Determining How Citations Are Used in Citation Contexts

2019 ◽  
pp. 380-383 ◽  
Author(s):  
Michael Färber ◽  
Ashwath Sampath
Keyword(s):  
Citation Contexts

Exploring multiple diversification strategies for academic citation contexts recommendation

2020 ◽  
Vol 38 (4) ◽  
pp. 821-842
Author(s):  
Haihua Chen ◽  
Yunhan Yang ◽  
Wei Lu ◽  
Jiangping Chen
Keyword(s):  
Information Retrieval ◽  
Information Needs ◽  
Semantic Distance ◽  
Evaluation Framework ◽  
Data Set ◽  
Content Type ◽  
User Interests ◽  
Information Retrieval Evaluation ◽  
Diversification Strategies ◽  
Citation Contexts

Purpose Citation contexts have been found useful in many scenarios. However, existing context-based recommendations ignored the importance of diversity in reducing the redundant issues and thus cannot cover the broad range of user interests. To address this gap, the paper aims to propose a novelty task that can recommend a set of diverse citation contexts extracted from a list of citing articles. This will assist users in understanding how other scholars have cited an article and deciding which articles they should cite in their own writing. Design/methodology/approach This research combines three semantic distance algorithms and three diversification re-ranking algorithms for the diversifying recommendation based on the CiteSeerX data set and then evaluates the generated citation context lists by applying a user case study on 30 articles. Findings Results show that a diversification strategy that combined “word2vec” and “Integer Linear Programming” leads to better reading experience for participants than other diversification strategies, such as CiteSeerX using a list sorted by citation counts. Practical implications This diversifying recommendation task is valuable for developing better systems in information retrieval, automatic academic recommendations and summarization. Originality/value The originality of the research lies in the proposal of a novelty task that can recommend a diversification context list describing how other scholars cited an article, thereby making citing decisions easier. A novel mixed approach is explored to generate the most efficient diversifying strategy. Besides, rather than traditional information retrieval evaluation, a user evaluation framework is introduced to reflect user information needs more objectively.

scholarly journals Citation concept analysis (CCA) of Robert K. Merton’s book Social Theory and Social Structure: How often are certain concepts from the book cited in subsequent publications?

2020 ◽  
pp. 1-16
Author(s):  
Charles Crothers ◽  
Lutz Bornmann ◽  
Robin Haunschild
Keyword(s):  
Social Structure ◽  
Social Theory ◽  
Explanatory Power ◽  
Citation Impact ◽  
Concept Analysis ◽  
Specific Information ◽  
Political Sciences ◽  
The Impact ◽  
To Receive ◽  
Citation Contexts

Citations can be used in evaluative bibliometrics to measure the impact of papers. However, citation analysis can be extended by considering a multidimensional perspective on citation impact which is intended to receive more specific information about the kind of received impact. Bornmann, Wray, and Haunschild (2020) introduced the citation concept analysis (CCA) for capturing the importance and usefulness certain concepts (explained in publications) have in subsequent research. In this paper, we apply the method by investigating the impact various concepts introduced in Robert K. Merton’s book Social Theory and Social Structure has had. This book was to lay down a manifesto for sociological analysis in the immediate postwar period, and retains a major impact 70 years later. We found that the most cited concepts are “self-fulfilling” and “role” (about 20% of the citation contexts are related to one of these concepts). The concept “self-fulfilling” seems to be important especially in computer sciences and psychology. For “role,” this seems to be additionally the case for political sciences. These and further results of the study could demonstrate the high explanatory power of the CCA method.

scholarly journals A Glimpse of the First Eight Months of the COVID-19 Literature on Microsoft Academic Graph: Themes, Citation Contexts, and Uncertainties

2020 ◽  
Vol 5 ◽  
Author(s):  
Chaomei Chen
Keyword(s):  
Data Collection ◽  
The Body ◽  
Sense Making ◽  
Synthetic Process ◽  
Amount Of Information ◽  
Scholarly Publications ◽  
Numerous Data ◽  
Multiple Levels ◽  
Citation Contexts ◽  
Research Domain

As scientists worldwide search for answers to the overwhelmingly unknown behind the deadly pandemic, the literature concerning COVID-19 has been growing exponentially. Keeping abreast of the body of literature at such a rapidly advancing pace poses significant challenges not only to active researchers but also to society as a whole. Although numerous data resources have been made openly available, the analytic and synthetic process that is essential in effectively navigating through the vast amount of information with heightened levels of uncertainty remains a significant bottleneck. We introduce a generic method that facilitates the data collection and sense-making process when dealing with a rapidly growing landscape of a research domain such as COVID-19 at multiple levels of granularity. The method integrates the analysis of structural and temporal patterns in scholarly publications with the delineation of thematic concentrations and the types of uncertainties that may offer additional insights into the complexity of the unknown. We demonstrate the application of the method in a study of the COVID-19 literature.

Proposed Direct and Indirect Citation Weighting Methods Based on Citation Context Similarity

2020 ◽  
Author(s):  
Toluwase Asubiaro
Keyword(s):  
Semantic Similarity ◽  
Citation Count ◽  
Research Impact ◽  
Knowledge Flow ◽  
Citation Context ◽  
Context Similarity ◽  
Level Of Knowledge ◽  
Direct Citation ◽  
Phd Thesis ◽  
Citation Contexts

Hypothetically, if paper B has cited paper A, and paper C has cited paper B, but paper C has not cited paper A; citation count can neither communicate the probable influence of paper A on C nor weigh the influence of A in B. In this case, paper A receives a direct citation from paper B, while it receives an indirect citation from paper C. This PhD thesis proposes methods for weighting direct and indirect citations which are based on the semantic cita-tion context similarity. The direct citation weighting is based on the unique-ness of in-text citation contexts, where unique in-text citation contexts attract more weights. The indirect citations are weighted based on the knowledge flow between papers A and C, that is, the semantic similarity between the ci-tation context of paper B in paper A and citation context of paper C in paper B where level of knowledge flow depends on the semantic similarity. Bio-medical publications will be used while semantic similarity is calculated based on cosine similarity which is implemented using the Fasttext-based bi-osentvec word embedding models. The proposed methods have the potential of being useful in determining the research impact of articles, authors and in-stitutions. They can also be useful in sorting of documents retrieved from in-formation retrieval systems.

Relationships between method-section citation rates and citation contexts: evidence from highly cited references in psychology

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kai Li
Keyword(s):  
Peer Review ◽  
Large Scale ◽  
Material Objects ◽  
Method Section ◽  
Linguistic Features ◽  
Scientific Publications ◽  
Content Type ◽  
Research Material ◽  
Scholarly Data ◽  
Citation Contexts

PurposeThe Method section of research articles offers an important space for researchers to describe their research processes and research objects they utilize. To understand the relationship between these research materials and their representations in scientific publications, this paper offers a quantitative examination of the citation contexts of the most frequently cited references in the Method section of the paper sample, many of which belong to the category of research material objects.Design/methodology/approachIn this research, the authors assessed the extent to which these references appear in the Method section, which is regarded as an indicator of the instrumentality of the reference. The authors also examined how this central measurement is connected to its other citation contexts, such as key linguistic attributes and verbs that are used in citation sentences.FindingsThe authors found that a series of key linguistic attributes can be used to predict the instrumentality of a reference. The use of self-mention phrases and the readability score of the citances are especially strong predictors, along with boosters and hedges, the two measurements that were not included in the final model.Research limitations/implicationsThis research focuses on a single research domain, psychology, which limits the understanding of how research material objects are cited in different research domains or interdisciplinary research contexts. Moreover, this research is based on 200 frequently cited references, which are unable to represent all references cited in psychological publications.Practical implicationsWith the identified relationship between instrumental citation contexts and other characteristics of citation sentences, this research opens the possibility of more accurately identifying research material objects from scientific references, the most accessible scholarly data.Originality/valueThis is the first large-scale, quantitative analysis of the linguistic features of citations to research material objects. This study offers important baseline results for future studies focusing on scientific instruments, an increasingly important type of object involved in scientific research.Peer reviewThe peer review history for this article is available at: 10.1108/OIR-03-2021-0171

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