Identifying the Patterns of Author-Generated Tags to Library and Information Science Papers in The Academic Social Networks: Focusing on Academia.edu.
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| Title: | Identifying the Patterns of Author-Generated Tags to Library and Information Science Papers in The Academic Social Networks: Focusing on Academia.edu. |
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| Authors: | Saadat, Rasul1 saadat.rasul@gmail.com, Shabani, Ahmad2 shabania@edu.ui.ac.ir, Asemi, Asefeh3 asemi.asefeh@unicorvinus.hu, Sohrabi, Mehrdad Cheshmeh2 mo.sohrabi@edu.ui.ac.ir, Ravari, Mohammad Tavakolizadeh4 tavakoli@yazd.ac.ir |
| Source: | Knowledge Organization. 2024, Vol. 51 Issue 1, p26-37. 12p. |
| Subjects: | Social networks, Library schools, Folksonomies, Tags (Metadata), Distribution (Probability theory) |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Knowledge Organization is the property of IMR Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 176421622 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Identifying the Patterns of Author-Generated Tags to Library and Information Science Papers in The Academic Social Networks: Focusing on Academia.edu. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Saadat%2C+Rasul%22">Saadat, Rasul</searchLink><relatesTo>1</relatesTo><i> saadat.rasul@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Shabani%2C+Ahmad%22">Shabani, Ahmad</searchLink><relatesTo>2</relatesTo><i> shabania@edu.ui.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Asemi%2C+Asefeh%22">Asemi, Asefeh</searchLink><relatesTo>3</relatesTo><i> asemi.asefeh@unicorvinus.hu</i><br /><searchLink fieldCode="AR" term="%22Sohrabi%2C+Mehrdad+Cheshmeh%22">Sohrabi, Mehrdad Cheshmeh</searchLink><relatesTo>2</relatesTo><i> mo.sohrabi@edu.ui.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Ravari%2C+Mohammad+Tavakolizadeh%22">Ravari, Mohammad Tavakolizadeh</searchLink><relatesTo>4</relatesTo><i> tavakoli@yazd.ac.ir</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Knowledge+Organization%22">Knowledge Organization</searchLink>. 2024, Vol. 51 Issue 1, p26-37. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Social+networks%22">Social networks</searchLink><br /><searchLink fieldCode="DE" term="%22Library+schools%22">Library schools</searchLink><br /><searchLink fieldCode="DE" term="%22Folksonomies%22">Folksonomies</searchLink><br /><searchLink fieldCode="DE" term="%22Tags+%28Metadata%29%22">Tags (Metadata)</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Knowledge Organization is the property of IMR Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.5771/0943-7444-2024-1-26 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 26 Subjects: – SubjectFull: Social networks Type: general – SubjectFull: Library schools Type: general – SubjectFull: Folksonomies Type: general – SubjectFull: Tags (Metadata) Type: general – SubjectFull: Distribution (Probability theory) Type: general Titles: – TitleFull: Identifying the Patterns of Author-Generated Tags to Library and Information Science Papers in The Academic Social Networks: Focusing on Academia.edu. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Saadat, Rasul – PersonEntity: Name: NameFull: Shabani, Ahmad – PersonEntity: Name: NameFull: Asemi, Asefeh – PersonEntity: Name: NameFull: Sohrabi, Mehrdad Cheshmeh – PersonEntity: Name: NameFull: Ravari, Mohammad Tavakolizadeh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 09437444 Numbering: – Type: volume Value: 51 – Type: issue Value: 1 Titles: – TitleFull: Knowledge Organization Type: main |
| ResultId | 1 |
