A comparative analysis of Twitter users who Tweeted on psychology and political science journal articles
| Published date | 11 November 2019 |
| Date | 11 November 2019 |
| Pages | 1188-1208 |
| DOI | https://doi.org/10.1108/OIR-03-2019-0097 |
| Author | Yanfen Zhou,Jin-Cheon Na |
| Subject Matter | Library & information science,Information behaviour & retrieval,Collection building & management,Bibliometrics,Databases,Information & knowledge management,Information & communications technology,Internet,Records management & preservation,Document management |
A comparative analysis of
Twitter users who Tweeted on
psychology and political
science journal articles
Yanfen Zhou and Jin-Cheon Na
Wee Kim Wee School of Communication and Information,
Nanyang Technological University, Singapore
Abstract
Purpose –The purpose of this paper is to understand the similarities and differences between the Twitter
users who tweeted on journal articles in psychology and political science disciplines.
Design/methodology/approach –The data were collected from Web of Science, Altmetric.com, and
Twitter. A total of 91,826 tweets with 22,541 distinct Twitter user profiles for psychology discipline and
29,958 tweets with 10,478 distinct Twitter user profiles for political science discipline were used for analysis.
The demographics analysis includes gender, geographic location, individual or organization user, academic or
non-academic background, and psychology/political science domain knowledge background. A machine
learning approach using support vector machine (SVM) was used for user classification based on the Twitter
user profile information. Latent Dirichlet allocation (LDA) topic modeling was used to discover the topics that
the users discussed from the tweets.
Findings –Results showed that the demographics of Twitter users who tweeted on psychology and political
science are significantly different. Tweets on journal articles in psychology reflected more the impact of
scientific research finding on the general public and attracted more attention from the general public than the
ones in political science. Disciplinary difference in term of user demographics exists, and thus it is important
to take the discipline into consideration for future altmetrics studies.
Originality/value –From this study, researchers or research organizations may have a better idea on who
their audiences are, and hence more effective strategies can be taken by researchers or organizations to reach
a wider audience and enhance their influence.
Keywords Psychology, Machine learning, Scholarly communication, Political science, Twitter user profile
Paper type Research paper
Introduction
The advent of Web 2.0 has tremendously influenced every aspects of the society. The way of
scholarly communication is also transforming. Apart from the traditional citation impact
metrics that typically refer to a document in support of an argument, another form of metric,
altmetrics (short for alternative metrics), was proposed in 2010. It aims to measure
web-driven scholarly interactions, such as how often a research article (or data) is tweeted,
blogged or bookmarked (Howard, 2012). Twitter provides an easy communication channel
between researchers and the public, either for educational purposes or dissemination of
research articles to wider audiences (Vainio and Holmberg, 2017). Twitter is often used for
altmetrics studies thanks to its popularity and ease with which data can be extracted
(Thelwall et al., 2013). Twitter citation (or mention) can be defined as a direct or indirect link
to a peer-reviewed journal article via Twitter (Priem and Costello, 2010). Twitter users cite a
research article because it is interesting or considered useful for their followers who would
not otherwise be exposed to.
Online Information Review
Vol. 43 No. 7, 2019
pp. 1188-1208
© Emerald PublishingLimited
1468-4527
DOI 10.1108/OIR-03-2019-0097
Received 23 March 2019
Revised 15 July 2019
Accepted 22 July 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1468-4527.htm
The authors wish to thank Sajeev Udayakumar, Senadeera Damith Chamika and Selvaraj Yamunarani
for the help of psychology data collection, as well as Mayuri Saxena, Patil Deepthi and Soundararajan
Shanmugapriya for the help of political science data collection.
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OIR
43,7
As the use of altmetrics grows and becomes increasingly important, more and more
concerns about disciplinary differences of scholarly discussion in social media have been
raised. For instance, Htoo and Na (2017b) examined the demographics of Twitter users who
used tweets for scholarly communication. But their study was limited to one particular
discipline only. To the best of authors’knowledge, no previous altmetrics study has
compared the user’s demographics among different disciplines, and a comparative analysis
with other disciplines is necessary to validate the result. Moreover, different demographic
groups behave differently on social media (Goel et al., 2012), and the variation in user
demographics of different disciplines is still unknown. The motivation of this study is to fill
the gap of previous research on whether there is any discipline difference in the Twitter user
demographics for scholarly communication. Besides, the comparative approach adopted by
this study can be used to validate the results attained from previous studies. On top of that,
the findings may provide some guidelines on which informed decision can be made in terms
of adopting Twitter as a new form of scholarly communication in specific discipline.
Costas et al. (2017), Ke et al. (2017) and Mohammadi et al. (2018) found that social science
and humanities are more popular among the disciplines in Twitter. In addition, after
investigating the altmetrics in nine different social science disciplines, Htoo and Na (2017a)
pointed out that psychiatry, clinical psychology and political science are among the most
promising disciplines where high correlation between altmetrics and citation count can be
found. A steep upward trend is observed in research articles’altmetric presence over the
years and a large percentage of these social media citations on research articles come from
Twitter. Among these three disciplines, psychology and political science were selected for
this study as they are more popular in the general public.
A machine learning approach is used to automatically profile the Twitter users from
their user description information. In previous work, the methods for Twitter user profiling
involved analyzing Twitter accounts manually or survey research, which are labor
intensive. This study has the following objectives: to derive Twitter users’demographics
who tweeted on psychology and political science journal articles using a machine learning
approach: the demographics including gender, geographic location, individual or
organization user, academic or non-academic background, and psychology/political
science domain knowledge background; to discover the topics that the users discuss
about based on the tweets using topic modeling with Latent Dirichlet allocation (LDA); to
conduct a thorough comparative analysis on the distribution of user demographics between
the psychology and political science disciplines, and find out any commonality or difference
of the user demographics between the two disciplines.
Results suggest that there are significant differences in the demographics of Twitter
users who tweeted on psychology and political science. Compared with the tweets on journal
articles in political science, psychology reflected more the impact of scientific research
finding on the general public and attracted more attention from the general public.
Discipline differences should be considered in the future altmetrics studies.
Literature review
Sharing journal articles on Twitter
Twitter has been increasingly used for academic purposes. Many researchers start to
investigate whether Tweet citation can give any implication on the impact of journal
articles. Eysenbach (2011) stated that social media could reflect the quality of journal articles
because the study of correlation between altmetrics and traditional citation count showed
that the discussion of journal articles in Twitter was a strong indicator of the public interest
in the research articles. Jung et al. (2016) also concluded that altmetrics were complementary
to traditional citation count by providing a measurement of other kind of impacts that
would be otherwise ignored. Twitter citation was also considered as evidence of
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Comparative
analysis of
Twitter users
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