Mining the Web to approximate university rankings
| Pages | 173-183 |
| Published date | 20 August 2018 |
| DOI | https://doi.org/10.1108/IDD-05-2018-0014 |
| Date | 20 August 2018 |
| Author | Corren G. McCoy,Michael L. Nelson,Michele C. Weigle |
| Subject Matter | Library & information science,Library & information services,Lending,Document delivery,Collection building & management,Stock revision,Consortia |
Mining the Web to approximate
university rankings
Corren G. McCoy, Michael L. Nelson and Michele C. Weigle
Old Dominion University, Norfolk, Virginia, USA
Abstract
Purpose –The purpose of this study is to present an alternative to university ranking lists published in U.S. News & World Report,Times Higher
Education,Academic Ranking of World Universities and Money Magazine. A strategy is proposed to mine a collection of university data obtained
from Twitter and publicly available online academic sources to compute social media metrics that approximate typic al academic rankings of US
universities.
Design/methodology/approach –The Twitter application programming interface (API) is used to rank 264 universities using two ea sily collected
measurements. The University Twitter Engagement (UTE) score is the total number of primary and secondary followers affiliated with the university.
The authors mine other public data sources related to endowment funds, athletic expenditures and student enrollment to compute a ranking based
on the endowment, expenditures and enrollment (EEE) score.
Findings –In rank-to-rank comparisons, the authors observed a significant, positive rank correlation (
t
= 0.6018) between UTE and an aggregate
reputation ranking, which indicates UTE could be a viable proxy for ranking atypical institutions normally excluded from traditional lists.
Originality/value –The UTE and EEE metrics offer distinct advantages because they can be calculated on-demand rather than relying on an annual
publication and they promote diversity in the ranking lists, as any university with a Twitter account can be ranked by UTE and any university with
online information about enrollment, expenditures and endowment can be given an EEE rank. The authors also propose a unique approach for
discovering official university accounts by mining and correlating the profile information of Twitter friends.
Keywords Information retrieval, Social media, Twitter, University ranking, Data mining, Knowledge discovery
Paper type Research paper
1. Introduction
Universities and other academic institutions increasingly see
their presence and visibility on the Web as central to their
reputation. In this context, information content on the
academic Web is viewed as a reflection of the overall
organization and performance of the university (Aguillo et al.,
2008). Academic rankings can play an important role in
assessing reputation. With disparate criteria and
methodologies, however, there can be a significant divergence
in the rankings of a particular institution.
We consider the set of data associated with a universitythat is
publicly available on the Web as a collection. In this work, we
mine the data in this collection to compute two different
metrics that can be used to approximate typical academic
rankings of US universities. We mine Twitter data to compute
a ranking based on the UniversityTwitter Engagement (UTE)
score. We mine other public sources of data related to
endowment funds, athletic expenditures and student
enrollment to compute a ranking based on the endowment,
expenditures and enrollment (EEE) score. Both of these
metrics can be computedat any time and by any party, without
having to wait for the release of annual university rankings or
depending on subjectivemeasures such as reputation.
UTE is the total number of all affiliated users the university
promotes on its homepage plus the followers of any Twitter
friends who indicate an affiliation with the university in their
profile Uniform Resource Identifier (URI). The UTE score
quantifies the potential popularity or prestige of the university
without an extensive data collection effort. The EEE score is
computed from publicly available data on the web about
alumni engagement (reflected in alumni donations and
endowments), athletic engagement (reflected in athletic
expenditures)and student enrollment.
We assume that:
Universities with higher undergraduate enrollment are
likely to have more Twitter followers as students transition
to alumni status.
Official Twitter accounts will be featured on the
university’s homepage.
Sports participation is a driver that increases awareness of
the university’s brand.
The data needed to comprise the ranking criteria are
readily available from public data on the Web.
Figure 1 depicts a point-in-time glimpse into the Twitter
followers (675K) for Harvard University, a perennially
The current issue and full text archive of this journal is available on
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Information Discovery and Delivery
46/3 (2018) 173–183
© Emerald Publishing Limited [ISSN 2398-6247]
[DOI 10.1108/IDD-05-2018-0014]
This paper was accepted for presentation at the JCDL 2018 Workshop on
Knowledge Discovery from Digital Libraries, June 6, 2018, Fort Worth,
TX.
Received 15 May 2018
Accepted 14 August 2018
173
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