Does YouTube help to reduce plagiarism? A content analysis of YouTube videos and sentiment analysis through viewers comments

Purpose Plagiarism can occur in research in various ways, whether intentionally or unintentionally. This study aims to assess the role of YouTube in providing information and creating awareness about plagiarism through videos. It also investigates the topics related to plagiarism covered in YouTube videos and evaluates viewer satisfaction with the content provided. Design/methodology/approach Using the software Webometric Analyst and various search strings, a total of 526 YouTube videos and 18,050 comments were retrieved on April 23, 2023. The “syuzhet” package has been installed on R-studio to comprehend the sentiment associated with the videos. Findings The greatest number of videos (184) were submitted describing plagiarism software-related information, followed by 122 videos describing examples of plagiarism. The sentiment analysis of viewers’ associated comments revealed that the majority (111.36 %) were positive, indicating that viewers are satisfied with the available videos. Research limitations/implications This study has potential limitations. The number of videos assigned to each content category may be biased to some extent because the videos were not fully viewed and the content categories were assigned manually by analyzing the video title. While analyzing comments for sentiment analysis, some comments are difficult for the software to assign a type of sentiment to, so the percentage of sentiment may be slightly biased. Originality/value All data used in this research are original and verified manually. The data for this study was obtained using software (Webometric Analyst) and cross-checked on a regular basis. The titles of the videos were individually inspected and evaluated to analyze the content and emphasis area of previously posted films. This allows the authors to identify the areas of plagiarism when no or few videos have been submitted.

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Publication Details

Journal
Global Knowledge Memory and Communication
Published
2024-11-27
DOI
https://doi.org/10.1108/gkmc-09-2023-0357
Citations
3
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
1.04
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article

Does YouTube help to reduce plagiarism? A content analysis of YouTube videos and sentiment analysis through viewers comments

Lulu Rout, Shubhasmita Acharya
3 citations
Global Knowledge Memory and Communication
Hate Speech and Cyberbullying Detection
1.04
article

Does YouTube help to reduce plagiarism? A content analysis of YouTube videos and sentiment analysis through viewers comments

Lulu Rout, Shubhasmita Acharya
article en
3 citations

Abstract

Purpose Plagiarism can occur in research in various ways, whether intentionally or unintentionally. This study aims to assess the role of YouTube in providing information and creating awareness about plagiarism through videos. It also investigates the topics related to plagiarism covered in YouTube videos and evaluates viewer satisfaction with the content provided. Design/methodology/approach Using the software Webometric Analyst and various search strings, a total of 526 YouTube videos and 18,050 comments were retrieved on April 23, 2023. The “syuzhet” package has been installed on R-studio to comprehend the sentiment associated with the videos. Findings The greatest number of videos (184) were submitted describing plagiarism software-related information, followed by 122 videos describing examples of plagiarism. The sentiment analysis of viewers’ associated comments revealed that the majority (111.36 %) were positive, indicating that viewers are satisfied with the available videos. Research limitations/implications This study has potential limitations. The number of videos assigned to each content category may be biased to some extent because the videos were not fully viewed and the content categories were assigned manually by analyzing the video title. While analyzing comments for sentiment analysis, some comments are difficult for the software to assign a type of sentiment to, so the percentage of sentiment may be slightly biased. Originality/value All data used in this research are original and verified manually. The data for this study was obtained using software (Webometric Analyst) and cross-checked on a regular basis. The titles of the videos were individually inspected and evaluated to analyze the content and emphasis area of previously posted films. This allows the authors to identify the areas of plagiarism when no or few videos have been submitted.

Global Knowledge Memory and Communication
Mizoram University (IN), Berhampur University (IN)
Openalex Percentile: Top 18%
Hate Speech and Cyberbullying Detection
1.04
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