGate,
Academia.edu or Mendeley. Research is discussed on blogs or Twitter, while datasets, software code and
presentations are shared on Dryad, Github, FigShare and similar websites for reproducibility and reuse. Literature
is managed, annotated and shared with online tools such as Mendeley and Zotero, and peer review is starting to
be more open and transparent. The changing landscape of scholarly communication has also brought about new
possibilities regarding its evaluation. So-called altmetrics are based on scholarly social media activity and have
been introduced to reflect scholarly output and impact beyond considering only peer-reviewed journal articles
and citations within them to measure scientific success. This includes the measurement of more diverse types of
scholarly work and various forms of impact including that on society.
This report provides an overview of how various social media tools are used in the research context based on
1) an extensive review of the current literature as well as 2) an empirical analysis of the use of Twitter by the 2010
cohort of SSHRC Doctoral Award recipients was analyzed in depth. Twitter has been chosen as one of the most
promising tools regarding interaction with the general public and scholarly communication beyond the scientific
community. The report focuses on the opportunities and challenges of social media and derived metrics and
attempts to provide SSHRC with information to develop guidelines regarding the use of social media by funded
researchers as well support the informed used of social media metrics
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