More Web Proxy on the site http://driver.im/

short-paper

User Preferences for Automated Curation of Snackable Content

Authors:

Michael J GonzalesAuthors Info & Claims

IUI '21: Proceedings of the 26th International Conference on Intelligent User Interfaces

Pages 270 - 274

https://doi.org/10.1145/3397481.3450690

Published: 14 April 2021 Publication History

Abstract

As the volume of content and the connectivity of social media have grown, snackable content has increasingly become an enjoyable and engaging way to share content. Snackable content is a shortened form of original content focusing on a single theme or motif for entertainment and quick understanding of a video moment. For content owners with a large library of long-form content (movies, television series, documentaries, etc.), one challenge in accommodating snackable content in social media uses is the correct identification and cutting of interesting regions. Related problems have been studied for algorithmic discovery of content for movie trailers, short-duration meme content, and medium duration news stories, but none of these approaches included user preferences as explicit drivers for cuts. This paper analyzes both human and automatic methods for creating snackable clips across different categories of content with two comprehensive user studies. Contrary to initial expectations, findings amongst the surveyed population indicate a preference for slightly longer snackable clips (60-90 seconds) and those that began or ended with a human character.

References

[1]

G. Adomavicius and A. Tuzhilin. 2005. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 6 (June 2005), 734–749. https://doi.org/10.1109/TKDE.2005.99

Digital Library

[2]

Sonakshi Babbar. 2014. 5 Attention-Grabbing Snackable Content Ideas. https://vwo.com/blog/snackable-content/

[3]

Lorenzo Baraldi, Costantino Grana, and Rita Cucchiara. 2016. Recognizing and Presenting the Storytelling Video Structure with Deep Multimodal Networks. arXiv:1610.01376 [cs] (Nov. 2016). http://arxiv.org/abs/1610.01376 arXiv: 1610.01376.

[4]

Tim Dams. 2019. Quibi: Assessing the appetite for snackable streaming. https://www.ibc.org/trends/quibi-assessing-the-appetite-for-snackable-streaming/5272.article

[5]

Media Distillery. 2020. Snackable Content™: Short content based on consumer interests. https://www.mediadistillery.com/tv-operators/snackable-content-2/

[6]

Shawn Hershey, Sourish Chaudhuri, Daniel P. W. Ellis, Jort F. Gemmeke, Aren Jansen, R. Channing Moore, Manoj Plakal, Devin Platt, Rif A. Saurous, Bryan Seybold, Malcolm Slaney, Ron J. Weiss, and Kevin Wilson. 2017. CNN Architectures for Large-Scale Audio Classification. arXiv:1609.09430 [cs, stat] (Jan. 2017). http://arxiv.org/abs/1609.09430 arXiv: 1609.09430.

[7]

Nielsen. 2018. Younger, More Affluent Viewers Power Out-of-Home TV Viewing Lift. https://www.nielsen.com/us/en/insights/article/2018/younger-more-affluent-viewers-power-out-of-home-tv-viewing-lift

[8]

Nielsen. 2019. Game Changer: Rethinking Sports Experiences for Generation Z. https://nielsensports.com/game-changer-rethinking-sports-experiences-for-generation-z/

[9]

Nielsen. 2020. Nielsen’s February 2020 Total Audience Report Special Edition: Streaming Wars – Nielsen. https://www.nielsen.com/us/en/insights/report/2020/the-nielsen-total-audience-report-february-2020/

[10]

Mayu Otani, Yuta Nakashima, Esa Rahtu, and Janne Heikkilä. 2019. Rethinking the Evaluation of Video Summaries. arXiv:1903.11328 [cs] (April 2019). http://arxiv.org/abs/1903.11328 arXiv: 1903.11328.

[11]

Jae Sung Park, Marcus Rohrbach, Trevor Darrell, and Anna Rohrbach. 2019. Adversarial Inference for Multi-Sentence Video Description. arXiv:1812.05634 [cs] (April 2019). http://arxiv.org/abs/1812.05634 arXiv: 1812.05634.

[12]

Snackable. 2019. The Approaching Audio-First Web. https://www.snackable.ai/blog/2019/12/14/the-approaching-audio-first-web

[13]

Maxim Tkachenko, Mikhail Malyuk, Nikita Shevchenko, Andrey Holmanyuk, and Nikolai Liubimov. 2021. Label Studio: Data Labeling Software. https://github.com/heartexlabs/label-studio original-date: 2019-06-19T02:00:44Z.

[14]

Lezi Wang, Dong Liu, Rohit Puri, and Dimitris N. Metaxas. 2020. Learning Trailer Moments in Full-Length Movies. arXiv:2008.08502 [cs] (Aug. 2020). http://arxiv.org/abs/2008.08502 arXiv: 2008.08502.

[15]

Wikipedia. 2020. Netflix Prize. https://en.wikipedia.org/w/index.php?title=Netflix_Prize&oldid=970689854 Page Version ID: 970689854.

[16]

Yale Song, Jordi Vallmitjana, Amanda Stent, and Alejandro Jaimes. 2015. TVSum: Summarizing web videos using titles. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 5179–5187. https://doi.org/10.1109/CVPR.2015.7299154ISSN: 1063-6919.

[17]

Yifan Zhong, Tahir Lazaro Sousa Menezes, Vikas Kumar, Qian Zhao, and F. Maxwell Harper. 2018. A field study of related video recommendations: newest, most similar, or most relevant?. In Proceedings of the 12th ACM Conference on Recommender Systems(RecSys ’18). Association for Computing Machinery, Vancouver, British Columbia, Canada, 274–278. https://doi.org/10.1145/3240323.3240395

Digital Library

Cited By

Infante Pineda SGarcía Vega AMartínez Borda RBarrajón Lara I(2025)Perfil periodístico y competencias digitales en redes sociales: estudio de caso de Ángel Martín en TikTokThe journalist’s profile and their digital competence in social media: the case study of Ángel Martín on TikTokDoxa Comunicación. Revista Interdisciplinar de Estudios de Comunicación y Ciencias Sociales10.31921/doxacom.n40a2723Online publication date: 1-Jan-2025
https://doi.org/10.31921/doxacom.n40a2723
Argaw DSoldan MPardo AZhao CHeilbron FChung JGhanem B(2024)Towards Automated Movie Trailer Generation2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00711(7445-7454)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00711
Skeen STokarz SGasik RSolano CSmith ESagoe MHudson LSteele KTheall KClum G(2023)A Trauma-Informed, Geospatially Aware, Just-in-Time Adaptive mHealth Intervention to Support Effective Coping Skills Among People Living With HIV in New Orleans: Development and Protocol for a Pilot Randomized Controlled TrialJMIR Research Protocols10.2196/4715112(e47151)Online publication date: 24-Oct-2023
https://doi.org/10.2196/47151
Show More Cited By

Index Terms

User Preferences for Automated Curation of Snackable Content
1. Human-centered computing
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

Recommendations

Distributed Content Curation on the Web
Special Issue on Pricing and Incentives in Networks and Systems and Regular Papers

In recent years there has been an explosive growth of digital content in the form of news feeds, videos, and original content on online platforms such as blogs and social networks. Indeed, such platforms have been used as a means of sharing and ...
Content Curation for Spatial Experience of Architectural Heritage
Augmented space can be usefully applied to cultural heritage experience, because it is possible to experience physical space and acquire information at the same time. Experiencers can expand their experiences through virtual content while feeling the ...
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, ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

IUI '21: Proceedings of the 26th International Conference on Intelligent User Interfaces

April 2021

618 pages

ISBN:9781450380171

DOI:10.1145/3397481

Copyright © 2021 ACM.

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 ACM 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 April 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Conference

IUI '21

Sponsor:

IUI '21: 26th International Conference on Intelligent User Interfaces

April 14 - 17, 2021

TX, College Station, USA

Acceptance Rates

Overall Acceptance Rate 746 of 2,811 submissions, 27%

Upcoming Conference

IUI '25

Sponsor:
sigai
sigai

30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
216
Total Downloads

Downloads (Last 12 months)41
Downloads (Last 6 weeks)2

Reflects downloads up to 18 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Infante Pineda SGarcía Vega AMartínez Borda RBarrajón Lara I(2025)Perfil periodístico y competencias digitales en redes sociales: estudio de caso de Ángel Martín en TikTokThe journalist’s profile and their digital competence in social media: the case study of Ángel Martín on TikTokDoxa Comunicación. Revista Interdisciplinar de Estudios de Comunicación y Ciencias Sociales10.31921/doxacom.n40a2723Online publication date: 1-Jan-2025
https://doi.org/10.31921/doxacom.n40a2723
Argaw DSoldan MPardo AZhao CHeilbron FChung JGhanem B(2024)Towards Automated Movie Trailer Generation2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00711(7445-7454)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00711
Skeen STokarz SGasik RSolano CSmith ESagoe MHudson LSteele KTheall KClum G(2023)A Trauma-Informed, Geospatially Aware, Just-in-Time Adaptive mHealth Intervention to Support Effective Coping Skills Among People Living With HIV in New Orleans: Development and Protocol for a Pilot Randomized Controlled TrialJMIR Research Protocols10.2196/4715112(e47151)Online publication date: 24-Oct-2023
https://doi.org/10.2196/47151
Cortés Quesada JBarceló Ugarte TFuentes Cortina G(2022)El consumo audiovisual de los Millennials y la Generación Z: preferencia por los contenidos snackablesDoxa Comunicación. Revista Interdisciplinar de Estudios de Comunicación y Ciencias Sociales10.31921/doxacom.n36a1687(303-320)Online publication date: 28-Sep-2022
https://doi.org/10.31921/doxacom.n36a1687

View Options

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents