Investigating factors for assessing the quality of academic user-generated content on social media

Lei Li, Linlin Zhang, Ao Wang, Kun Huang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

As scholars increasingly use academic user-generated content from various social media to support their research, evaluating the quality of this content becomes challenging. This study recruited researchers to conduct retrieval experiments and participate in post-experiment interviews, aiming to identify the factors that affect their evaluation of various types of academic user-generated content. By analyzing the interview data, 23 factors affecting the evaluation were obtained. This pilot study provides foundations for building a quality evaluation model for academic user-generated content.

Original languageEnglish
Title of host publicationJCDL 2020 - Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages511-512
Number of pages2
ISBN (Electronic)9781450375856
DOIs
StatePublished - 1 Aug 2020
Externally publishedYes
Event2020 ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2020 - Virtual, Online, China
Duration: 1 Aug 20205 Aug 2020

Publication series

NameProceedings of the ACM/IEEE Joint Conference on Digital Libraries
ISSN (Print)1552-5996

Conference

Conference2020 ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2020
Country/TerritoryChina
CityVirtual, Online
Period1/08/205/08/20

Keywords

  • Academic user-generated content
  • Quality assessment
  • Social media

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