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From reducing stress and loneliness, to boosting productivity and overallwell-being, pets are believed to play a significant role in people's dailylives. Many traditional studies have identified that frequent interactions withpets could make individuals become healthier and more optimistic, andultimately enjoy a happier life. However, most of those studies are not onlyrestricted in scale, but also may carry biases by using subjectiveself-reports, interviews, and questionnaires as the major approaches. In thispaper, we leverage large-scale data collected from social media and thestate-of-the-art deep learning technologies to study this phenomenon in depthand breadth. Our study includes four major steps: 1) collecting timeline postsfrom around 20,000 Instagram users, 2) using face detection and recognition on2-million photos to infer users' demographics, relationship status, and whetherhaving children, 3) analyzing a user's degree of happiness based on images andcaptions via smiling classification and textual sentiment analysis, 3) applyingtransfer learning techniques to retrain the final layer of the Inception v3model for pet classification, and 4) analyzing the effects of pets on happinessin terms of multiple factors of user demographics. Our main results havedemonstrated the efficacy of our proposed method with many new insights. Webelieve this method is also applicable to other domains as a scalable,efficient, and effective methodology for modeling and analyzing socialbehaviors and psychological well-being. In addition, to facilitate the researchinvolving human faces, we also release our dataset of 700K analyzed faces.

The Effect of Pets on Happiness: A Large-scale Multi-Factor Analysis using Social Multimedia