The Effect of Pets on Happiness: A Data-Driven Approach via Large-Scale Social Media

TL;DR

This paper presents a scalable social media-based method using CNNs and image analysis to study how pet ownership influences happiness, offering a new data-driven perspective beyond traditional surveys.

Contribution

It introduces a novel framework combining social media data collection, CNN-based pet classification, and happiness detection to analyze pet effects on happiness at large scale.

Findings

Effective identification of pet owners and control group from Instagram data

Demonstrated correlation between pet ownership and increased happiness

Proposed methodology applicable to other social media-based psychological studies

Abstract

Psychologists have demonstrated that pets have a positive impact on owners' happiness. For example, lonely people are often advised to have a dog or cat to quell their social isolation. Conventional psychological research methods of analyzing this phenomenon are mostly based on surveys or self-reported questionnaires, which are time-consuming and lack of scalability. Utilizing social media as an alternative and complimentary resource could potentially address both issues and provide different perspectives on this psychological investigation. In this paper, we propose a novel and effective approach that exploits social media to study the effect of pets on owners' happiness. The proposed framework includes three major components: 1) collecting user-level data from Instagram consisting of about 300,000 images from 2905 users; 2) constructing a convolutional neural network (CNN) for pets classification, and combined with timeline information, further identifying pet owners and the control group; 3) measuring the confidence score of happiness by detecting and analyzing selfie images. Furthermore, various factors of demographics are employed to analyze the fine-grained effects of pets on happiness. Our experimental results demonstrate the effectiveness of the proposed approach and we believe that this approach can be applied to other related domains as a large-scale, high-confidence methodology of user activity analysis through social media.