The linguistic patterns and rhetorical structure of citation context: an approach using n-grams

Bertin, Marc; Atanassova, Iana; Sugimoto, Cassidy R.; Larivière, Vincent

doi:10.1007/s11192-016-2134-8

Cited by 38 publications

(43 citation statements)

References 51 publications

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“…The word "show" was the most frequent verb in the introduction and discussion sections, and the second most frequent verb in results, but not among the top 10 verbs in the method section. "Show" was also among the top words in introduction and methods sections in the study by Bertin, Atanassova, Sugimoto, et al (2016). It also was among the top words in discovery citation sentences (citances) of Small (2018) which are explained in the following.…”

Section: Linguistic Patterns (Hedge Cues Verbs and Words)mentioning

confidence: 93%

“…PLOS journals are great sources of citation content and context research, since they cover all fields of science and social sciences. In PLOS, papers are available in XML format (Bertin, Atanassova, Sugimoto, et al, 2016).…”

Section: Technical Developments and New Sources Of Citation Studiesmentioning

confidence: 99%

“…Analyzing the context of citations can be used to determine the extent and nature of the influence of a work on subsequent publications (Anderson, 2006). Citation context has been operationalized in several ways, including the position of the citation within the citing text, the semantics surrounding the reference (Bertin, Atanassova, Sugimoto, & Lariviere, 2016), and the words around citations (Bornmann, Haunschild, & Hug, 2018). Citation content analysis has also been used by some studies to determine the functions of citations.…”

Section: Introductionmentioning

confidence: 99%

“…Teufel et al, 2006). Having access to full text databases has enabled researchers employing computational techniques to conduct complex analyses on scientific documents (Bertin, Atanassova, Sugimoto, et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018

2019

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The purpose of this paper is to update the review of Bornmann and Daniel (2008) presenting a narrative review of studies on citations in scientific documents. The current review covers 41 studies published between 2006 and 2018. Bornmann and Daniel (2008) focused on earlier years. The current review describes the (new) studies on citation content and context analyses as well as the studies that explore the citation motivation of scholars through surveys or interviews. One focus in this paper is on the technical developments in the last decade, such as the richer meta-data available and machine-readable formats of scientific papers. These developments have resulted in citation context analyses of large datasets in comprehensive studies (which was not possible previously). Many studies in recent years have used computational and machine learning techniques to determine citation functions and polarities, some of which have attempted to overcome the methodological weaknesses of previous studies. The automated recognition of citation functions seems to have the potential to greatly enhance citation indices and information retrieval capabilities. Our review of the empirical studies demonstrates that a paper may be cited for very different scientific and non-scientific reasons. This result accords with the finding by Bornmann and Daniel (2008). The current review also shows that to better understand the relationship between citing and cited documents, a variety of features should be analyzed, primarily the citation context, the semantics and linguistic patterns in citations, citation locations within the citing document, and citation polarity (negative, neutral, positive).

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Section: Linguistic Patterns (Hedge Cues Verbs and Words)mentioning

confidence: 93%

Section: Technical Developments and New Sources Of Citation Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018

2019

View full text Add to dashboard Cite

show abstract

“…This can be achieved through natural language processing and will be investigated in future studies. Related investigations have been conducted by several researchers (e.g., Bertin, Atanassova, Sugimoto, & Lariviere, 2016;Cano, 1989;Hu, Chen, & Liu, 2013;Ritchie, 2008;Small, 2011).…”

Section: Determining the Proportions Of Authors' And References' Contmentioning

confidence: 99%

A Discussion of citations from the perspective of the contribution of the cited paper to the citing paper

Fang

2018

Asso for Info Science & Tech

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To more reasonably allocate a paper's credit, this article argues that both a paper's authors and references contribute to a given paper. Accordingly, we quantitatively represent the proportion of contributions from each author and reference to a paper. A paper's credit can be allocated among its authors and references based on their contributions. All papers carry innate credit because of publication. If cited, they also carry external credit from the citing papers. The proportion of a paper's credit allocated to references can be regarded as a credit output and serves as an input for these references. In this scenario, only the credit assigned to a paper's authors remains as the paper's deserved credit. The credit of papers can be transferred in a direction opposite that of knowledge diffusion. Via this method, the estimate of an individual reference's contribution incorporates content‐based citation analysis, a promising method to differentiate different citations. A paper's deserved credit represents the contribution of the paper's authors to the scientific community via the new knowledge they provide in the paper. Therefore, it is rational to evaluate papers according to their deserved credit, not the credit they carry.

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Understanding and predicting future research impact at different career stages—A social network perspective

Zuo

Zhao

2020

Asso for Info Science & Tech

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Performance assessment is ubiquitous and crucial in people analytics. Scientific impact, particularly, plays a significant role in the academia. This paper attempts to understand researchers' career trajectories by considering the research community as a social network, where individuals build ties with each other via coauthorship. The resulting linkage facilitates information flow and affects researchers' future impact. Consequently, we systematically investigate the career trajectories of researchers with respect to research impact using the social capital theory as our theoretical foundation. Specifically, for early‐stage and mid‐career academics, we find that connections with prominent researchers associate with greater impact. Brokerage positions, in addition, are beneficial to a researcher's research impact in the long run. For senior researchers, however, the only social network feature that significantly affects their future impact is the reputation of their recently built ties. Finally, we build predictive models on future research impact which can be leveraged by both organizations and individuals. This paper provides empirical evidence for how social networks provide signals on researchers' career dynamics guided by social capital theory. Our findings have implications for individual researchers to strategically plan and promote their careers and for research institutions to better evaluate current as well as prospective employees.

show abstract

The linguistic patterns and rhetorical structure of citation context: an approach using n-grams

Cited by 38 publications

References 51 publications

What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018

What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018

A Discussion of citations from the perspective of the contribution of the cited paper to the citing paper

Understanding and predicting future research impact at different career stages—A social network perspective

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