research-article

Measuring Audience Retention in YouTube

Authors:

Eitan Altman,

Tania JiménezAuthors Info & Claims

VALUETOOLS 2019: Proceedings of the 12th EAI International Conference on Performance Evaluation Methodologies and Tools

Pages 79 - 85

https://doi.org/10.1145/3306309.3306322

Published: 12 March 2019 Publication History

Get Access

Abstract

There exist many aspects involved in a video turning viral on YouTube. These include properties of the video such as the attractiveness of its title and thumbnail, the recommendation policy of YouTube, marketing and advertising policies and the influence that the video's creator or owner has in social networks. In this work, we study audience retention measures provided by YouTube to video creators which may provide valuable information for improving the videos and for better understanding the viewers' potential interests in them. We then study the question of when is a video too long and can gain from being shortened. We examine consistency between several existing audience retention measures. We end in a proposal for a new audience retention measure and identify its advantages.

References

[1]

Eitan Altman, "A stochastic game approach for competition over popularity in social networks", Dynamic Games and Applications, special Issue on stochastic games, Dynamic Games and Applications 3, 2 (2013) 313--323

Google Scholar

[2]

Eitan Altman, Yonathan Portilla, "Social Networks: A Cradle of Globalized Culture in the Mediterranean Region." International Conference on Advances in Social Networks Analysis and Mining (ICASNAM 2015), Jan 2015, Hammamet, Tunisia. See related video (French) at https://www.youtube.com/watch?v=2H_HQwMK5pg

Google Scholar

[3]

Eitan Altman, Nahum Shimkin, "The Ordered Timeline Game: Strategic Posting Times Over a Temporally Ordered Shared Medium", Dynamic Games and Applications, Springer Verlag, 2015, pp.1--25.

Google Scholar

[4]

Dawkins, Richard (1989), "The Selfish Gene", Oxford University Press, ISBN 0-19-286092-5.

Google Scholar

[5]

Ezra Fishman and Alyce Currier, Understanding Audience Retention, WISTIA, https://wistia.com/library/understanding-audience-retention

Google Scholar

[6]

David Lee King, "Managing your library's social media channels", Library Technology Reports, Chapter 5, alatechsource.org January 2015.

Google Scholar

[7]

Yonathan Portilla, Alexandre Reiffers, Eitan Altman, Rachid El-Azouzi, "A Study of YouTube recommendation graph based on measurements and stochastic tools", 3rd International Workshop on Big Data and Social Networking Management and Security, 2015.

Digital Library

Google Scholar

[8]

Raul Rodriguez-Fernandiz, Victoria Tur-Vines and Kiko Mora Contreras, "The tube on YouTube: TV series, Media strategies and user tactics in a transmedia environment", International Journal of Communications 10(2016), pages 1991--2013.

Google Scholar

[9]

Cedric Richier, Eitan Altman, Rachid El-Azouzi, Tania Jimenez, Georges Linares and Yonathan Portilla, "Bio-Inspired Models for Characterizing YouTube View-count", 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

Digital Library

Google Scholar

[10]

Valerio Arnaboldi, Marco Conti, Andrea Passarella and Fabio Pezzoni; Analysis of Ego Network Structure in Online Social Networks, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing

Digital Library

Google Scholar

[11]

James Cook, https://www.youtube.com/watch?v=SM08TsCldWI

Google Scholar

[12]

Olivia Solon, "Richard Dawkins on the internet's hijacking of the word 'meme'". Wired UK. July 9, 2013. Available on http://www.webcitation.org/6HzDGE9Go.

Google Scholar

[13]

M.Vinson, B Goerlich, M. Loper, M. Martin and A. Yazdani, "System and method for measurinng television audience engagement", Patent US 8904419 B2

Google Scholar

[14]

Lorenzo Maggi, Lazaros Gkatzikis, Georgios Paschos and Jeremie Leguay, "Adapting Caching to Audience Retention Rate: Which Video Chunk to Store?" arXiv:1512.03274 2017.

Google Scholar

Cited By

View all

Lekharu AGupta PSur APatra M(2024)Collaborative Video Caching in the Edge Network using Deep Reinforcement LearningACM Transactions on Internet of Things10.1145/36646135:3(1-26)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3664613
Judel Svom Felde JSchroeder U(2024)Video Analytics in Moodle Using xAPITechnology, Knowledge and Learning10.1007/s10758-023-09720-329:4(1939-1963)Online publication date: 20-Jan-2024
https://doi.org/10.1007/s10758-023-09720-3
González-Neira AFiaño Salinas C(2023)La televisión más allá de la televisión: YouTube y los canales autonómicosDoxa Comunicación. Revista Interdisciplinar de Estudios de Comunicación y Ciencias Sociales10.31921/doxacom.n37a1838(401-429)Online publication date: 1-Jul-2023
https://doi.org/10.31921/doxacom.n37a1838
Show More Cited By

Index Terms

Measuring Audience Retention in YouTube
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia content creation

Recommendations

The effects of computer-assisted instruction on the achievement, attitudes and retention of fourth grade mathematics students in North Cyprus

The purpose of this study was to examine the effects of the educational software Frizbi Mathematics 4 on 4th grade student's mathematics achievement, retention, attitudes toward mathematics and attitude toward computer assisted learning. Two groups (...
Exploring the user-generated content (UGC) uploading behavior on youtube
WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

YouTube is the world's largest video sharing platform where both professional and non-professional users participate in creating, uploading, and viewing content. In this work, we analyze content in the music category created by the non-professionals, ...
The Audience and Business of YouTube and Online Videos

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

VALUETOOLS 2019: Proceedings of the 12th EAI International Conference on Performance Evaluation Methodologies and Tools

March 2019

202 pages

ISBN:9781450365963

DOI:10.1145/3306309

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

In-Cooperation

EAI: The European Alliance for Innovation
Universitat de les Illes Balears: Universitat de les Illes Balears

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 March 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

VALUETOOLS 2019

VALUETOOLS 2019: 12th EAI International Conference on Performance Evaluation Methodologies and Tools

March 12 - 15, 2019

Palma, Spain

Acceptance Rates

VALUETOOLS 2019 Paper Acceptance Rate 18 of 42 submissions, 43%;

Overall Acceptance Rate 90 of 196 submissions, 46%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
620
Total Downloads

Downloads (Last 12 months)132
Downloads (Last 6 weeks)18

Reflects downloads up to 12 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Lekharu AGupta PSur APatra M(2024)Collaborative Video Caching in the Edge Network using Deep Reinforcement LearningACM Transactions on Internet of Things10.1145/36646135:3(1-26)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3664613
Judel Svom Felde JSchroeder U(2024)Video Analytics in Moodle Using xAPITechnology, Knowledge and Learning10.1007/s10758-023-09720-329:4(1939-1963)Online publication date: 20-Jan-2024
https://doi.org/10.1007/s10758-023-09720-3
González-Neira AFiaño Salinas C(2023)La televisión más allá de la televisión: YouTube y los canales autonómicosDoxa Comunicación. Revista Interdisciplinar de Estudios de Comunicación y Ciencias Sociales10.31921/doxacom.n37a1838(401-429)Online publication date: 1-Jul-2023
https://doi.org/10.31921/doxacom.n37a1838
Chau D(2023)The Effect of Video Topic on the Popularity of YouTube ChannelsSSRN Electronic Journal10.2139/ssrn.4597573Online publication date: 2023
https://doi.org/10.2139/ssrn.4597573
Hill VGrant WMcMahon MSinghal I(2022)How prominent science communicators on YouTube understand the impact of their workFrontiers in Communication10.3389/fcomm.2022.10144777Online publication date: 1-Dec-2022
https://doi.org/10.3389/fcomm.2022.1014477
Thiel OMarie Hanssen S(2022)Bruk av video i barnehagelærerutdanning. En analyse av seerdataeneUniped10.18261/uniped.45.2.345:2(93-114)Online publication date: 15-Jun-2022
https://doi.org/10.18261/uniped.45.2.3
Bansal IGupta KAakriti Sehgal BSharma V(2022)A Social Network Approach for Automated Generation of YouTube Playlists2022 IEEE International Conference on Data Science and Information System (ICDSIS)10.1109/ICDSIS55133.2022.9916006(1-6)Online publication date: 29-Jul-2022
https://doi.org/10.1109/ICDSIS55133.2022.9916006
González-Neira AVázquez-Herrero JQuintas-Froufe N(2021)Convergence of linear television and digital platforms: An analysis of YouTube offer and consumptionEuropean Journal of Communication10.1177/0267323121105472037:4(426-442)Online publication date: 8-Nov-2021
https://doi.org/10.1177/02673231211054720
Ducasse JKljun MAttygalle NPucihar K(2021)Interactive Web Documentaries: A Case Study of Video Viewing Behaviour on iOtokInternational Journal of Human–Computer Interaction10.1080/10447318.2021.197651138:10(949-972)Online publication date: 27-Sep-2021
https://doi.org/10.1080/10447318.2021.1976511
Jent JAndrew Rothenberg WWeinstein AStokes JBarnett MSrivatsa NDandes EGarcia D(2021)Comparing Traditional and eBook Augmented Parent-Child Interaction Therapy (PCIT): A Randomized Control Trial of Pocket PCITBehavior Therapy10.1016/j.beth.2021.02.013Online publication date: Mar-2021
https://doi.org/10.1016/j.beth.2021.02.013
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

The effects of computer-assisted instruction on the achievement, attitudes and retention of fourth grade mathematics students in North Cyprus

Exploring the user-generated content (UGC) uploading behavior on youtube

The Audience and Business of YouTube and Online Videos