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Authors’ Status and the Perceived Quality of Their Work:

Measuring Citation Sentiment Change in Nobel Articles

Erjia Yan

College of Computing and Informatics, Drexel University, Philadelphia, PA. E-mail: [email protected]

Zheng Chen

College of Computing and Informatics, Drexel University, Philadelphia, PA

Kai Li

College of Computing and Informatics, Drexel University, Philadelphia, PA

Prior research in status ordering has used numeric indi-

cators to examine the impact of a status change on the

perception of a scientist’s work. This study measures

the perception change directly as reﬂected in citation

sentiment, with the attainment of a Nobel Prize in Chem-

istry or a Nobel Prize in Physiology or Medicine consid-

ered the status change. The article identiﬁes 12,393

citances to 25 Nobel articles in PubMed Central and

includes a control article set of 75 articles with 30,851

citances. The results show a moderate increase in cita-

tion sentiment toward Nobel articles postaward. Dynam-

ically, for Nobel articles there is a steady sentiment

increase, and a Nobel Prize seems to co-occur with this

trend. This trend, however, is not evident in the control

article set.

Introduction

Status ordering is a fundamental area of sociological

research. Humans are hardwired to understand their position

within a group, from children in playgrounds (Bothner,

Godart, & Lee, 2010), to pupils in classrooms (Patterson,

Kupersmidt, & Griesler, 1990), to employees in work-

places (George, Dahlander, Grafﬁn, & Sim, 2016; Harley,

1999). This near-obsession with knowing one’s status and

advancing in the status ordering may be attributed to the

availability of resources: the scarcer the resources, the more

desirable they are to us. In this competition for resources,

higher status confers an advantage, thus making such status

more appealing.

In science, the disproportionate concentration of resources

is known as the Matthew Effect. Coined by Robert Merton

(1968), the term describes the phenomenon whereby emi-

nent scientists often get more scientiﬁc credit compared

with those who are relatively unknown. Thus, a change in

status will inevitably result in a marked change in one’s

access to resources, social capital, and self-assurance, among

other characteristics. Past research has established the exis-

tence of the Matthew Effect in a variety of scientiﬁcand

scholarly settings, including scientiﬁc collaboration (Barabâsi

et al., 2002; Newman, 2001a, 2001b, 2001c, 2004), informa-

tion networks (Clauset, Shalizi, & Newman, 2009), distribu-

tion of citations (Bensman, 2008; Redner, 1998; Seglen,

1992; Stringer, Sales-Pardo, & Amaral, 2010), and word fre-

quency distributions (Newman, 2005), among many other

types of empirical data (Perc, 2014).

Prior studies have largely relied on numeric indicators

to approximate resources when examining status ordering.

The number of collaborators, for instance, has been used

as a proxy for social capital (Barabâsi et al., 2002; Newman,

2001a, 2001b, 2001c, 2004), and number of citations has

stood in for the concept of scientiﬁc rewards (Bensman,

2008; Redner, 1998; S eglen, 1992 ; Stringer et al., 2 010).

Although these studies have provided quantitative evidence

of the Matthew Effect, we have made little headway in

understanding the impact of status change on subjective per-

ceptions of one’s work. This lack of progress is likely due to

the lack of proper instruments with which to measure percep-

tions. In the present study, we aim to address this problem

by tackling the complexity of citation sentiments, as used to

measure the impact of authors’ status change on how others

cite their work. Among all the possible status changes in a

scientist’s career, winning a Nobel Prize is unparalleled.

Thus, this study uses Nobel Prizes as the status change and

Received April 3, 2018; revised March 13, 2019; accepted March

21, 2019

Library (wileyonlinelibrary.com). DOI: 10.1002/asi.24237

JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY, 00(0):1–11, 2019

measures the citation sentiment of articles citing a Nobel arti-

cle before and after its author’s investiture.

As the most highly esteemed science award, the Nobel

Prize has been studied in the context of scholarly commu-

nication from several perspectives, as have its laureates.

Topics of such research include prize prediction (Ashton &

Oppenheim, 1978; Garﬁeld, 1986; Gingras & Wallace, 2010),

sources of funding of Nobel articles (Tatsioni, Vavva, &

Ioannidis, 2010), Nobel Prize effects on citation impact

(Farys & Wolbring, 2017; Frandsen & Nicolaisen, 2013), pat-

terns of collaboration (Chan, Önder, & Torgler, 2015), collab-

oration networks (Wagner, Horlings, Whetsell, Mattsson, &

Nordqvist, 2015), and the likelihood of laureates to obtain

more awards after their Nobel Prize (Chan, Gleeson, &

Torgler, 2014).

Because of the Nobel Prize ’s unmatched prestige, schol-

arly communication researchers have been curious to ﬁnd

out whether Nobel laureates behave differently from aver-

age scientists, and whether their characteristics can be identi-

ﬁed and used to predict future laureates. It has been found

that although Nobel laureates differ from average scientists

in their citation impact, bibliometric indicators alone are not

able to predict prize winners; rather, such indicators simply

identify a group of elite scientists (Ashton & Oppenheim,

1978; Garﬁeld, 1986; Gingras & Wallace, 2010). In addi-

tion, there seems to be a cascading effect: a Nobel Prize

affects not only the citation of the laureate’swork,butalso

that of its cited references, as shown in a case study of

Robert J. Aumann (Frandsen & Nicolaisen, 2013). How-

ever, when citations are normalized based on the articles’

publication year, s uch an effect is no longer observed

(Farys & Wolbring, 2017). Apart from citation impact,

studies have also shown that Nobel laureates have fewer

coauthors compared with a control group and tend to pos-

sess higher social capital, by bridging different communi-

ties of researchers (Wagner et al., 2015).

Particularly relevant to the scope of this research are the

studies examining pattern changes following prize recogni-

tion. Patterns examined to date include collaboration patterns

(Chan et al., 2015), citation proﬁles (Azoulay, Stuart, &

Wang, 2013), and the ability to obtain awards (Chan e t al.,

2014). Studies have found that Nobel laureates form fewer

collaborations with new coautho rs postaward tha n preaward

(Chan e t al., 2015), and their likelihood to obtain awards

drops after they win a Nobel Prize (Chan et al., 2014). In a

similar vein, a study showed a postaward citation increase

to articles published before their authors earned a Howard

Hughes Medical Institute (HHMI) Investigator recognition

(Azoulay et al., 2013). This conﬁrms the theory that people

change their perceptions of the quality of others’ work when

there is a shock to the authors’ status (such as a highly

esteemed award).

This study builds on prior work by using citation senti-

ment to directly measure the change in perception of a sci-

entiﬁc article after a change in the author ’ s status. The

change in this case is the receipt of either of two Nobel

Prizes: the Nobel Prize in Chemistry or the Nobel Prize in

Physiology or Medicine. The set of citation sentences (that

is, “citances”) from citing articles in PubMed Central (PMC)

to Nobel laureates’ work is extracted and processed for senti-

ment analysis. We compare the sentiment of citances from

before and after the work’s author won a Nobel Prize and

use a set of control group articles to benchmark the senti-

ment change. This approach allows us to address one central

research question in this article: to what extent does a change

in authors’ status—such as winning a Nobel Prize—change

the perception of the quality of their work as measured by

citation sentiment?

Literature Review

Citation analysis is strongly based on the relationship

between citing and cited documents. The classic explana-

tion of citation behavior is the normative theory, which

regards citations as a means of paying an intellectual debt

to the authors being cited (Gar

ﬁeld & Merton, 1979; Kaplan,

1965). Despite its importance in the hi story of citation

analysis, subsequent generations of researchers increas-

ingly questioned t he normative assumptions behind this

theory (for example, Bornmann & Daniel, 2008) and

accepted a new approach to citation data that focuses on

the content and context of citation relation.

This newer program of research was ofﬁcially named

content and context analysis in the early 1980s (Small,

1982), deﬁned as including those studies examining the

“particular message or statement within the citing docu-

ment containing the reference” (p. 288). This type of analy-

sis can be traced back to Lipetz’s pioneering work (1965),

in which the author identiﬁed four categories of relationship

between the document pair: original scientiﬁc contribution

of citing article, contribution other than original scientiﬁc

contribution, identity or continuity relationship between arti-

cles, and disposition of the scientiﬁc contribution of the

cited article to the citing article. A large number of studies

under this category have been conducted since then

(Case & Higgins, 2000; Chubin & Moitra, 1975; Duncan,

1981; Frost, 1979; McCain & Salvucci, 2006; Moravcsik &

Murugesan, 1975; Spiegel-Rösing, 1977), most of which

developed a classiﬁcation scheme based on a set of scien-

tiﬁcdocuments.

Cronin (1984) correctly observed that most of these

studies fail to form a “cumulative endeavor” (p. 35), despite

the fact that some regularities are evident in these studies.

His observation is supported by work which identiﬁes and

summarizes the facets underlying these classiﬁcation

schemes. An article by Zhang, Ding, and Milojevi



c (2013),

for example, identiﬁes six principles embedded in all these

studies, including the type of motivation, level of impor-

tance, type of resource, function of citing, type of dis-

position/sentiment, and location of mentioning (p. 21).

Despite its status as one of th e central principles of con-

tent and context analysis, the sentiment polarity expressed

in texts is a relatively late-coming research topic. Although

sentiment polarity has been explored by researchers from

2 JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY—Month 2019

DOI: 10.1002/asi

the perspectives of citation analysis (for example,

MacRoberts & MacRoberts, 1984; Ziman, 1968) and com-

putational linguistics (for example, Wilks & Bien, 1983), it

did not become a popular topic in its own right until the

beginning of the 20th century, a phenomenon partly driven

by the rise of machine-learning methods and the increasing

availability of data sets (Pang & Lee, 2008).

As a response to these new developments in computa-

tional data science, data-driven sentiment analysis was soon

introduced to the ﬁeld of citation analysis. Awais Athar and

colleagues discussed the difﬁculties of applying sentiment

analysis to citation statements and proposed a support vector

machine (SVM) method based on different aspects of sen-

tence structure (for example, Athar, 2011, 2014; Athar &

Teufel, 2012a, 2012b). Machine-learning techniques are a

dominant methodological element in citation sentiment stud-

ies. In addition to SVM (see also Hernández-Alvarez &

Gómez, 2015; Kim & Thoma, 2015; Xu, Martin, &

Mahidadia, 2013), notable examples include random

forest (Abu-Jbara, Ezra, & Radev, 2013; Parthasarathy &

Tomar, 2014), naïve Bayes (Butt et al., 2015; Sula &

Miller, 2014), and neural network methods (Lauscher,

Glavaš, Ponzetto, & Eckert, 2017). Within this category,

SentiWordNet, a lexical resource for opinion mining that

is partly based on a semisupervised machine-learning method,

has also been used in a number of studies (Goodarzi,

Mahmoudi, & Zamani, 2014; Sendhilkumar, Elakkiya, &

Mahalakshmi, 2013).

It should be noted that most of the above-mentioned

studies focus primarily on establishing and testing a meth-

odological framework for citation sentiment analysis. With

more citation analyses taking the sentiment into consider-

ation, we are also gaining more insights about how social

factors help to construct scientiﬁc knowledge. For example,

before the advancement of computational citation semantic

analysis, Small (2011) concluded that there is a correlation

between prominent sentiments and competing knowledge

claims. On the other hand, a more recent study (Ma, Nam, &

Weihe, 2016) suggested that an author’s reputation is a

reliable predictor of the sentiment toward one’s works.

Although these ﬁndings contributed to a deeper under-

standing of citations, we are still far from fully understand-

ing the relationship between citation sentiment and the

authors who receive it. Thus, in this artic le we examine the

impact of a change to authors’ status on the perception of

their work as measured by citation sentiment.

Data

Two sets of articles are distinguished, and both are

introduced in this section. The ﬁrst set is the so-called

“Nobe l articles,” written by Nobel Prize laureates and thought

to be closely connected with the conferral of their respective

Nobel Prizes. The Advanced Information section of each

award at www.nobelprize.org served as our data source for

identifying Nobel articles. Wefocusedontwoawards,the

Nobel Prize in Chemistry and the Nobel Prize in Physiology

or Medicine, because articles that cite these Nobel articles are

more likely to be included in PubMed Central (PMC)—a

large full-text repository freely accessible to the public. Nobel

articles, as we deﬁne them, are typically referred to as “land-

mark” articles in the Advanced Information section or were

published within a 3-year window of the landmark articles by

thesameteamsofauthors.

Most articles in PMC were published after 2008; thus,

to monitor the impact of an award on citation sentiment

change, we narrowed Nobel Prizes to those conferred between

2010 and 2015. This time frame provides a minimum 3-year

citation window for the oldest (2008) article in PMC to cite

a Nobel article at the time of data collection. To effectively

link a Nobel article with its citing articles, the Nobel article

should be indexed in PubMed and have a PubMed ID

(PMID), but it do es not itself need to be included in PMC.

After removing those Nobel articles that lack a PMID and

those with fewer than 20 citations before their respective

Nobel Prize, we included in the ﬁnal data set 14 articles for

Physiology or Medicine (hereafter Medicine) and 11 for

Chemistry. The 2018 October version of PMC was used for

data collection. In all, these articles received 12,393 citances,

of which 4,677 took place before the award. The data set

used in this study is uploaded to Figshare (Yan, 2019).

The second set contains control-group articles with which

to benchmark the sentiment change in the Nobel articles. For

each Nobel article, we selected three control articles that each

had a similar citation count to the Nobel article and were

published within 2 years of the Nobel articles. These selec-

tion criteria ensure that the control articles a re sufﬁciently

similar to the Nobel articles that the major distinguishing fac-

tor is the Nobel Prize. In total, there are 75 articles in the

control group, receiving a total of 30,851 citances.

To calculate the number of citations an article has received

in PMC, one can count the number of times that the cited

article’s PMID occurred in other articles’ cited reference

sections. A full-text search does not need be conducted;

thus, we refer to this process as citation metadata matching.

Tracking down citances within an article’sfulltext,how-

ever, is less straightforward and presents several challenges.

The ﬁrst challenge is to identify the exact sentence where

the citance appears and extract all words that surround the

citance in order to evaluate sentiment. To achieve this, for

every paragraph in the abstract and the document body, the

XML nodes for citations (<xref> nodes) were ﬁrst replaced

by a special token, and then the full paragraph text was

tokenized and further split by the recognized punctuations.

Initially, when only considering the standard XML nodes

(<xref> nodes), for our Nobel article set, this returned less

than half of all citations Nobel articles received in PMC as

returned by a metadata matching.

Thus, we need to deal with the second challenge, which

is the lack of consistency in XML citation coding. We pro-

vide some examples here.

1. Different encodings are used for the dash character ‘-’

inside a multicitation instance such as “[11–17].” It may be

JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY—Month 2019

DOI: 10.1002/asi

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