Traditional Author Co-citation Analysis Research Articles

Telescopic and panoramic views of library and information science research 2011–2018: a comparison of four weighting schemes for author co-citation analysis

This study explores weighted author co-citation analysis (ACA) through a comparison of results from four weighted citation counting methods. The data set used comprises full-text research articles published in four top-tier library and information science (LIS) journals from 2011 to 2018. It finds that in-text frequency-weighted counting performs as well as traditional counting in identifying major dimensions of the LIS field but also shows more detail. Re-citation-based counting appears to highlight well-integrated specialties and weaken the presence of more fragmented ones compared to traditional counting. In-text frequency weighted re-citation counting, expected to highlight “deep” impact, appears to effectively zoom into the field to show intense streams of research within it, but fail to identify major dimensions of the field, essentially providing a telescopic view of the LIS field instead of the panoramic one that the other three methods provide. Measuring deep impact may be interesting and important for research evaluation but fails to retain the broader context that makes the visualizations of research fields so informative. It appears that what may be “noise” when considering impact of individuals can provide the context that allows us to see the forest for the trees when examining intellectual structures of research fields as in the case of traditional ACA.

Document- and Keyword-based Author Co-citation Analysis

AbstractIn the field of scientometrics, the principal purpose for author co-citation analysis (ACA) is to map knowledge domains by quantifying the relationship between co-cited author pairs. However, traditional ACA has been criticized since its input is insufficiently informative by simply counting authors’ co-citation frequencies. To address this issue, this paper introduces a new method that reconstructs the raw co-citation matrices by regarding document unit counts and keywords of references, named as Document- and Keyword-Based Author Co-Citation Analysis (DKACA). Based on the traditional ACA, DKACA counted co-citation pairs by document units instead of authors from the global network perspective. Moreover, by incorporating the information of keywords from cited papers, DKACA captured their semantic similarity between co-cited papers. In the method validation part, we implemented network visualization and MDS measurement to evaluate the effectiveness of DKACA. Results suggest that the proposed DKACA method not only reveals more insights that are previously unknown but also improves the performance and accuracy of knowledge domain mapping, representing a new basis for further studies.

Using the appearance of citations in full text on author co-citation analysis

As a frequently used method of depicting scientific intellectual structures, author co-citation analysis (ACA) has been applied to many domains. However, only count-based information is involved as the input of ACA, which is not sufficiently informative for knowledge representations. This article catches several metadata in full text of citing papers but not aims at content-level information, which increases the amount of information input to ACA without increasing computational complexity a lot. We propose a new method by involving information including the number of mentioned times in a citing paper and the number of context words in a citing sentence. We combine these pieces of information into the traditional ACA and compare the results between ACA and the proposed approach by using factor analysis, network analysis, and MDS-measurement. The result of our empirical study indicates that compared with the traditional ACA, the proposed method shows a better clustering performance in visualizations and reveals more details in displaying intellectual structures.

Yet another method for author co‐citation analysis: A new approach based on paragraph similarity

ABSTRACTCo‐citation analysis has been widely adopted to represent the intellectual structure of a discipline. In general, all co‐cited author pairs are regarded equal. With the development of computer technology and easy accessibility of machine readable full‐text articles, new weighting schemes for measuring co‐citation strength (CCS) have been proposed. However, in previous studies, only distance and sentence similarity are used to adjust CCS when applying co‐citation analysis. In this study, we propose a new approach to measuring CCS based on paragraph similarity. Investigation was carried out to compare our approach and traditional author co‐citation analysis (TACA), as well as other different parametric ACAs. Preliminary results show that TACA and distance‐based ACA (DACA) share many commonalities. In contrast, similarity‐based ACAs reveal the different structure from that of TACA and DACA. However, differences in resulting network structure were still found between paragraph‐similarity‐based ACA (PACA) and sentence‐similarity‐based ACA (SACA). Compared to SACA, PACA produces less number of factors and clusters and moderate size of clusters.

Identifying the topology of the K‐pop video community on YouTube: A combined Co‐comment analysis approach

YouTube is a successful social network that people use to upload, watch, and comment on videos. We believe comments left on these videos can provide insight into user interests, but to this point have not been used to map out a specific video community. Our study investigates whether and how user commenting behavior impacts the topology of the K‐pop video community through analysis of co‐commenting behavior on these videos. We apply a traditional author cocitation analysis to this behavior, in a process we refer to as co‐comment analysis, to detect the topology of this community. This involves: a) an analysis of user co‐comments to elicit the inclination of user homophily within the community; b) an analysis of user co‐comments, weighted frequency of co‐comments, to detect user interests in the community; and c) an analysis of user co‐comments, weighted sentiment scores, to capture user opinions by polarity. The results indicate that users who comment on specific K‐pop videos also tend to comment on topically similar YouTube videos. We also find that the number of comments made by users correlates with the degree of positivity of their comments. Conversely, users who comment negatively on K‐pop videos are not inclined to form specific user groups, but rather present only their opinions individually.

Visualizing information science: Author direct citation analysis in China and around the world

Abstract Author direct citation analysis (ADCA, also called inter-citation or cross citation) is a new feasible and applicable technique for exploring knowledge communication and discovering scientific structure. This study explored ADCA among prolific, highly cited, and core authors in information science in China and around the world. The results revealed the following. (1) The datasets in China and around the world cover overlapping, but also unique topics. Research subjects on information science around the world can be divided into three categories and 10 clusters; meanwhile, that in China can be divided into three categories and 9 clusters. Chinese scholars who are mostly involved in cross subjects and multi-fields are not as specialized and profound as foreign scholars. An obvious imbalance exists in the evolution of discipline structure around the world, indicating the necessity of a synchronous promotion of research specialty and cross comprehensiveness. Chinese scholars concentrate more on topics such as competitive intelligence, information resource management, and information retrieval, and they focus less on information security and user analysis. (2) Knowledge communication between active authors is stronger than the knowledge flow from highly influential authors to active authors around the world; meanwhile, Chinese researchers tend to adopt the knowledge of authoritative literature. The knowledge flow through bidirectional direct citation is related to mutual knowledge communication. Authoritative scholars are produced when prolific authors cite highly cited authors. The level of mutual recognition among Chinese scholars has not reached that among foreign scholars; in the former, less bidirectional flow of knowledge is involved, and unidirectional flow is limited to geographical proximity, cooperation, or teacher–student relationship. (3) In contrast to traditional author co-citation analysis (ACA), ADCA pays more attention to the mutual interaction among currently active scholars and to mainly showing the current research focus.

Content-based author co-citation analysis

Abstract Author co-citation analysis (ACA) has long been used as an effective method for identifying the intellectual structure of a research domain, but it relies on simple co-citation counting, which does not take the citation content into consideration. The present study proposes a new method for measuring the similarity between co-cited authors by considering author's citation content. We collected the full-text journal articles in the information science domain and extracted the citing sentences to calculate their similarity distances. We compared our method with traditional ACA and found out that our approach, while displaying a similar intellectual structure for the information science domain as the other baseline methods, also provides more details about the sub-disciplines in the domain than with traditional ACA.

Measuring author research relatedness: A comparison of word‐based, topic‐based, and author cocitation approaches

Relationships between authors based on characteristics of published literature have been studied for decades. Author cocitation analysis using mapping techniques has been most frequently used to study how closely two authors are thought to be in intellectual space based on how members of the research community co‐cite their works. Other approaches exist to study author relatedness based more directly on the text of their published works. In this study we present static and dynamic word‐based approaches using vector space modeling, as well as a topic‐based approach based on latent Dirichlet allocation for mapping author research relatedness. Vector space modeling is used to define an author space consisting of works by a given author. Outcomes for the two word‐based approaches and a topic‐based approach for 50 prolific authors in library and information science are compared with more traditional author cocitation analysis using multidimensional scaling and hierarchical cluster analysis. The two word‐based approaches produced similar outcomes except where two authors were frequent co‐authors for the majority of their articles. The topic‐based approach produced the most distinctive map.

Extending co-citation analysis to discover authors with multiple expertise

The author co-citation analysis (ACA) method is commonly used to group authors of reference papers. Since the traditional ACA method analyzes only first authors of reference papers, it disregards the contributions of other coauthors and can only group each first author into one cluster. This study proposes an innovative ACA algorithm called “complete author pair (CAP) algorithm”, which groups complete author sets of reference papers into clusters and thus finds authors who may have expertise in more than one area. The CAP algorithm is implemented in two citation data banks that collected paper references from two ACM journals during 2002–2005. The results show that the CAP algorithm runs up to 90% of average precision in each citation bank when comparing against ACM CCS.

Structure of personality psychology based on cocitation analysis of prominent authors

Traditional Author Cocitation Analysis was applied in order to map the personality psychology as a research field. A group of 25 authors were selected from the Haggbloom (1999, 2002) lists of the most cited and prominent psychologists, judged to be the main contributors to the field. All of their cocitations identified in the three year volumes of SSCI were downloaded to be analyzed by cluster analysis and MDS. The analysis resulted in four clusters comprising (1) theorists of individual differences amalgamated with authors of biological orientation with R. Cattell positioned centrally, (2) behaviorists joined by socio-cognitive theorists led by M.E.P. Seligman and A. Bandura, and (3) the group of psychoanalytic (dynamic) theorists with A. Adler in the middle. In fourth cluster G. Allport, H. Murray, K. Lewin, W. Mischel, and D. Buss were found mixed together to make a rather heterogeneous group. In two-axes representation one of the dimensions was understood as reflecting methodological and the other one content-specific differences among the authors, although this interpretation is not univocal. The same procedure was repeated on citations given to the same authors in SocioFakt - the Serbian Citation Index for Social Sciences, revealing a reduced picture of the domain. At this portrait, some important authors are missing as a result of their low citation rate, suggesting that in the Serbian personality psychology entire research fields don?t exist. .

Author cocitation analysis and Pearson's r

AbstractIn their article “Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient,” Ahlgren, Jarneving, and Rousseau fault traditional author cocitation analysis (ACA) for using Pearson's r as a measure of similarity between authors because it fails two tests of stability of measurement. The instabilities arise when rs are recalculated after a first coherent group of authors has been augmented by a second coherent group with whom the first has little or no cocitation. However, AJ&R neither cluster nor map their data to demonstrate how fluctuations in rs will mislead the analyst, and the problem they pose is remote from both theory and practice in traditional ACA. By entering their own rs into multidimensional scaling and clustering routines, I show that, despite r's fluctuations, clusters based on it are much the same for the combined groups as for the separate groups. The combined groups when mapped appear as polarized clumps of points in two‐dimensional space, confirming that differences between the groups have become much more important than differences within the groups—an accurate portrayal of what has happened to the data. Moreover, r produces clusters and maps very like those based on other coefficients that AJ&R mention as possible replacements, such as a cosine similarity measure or a chi square dissimilarity measure. Thus, r performs well enough for the purposes of ACA. Accordingly, I argue that qualitative information revealing why authors are cocited is more important than the cautions proposed in the AJ&R critique. I include notes on topics such as handling the diagonal in author cocitation matrices, lognormalizing data, and testing r for significance.

Pathfinder networks and author cocitation analysis: A remapping of paradigmatic information scientists

AbstractIn their 1998 article “Visualizing a discipline: An author cocitation analysis of information science, 1972–1995,” White and McCain used multidimensional scaling, hierarchical clustering, and factor analysis to display the specialty groupings of 120 highly‐cited (“paradigmatic”) information scientists. These statistical techniques are traditional in author cocitation analysis (ACA). It is shown here that a newer technique, Pathfinder Networks (PFNETs), has considerable advantages for ACA. In PFNETs, nodes represent authors, and explicit links represent weighted paths between nodes, the weights in this case being cocitation counts. The links can be drawn to exclude all but the single highest counts for author pairs, which reduces a network of authors to only the most salient relationships. When these are mapped, dominant authors can be defined as those with relatively many links to other authors (i.e., high degree centrality). Links between authors and dominant authors define specialties, and links between dominant authors connect specialties into a discipline. Maps are made with one rather than several computer routines and in one rather than many computer passes. Also, PFNETs can, and should, be generated from matrices of raw counts rather than Pearson correlations, which removes a computational step associated with traditional ACA. White and McCain's raw data from 1998 are remapped as a PFNET. It is shown that the specialty groupings correspond closely to those seen in the factor analysis of the 1998 article. Because PFNETs are fast to compute, they are used in AuthorLink, a new Web‐based system that creates live interfaces for cocited author retrieval on the fly.

Visualizing a knowledge domain's intellectual structure

To make knowledge visualizations clear and easy to interpret, we have developed a method that extends and transforms traditional author co-citation analysis by extracting structural patterns from the scientific literature and representing them in a 3D knowledge landscape.