The Effect of Pets on Happiness: A Large-scale Multi-Factor Analysis using Social Multimedia
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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 paper, 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 four 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, 3) applying transfer learning techniques to retrain the final layer of the Inception v3 model for pet classification, and 4) 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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