Your comments matter: incorporating viewers’ comments for ranking online video content using bibliometrics

IF 1.4 4区计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

New Review of Hypermedia and Multimedia Pub Date : 2018-10-02 DOI:10.1080/13614568.2019.1585486

N. Mirza, H. Khan, Tassawar Iqbal, Khalid Iqbal, Saqib Iqbal, Muhammad Imran

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引用次数: 0

Abstract

ABSTRACT The quality of user-generated content over World Wide Web media is a matter of serious concern for both creators and users. To measure the quality of content, webometric techniques are commonly used. In recent times, bibliometric techniques have been introduced to good effect for evaluation of the quality of user-generated content, which were originally used for scholarly data. However, the application of bibliometric techniques to evaluate the quality of YouTube content is limited to h-index and g-index considering only views. This paper advocates for and demonstrates the adaptation of existing Bibliometric indices including h-index, g-index and M-index exploiting both views and comments and proposes three indices hvc, gvc and mvc for YouTube video channel ranking. The empirical results prove that the proposed indices using views along with the comments outperform the existing approaches on a real-world dataset of YouTube.

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你的评论很重要:结合观众的评论，使用文献计量学对在线视频内容进行排名

万维网媒体上用户生成内容的质量是创作者和用户都非常关心的问题。为了测量内容的质量，通常使用网络测量技术。近年来，文献计量学技术在评估用户生成内容的质量方面取得了良好的效果，这些内容最初用于学术数据。然而，文献计量学技术在评价YouTube内容质量方面的应用仅限于仅考虑浏览量的h指数和g指数。本文倡导并论证了对现有文献计量指标h-index、g-index和M-index的改编，利用观点和评论并重，提出了YouTube视频频道排名的hvc、gvc和mvc三个指标。实证结果证明，在YouTube的真实数据集上，使用视图和评论的指数优于现有的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

New Review of Hypermedia and Multimedia COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

3.40

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The New Review of Hypermedia and Multimedia (NRHM) is an interdisciplinary journal providing a focus for research covering practical and theoretical developments in hypermedia, hypertext, and interactive multimedia.