How Long Will Your Videos Remain Popular? Empirical Study of the Impact of Video Features on YouTube Trending Using Deep Learning Methodologies

Min Gyeong Choe, Jae Hong Park, Dong Won Seo

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

3 Citations (Scopus)

Abstract

YouTube has become one of the most influential channels in recent years. There are an enormous number of videos on the platform, but few of them are popular, getting placed in the “Trending” section. But, videos on this list have different stories. Some of them will get constant popularity and others will fade out. Many researchers have analyzed what will make a video become popular. However, no study has focused on how long a video maintains its popularity. In addition, the content similarity between the thumbnail image and the title has been neglected, although it appears to play an important role in social media posts (e.g. blogs, Instagram). We measure the variable, content similarity, by analyzing the thumbnail image and text. This study investigates the impact of this new variable on popular videos’ survival to give YouTubers and advertisers insights into video marketing. Also, our suggested approach can achieve new academic results in the research of YouTube.

Original languageEnglish
Title of host publicationThe Ecosystem of e-Business
Subtitle of host publicationTechnologies, Stakeholders, and Connections - 17th Workshop on e-Business, WeB 2018, Revised Selected Papers
EditorsJennifer J. Xu, Bin Zhu, Xiao Liu, Michael J. Shaw, Han Zhang, Ming Fan
PublisherSpringer Verlag
Pages190-197
Number of pages8
ISBN (Print)9783030227838
DOIs
Publication statusPublished - 2019
Event17th Annual Workshop on e-Business, WeB 2018 - Santa Clara, United States
Duration: 12 Dec 201812 Dec 2018

Publication series

NameLecture Notes in Business Information Processing
Volume357
ISSN (Print)1865-1348
ISSN (Electronic)1865-1356

Conference

Conference17th Annual Workshop on e-Business, WeB 2018
Country/TerritoryUnited States
CitySanta Clara
Period12/12/1812/12/18

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

Keywords

  • Content similarity
  • Deep learning method
  • Video popularity
  • YouTube trending

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