research-article

Inferring Employee Engagement from Social Media

Authors:

N. Sadat Shami,

Michael Muller,

Mikhil Masli, and

Werner GeyerAuthors Info & Claims

CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

April 2015

Pages 3999 - 4008

https://doi.org/10.1145/2702123.2702445

Published: 18 April 2015 Publication History

Abstract

Employees increasingly are expressing ideas and feelings through enterprise social media. Recent work in CHI and CSCW has applied linguistic analysis towards understanding employee experiences. In this paper, we apply dictionary based linguistic analysis to measure 'Employee Engagement'. Employee engagement is a measure of employee willingness to apply discretionary effort towards organizational goals, and plays an important role in organizational outcomes such as financial or operational results. Organizations typically use surveys to measure engagement. This paper describes an approach to model employee engagement based on word choice in social media. This method can potentially complement surveys, thus providing more real-time insights into engagement and allowing organizations to address engagement issues faster. Our results predicting engagement scores on a survey by combining demographics with social media text demonstrate that social media text has significant predictive power compared to demographic data alone. We also find that engagement may be a state than a stable trait since social media posts closer to the administration of the survey had the most predictive power. We further identify the minimum number of social media posts required per employee for the best prediction.

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Cited By

Das Swain VGao LMondal AAbowd GDe Choudhury M(2024)Sensible and Sensitive AI for Worker Wellbeing: Factors that Inform Adoption and Resistance for Information WorkersProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642716(1-30)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642716
García‐Navarro CPulido‐Martos MPérez‐Lozano C(2024)The study of engagement at work from the artificial intelligence perspective: A systematic reviewExpert Systems10.1111/exsy.13673Online publication date: 16-Jul-2024
https://doi.org/10.1111/exsy.13673
Kawakami AChowdhary SIqbal SLiao QOlteanu ASuh JSaha K(2023)Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational StakeholdersProceedings of the ACM on Human-Computer Interaction10.1145/36102077:CSCW2(1-33)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3610207
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Index Terms

Inferring Employee Engagement from Social Media
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Computer supported cooperative work
2. Social and professional topics
  1. Professional topics
    1. Computing and business
      1. Computer supported cooperative work

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cover image ACM Conferences

CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

April 2015

4290 pages

ISBN:9781450331456

DOI:10.1145/2702123

General Chairs:
Bo Begole
Huawei, USA
,
Jinwoo Kim
Yonsei University, Korea
,
Program Chairs:
Kori Inkpen
Microsoft Research, USA
,
Woontack Woo
KAIST, Korea

Copyright © 2015 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]

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Published: 18 April 2015

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CHI '15: CHI Conference on Human Factors in Computing Systems

April 18 - 23, 2015

Seoul, Republic of Korea

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CHI '15 Paper Acceptance Rate 486 of 2,120 submissions, 23%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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https://dl.acm.org/doi/10.1145/3613904.3642716
García‐Navarro CPulido‐Martos MPérez‐Lozano C(2024)The study of engagement at work from the artificial intelligence perspective: A systematic reviewExpert Systems10.1111/exsy.13673Online publication date: 16-Jul-2024
https://doi.org/10.1111/exsy.13673
Kawakami AChowdhary SIqbal SLiao QOlteanu ASuh JSaha K(2023)Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational StakeholdersProceedings of the ACM on Human-Computer Interaction10.1145/36102077:CSCW2(1-33)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3610207
Saha KIqbal S(2023)Focus Time: Effectiveness of Computer Assisted Protected Time for Wellbeing and Work Engagement of Information WorkersProceedings of the 2nd Annual Meeting of the Symposium on Human-Computer Interaction for Work10.1145/3596671.3598571(1-13)Online publication date: 13-Jun-2023
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Mittal PJora RSodhi KSaxena P(2023)A Review of The Role of Artificial Intelligence in Employee Engagement2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS)10.1109/ICACCS57279.2023.10112957(2502-2506)Online publication date: 17-Mar-2023
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Singh VSingh SDash SGupta A(2022)Estimation of Employee Engagement in Organisations during Crisis using Machine Learning Technique2022 3rd International Conference on Computation, Automation and Knowledge Management (ICCAKM)10.1109/ICCAKM54721.2022.9990181(1-6)Online publication date: 15-Nov-2022
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