Document (#44236)

Author

Saadat, R.
Shabani, A.
Asemi, A.

Title

Identifying the patterns of author-generated tags to library and information science papers in the academic social networks : focusing on Academia.edu

Source

Knowledge organization. 51(2024) no.1, S.26-37

Year

2024

Abstract

This research aims to identify some patterns of author (as user) generated tags to the papers of library and information science field in Academia.edu. The research method is typically based on text analysis and word frequency distribution. The population contains over 6000 papers tagged in Academia.edu, and their abstracts were extracted from 159 English journals of the library and information science (LIS) field in the Scopus database. The growth of different types of tags in terms of the number of their words (one-word, two-word, three-word, and four-word and more), as well as the total number of tags over time, appeared as a logistic curve. It was also found that two-word tags had the most matching (54.92%) and four-word tags or more the least matching (1.76%) with different sections of papers (title, abstract, and authors' keywords). The total tags matched 7.5% with the title, 76.61% with the abstract, and 15.89% with the authors' keywords. Regarding the reuse of tags, it was revealed that on the one hand, 38.8% of the tags had been reused; on the other hand, 16% of the tags were reused in the first year, and more than 50% of the tags were reused in the first three years. Finally, it can be said that the users' consensus on specific terms can identify the new patterns of users' tagging at least partially compatible with professional indexing concepts, and by focusing on the most widely used tags and their sustainable distribution, the weighting of indexing terms and even classification schemes may be achieved.

Content

Vgl.: https://www.nomos-elibrary.de/10.5771/0943-7444-2024-1/ko-knowledge-organization-jahrgang-51-2024-heft-1?page=1.

Theme

Social tagging
Metadaten

Field

Bibliothekswesen
Informationswissenschaft

Object

Academia.edu
Scopus

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5. Chen, Y.-N.; Ke, H.-R.: ¬A study on mental models of taggers and experts for article indexing based on analysis of keyword usage (2014) 0.27

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