abstract

Inferring User Engagement from Interaction Data

Authors:

Jonathan Carlton,

Andy Brown,

Caroline Jay,

John KeaneAuthors Info & Claims

CHI EA '19: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

Paper No.: LBW1212, Pages 1 - 6

https://doi.org/10.1145/3290607.3313009

Published: 02 May 2019 Publication History

Get Access

Abstract

This paper presents preliminary results of a study designed to quantify users' engagement levels with interactive media content, through self-reported measures and interaction data. The broad hypothesis of the study is that interaction data can be used to predict the level of engagement felt by the user. The challenge addressed in this work is to explore the effectiveness of interaction data to act as a proxy for engagement levels and reveal what that data shows about engagement with media content. Preliminary results suggest several interesting insights about participants engagement and behaviour. Crucially, temporal statistics support the hypothesis that the participant making use of the controls in the interactive, video-based experience positively correlates with higher engagement.

References

[1]

I. Arapakis and L. A. Leiva. 2016. Predicting user engagement with direct displays using mouse cursor information. In Proc of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 599--608.

Digital Library

Google Scholar

[2]

J. Carlton, A. Brown, J. Keane, and C. Jay. 2018. Using Low-Level Interaction Data to Explore User Behaviour in InteractiveMedia Experiences. In Procs of the 11th International Conference: Measuring Behaviour 2018.

Google Scholar

[3]

J. Carlton, J. Woodcock, A. Brown, C. Jay, and J. Keane. 2018. Identifying latent indicators of technical difficulties from interaction data. In ACM KDD Workshop on Data Science, Journalism and Media (DSJM).

Google Scholar

[4]

D. Lagun and M. Lalmas. 2016. Understanding user attention and engagement in online news reading. In Proc of the 9th ACM International Conference on Web Search and Data Mining. ACM, 113--122.

Digital Library

Google Scholar

[5]

L. A. Leiva and R. Vivó. 2013. Web Browsing Behavior Analysis and Interactive Hypervideo. ACM Trans on the Web 7, 4 (2013).

Digital Library

Google Scholar

[6]

B. Miroglio and et. al. 2018. The Effect of Ad Blocking on User Engagement with the Web. In Proc of the 2018 World Wide Web Conference. 813--821.

Digital Library

Google Scholar

[7]

H.L. O'Brien, P. Cairns, and M. Hall. 2018. A practical approach to measuring user engagement with the refined user engagement scale (UES) and new UES short form. International Journal of Human-Computer Studies 112 (2018), 28--39.

Crossref

Google Scholar

[8]

H.L. O'Brien and M. Lebow. 2013. Mixed-methods approach to measuring user experience in online news interactions. Journal of the American Society for Information Science and Technology 64, 8 (2013), 1543--1556.

Crossref

Google Scholar

[9]

K. Williams and I. Zitouni. 2017. Does That Mean You're Happy? RNN-based Modeling of User Interaction Sequences to Detect Good Abandonment. In Procs of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 727--736.

Digital Library

Google Scholar

[10]

M. Zhuang, G. Demartini, and E. G. Toms. 2017. Understanding engagement through search behaviour. In Proc of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 1957--1966.

Digital Library

Google Scholar

Cited By

View all

Hong D(2023)How much is a “feedback” worth? User engagement and interaction for computer-supported adaptive quizzingInteractive Learning Environments10.1080/10494820.2023.2176521(1-16)Online publication date: 1-Mar-2023
https://doi.org/10.1080/10494820.2023.2176521
Rico Garcia OFernandez Fernandez JBecerra Saldana RWitkowski O(2022)Emotion-Driven Interactive Storytelling: Let Me Tell You How to FeelArtificial Intelligence in Music, Sound, Art and Design10.1007/978-3-031-03789-4_17(259-274)Online publication date: 15-Apr-2022
https://doi.org/10.1007/978-3-031-03789-4_17
Hadi Mogavi RMa XHui P(2021)Characterizing Student Engagement Moods for Dropout Prediction in Question Pool WebsitesProceedings of the ACM on Human-Computer Interaction10.1145/34490865:CSCW1(1-22)Online publication date: 22-Apr-2021
https://dl.acm.org/doi/10.1145/3449086
Show More Cited By

Index Terms

Inferring User Engagement from Interaction Data
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models
      2. User studies
    2. Interaction paradigms
      1. Web-based interaction
2. Information systems
  1. World Wide Web
    1. Web mining

Recommendations

Using Interaction Data to Predict Engagement with Interactive Media
MM '21: Proceedings of the 29th ACM International Conference on Multimedia

Media is evolving from traditional linear narratives to personalised experiences, where control over information (or how it is presented) is given to individual audience members. Measuring and understanding audience engagement with this media is ...
Interactive TV narratives: Opportunities, progress, and challenges

This article is motivated by the question whether television should do more than simply offer interactive services alongside (and separately from) traditional linear programs, in the context of its dominance being seriously challenged and threatened by ...
Exploring the User Engagement in Interacting with Game Interfaces
IVIC 2013: Third International Visual Informatics Conference on Advances in Visual Informatics - Volume 8237

This research investigates and identifies the factors that influence positive user engagement while interacting with games interfaces. In this paper we provide a finding on our experiences with two case studies; 1) A study on the variation of flow ...

Comments

Information & Contributors

Information

Published In

CHI EA '19: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

May 2019

3673 pages

ISBN:9781450359719

DOI:10.1145/3290607

General Chairs:
Stephen Brewster
University of Glasgow, Scotland, UK
,
Geraldine Fitzpatrick
TU Wien, Austria
,
Program Chairs:
Anna Cox
University College London, UK
,
Vassilis Kostakos
University of Melbourne, Australia

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 May 2019

Check for updates

Author Tags

Qualifiers

Abstract

Funding Sources

EPSRC

Conference

CHI '19

Sponsor:

SIGCHI

CHI '19: CHI Conference on Human Factors in Computing Systems

May 4 - 9, 2019

Glasgow, Scotland Uk

Acceptance Rates

Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

Upcoming Conference

CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
359
Total Downloads

Downloads (Last 12 months)27
Downloads (Last 6 weeks)0

Reflects downloads up to 30 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Hong D(2023)How much is a “feedback” worth? User engagement and interaction for computer-supported adaptive quizzingInteractive Learning Environments10.1080/10494820.2023.2176521(1-16)Online publication date: 1-Mar-2023
https://doi.org/10.1080/10494820.2023.2176521
Rico Garcia OFernandez Fernandez JBecerra Saldana RWitkowski O(2022)Emotion-Driven Interactive Storytelling: Let Me Tell You How to FeelArtificial Intelligence in Music, Sound, Art and Design10.1007/978-3-031-03789-4_17(259-274)Online publication date: 15-Apr-2022
https://doi.org/10.1007/978-3-031-03789-4_17
Hadi Mogavi RMa XHui P(2021)Characterizing Student Engagement Moods for Dropout Prediction in Question Pool WebsitesProceedings of the ACM on Human-Computer Interaction10.1145/34490865:CSCW1(1-22)Online publication date: 22-Apr-2021
https://dl.acm.org/doi/10.1145/3449086
Zhou YCalder BMalthouse EHessary Y(2021)Not all clicks are equal: detecting engagement with digital contentJournal of Media Business Studies10.1080/16522354.2021.1924558(1-18)Online publication date: 9-Jun-2021
https://doi.org/10.1080/16522354.2021.1924558
Brissette-Gendron RLéger PCourtemanche FChen SOuhnana MSénécal S(2020)The Response to Impactful Interactivity on Spectators’ Engagement in a Digital GameMultimodal Technologies and Interaction10.3390/mti40400894:4(89)Online publication date: 4-Dec-2020
https://doi.org/10.3390/mti4040089
Leiva LArapakis I(2020)The Attentive Cursor DatasetFrontiers in Human Neuroscience10.3389/fnhum.2020.56566414Online publication date: 16-Nov-2020
https://doi.org/10.3389/fnhum.2020.565664
Wu ZJiang YLiu YMa XBernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)Predicting and Diagnosing User Engagement with Mobile UI Animation via a Data-Driven ApproachProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376324(1-13)Online publication date: 21-Apr-2020
https://dl.acm.org/doi/10.1145/3313831.3376324

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.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Abstract

References

Cited By

Index Terms

Recommendations

Using Interaction Data to Predict Engagement with Interactive Media

Interactive TV narratives: Opportunities, progress, and challenges

Exploring the User Engagement in Interacting with Game Interfaces

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

HTML Format

Share

Share this Publication link

Share on social media

Affiliations