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The Effect of Pets on Happiness: A Large-Scale Multi-Factor Analysis Using Social Multimedia

Authors:

Jiebo LuoAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 9, Issue 5

Article No.: 60, Pages 1 - 15

https://doi.org/10.1145/3200751

Published: 22 June 2018 Publication History

Abstract

From reducing stress and loneliness, to boosting productivity and overall well-being, pets are believed to play a significant role in people’s daily lives. Many traditional studies have identified that frequent interactions with pets could make individuals become healthier and more optimistic, and ultimately enjoy a happier life. However, most of those studies are not only restricted in scale, but also may carry biases by using subjective self-reports, interviews, and questionnaires as the major approaches. In this article, we leverage large-scale data collected from social media and the state-of-the-art deep learning technologies to study this phenomenon in depth and breadth. Our study includes five major steps: (1) collecting timeline posts from around 20,000 Instagram users; (2) using face detection and recognition on 2 million photos to infer users’ demographics, relationship status, and whether having children, (3) analyzing a user’s degree of happiness based on images and captions via smiling classification and textual sentiment analysis; (4) applying transfer learning techniques to retrain the final layer of the Inception v3 model for pet classification; and (5) analyzing the effects of pets on happiness in terms of multiple factors of user demographics. Our main results have demonstrated the efficacy of our proposed method with many new insights. We believe this method is also applicable to other domains as a scalable, efficient, and effective methodology for modeling and analyzing social behaviors and psychological well-being. In addition, to facilitate the research involving human faces, we also release our dataset of 700K analyzed faces.

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Cited By

Li LWu XKong MLiu JZhang J(2024)Quantitatively Interpreting Residents Happiness Prediction by Considering Factor–Factor InteractionsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.324618111:1(1402-1414)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2023.3246181
Li RGuo YZhang B(2023)Adaptive Kernel Graph Nonnegative Matrix FactorizationInformation10.3390/info1404020814:4(208)Online publication date: 29-Mar-2023
https://doi.org/10.3390/info14040208
Ma ZKang ZLuo GTian LChen WWen Chen CCucchiara RHua XQi GRicci EZhang ZZimmermann R(2020)Towards Clustering-friendly RepresentationsProceedings of the 28th ACM International Conference on Multimedia10.1145/3394171.3413597(3081-3089)Online publication date: 12-Oct-2020
https://dl.acm.org/doi/10.1145/3394171.3413597

Index Terms

The Effect of Pets on Happiness: A Large-Scale Multi-Factor Analysis Using Social Multimedia
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social media

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 9, Issue 5

Research Survey and Regular Papers

September 2018

274 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3210369

Issue’s Table of Contents

Copyright © 2018 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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Association for Computing Machinery

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Publication History

Published: 22 June 2018

Accepted: 01 March 2018

Revised: 01 January 2018

Received: 01 August 2017

Published in TIST Volume 9, Issue 5

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Cited By

Li LWu XKong MLiu JZhang J(2024)Quantitatively Interpreting Residents Happiness Prediction by Considering Factor–Factor InteractionsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.324618111:1(1402-1414)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2023.3246181
Li RGuo YZhang B(2023)Adaptive Kernel Graph Nonnegative Matrix FactorizationInformation10.3390/info1404020814:4(208)Online publication date: 29-Mar-2023
https://doi.org/10.3390/info14040208
Ma ZKang ZLuo GTian LChen WWen Chen CCucchiara RHua XQi GRicci EZhang ZZimmermann R(2020)Towards Clustering-friendly RepresentationsProceedings of the 28th ACM International Conference on Multimedia10.1145/3394171.3413597(3081-3089)Online publication date: 12-Oct-2020
https://dl.acm.org/doi/10.1145/3394171.3413597

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