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Technol."],"published-print":{"date-parts":[[2018,9,30]]},"abstract":"<jats:p>From reducing stress and loneliness, to boosting productivity and overall well-being, pets are believed to play a significant role in people\u2019s 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\u2019 demographics, relationship status, and whether having children, (3) analyzing a user\u2019s 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.<\/jats:p>","DOI":"10.1145\/3200751","type":"journal-article","created":{"date-parts":[[2018,6,22]],"date-time":"2018-06-22T12:07:56Z","timestamp":1529669276000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["The Effect of Pets on Happiness"],"prefix":"10.1145","volume":"9","author":[{"given":"Xuefeng","family":"Peng","sequence":"first","affiliation":[{"name":"University of Rochester"}]},{"given":"Li-Kai","family":"Chi","sequence":"additional","affiliation":[{"name":"University of Rochester"}]},{"given":"Jiebo","family":"Luo","sequence":"additional","affiliation":[{"name":"University of Rochester"}]}],"member":"320","published-online":{"date-parts":[[2018,6,22]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Health correlates of compatibility and attachment in human-companion animal relationships","author":"Budge R. 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Gottschalk. 1997. The unobtrusive measurement of psychological states and traits. Text Analysis for the Social Sciences: Methods for Drawing Statistical Inferences from Texts and Transcripts, C. W. Roberts (Ed.). Lawrence Erlbaum Associates Mahwah, NJ, 117--129."},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Y. Hochberg and A. C. Tamhane. 1987. Multiple comparison procedures. Multiple Comparison Procedures. Sage 28--46.   Y. Hochberg and A. C. Tamhane. 1987. Multiple comparison procedures. Multiple Comparison Procedures. Sage 28--46.","DOI":"10.1002\/9780470316672"},{"volume-title":"8th International Conference on Weblogs and Social Media (ICWSM\u201914)","author":"Hutto C. J.","key":"e_1_2_1_8_1","unstructured":"C. J. Hutto and E. E. Gilbert . 2014. VADER: A parsimonious rule-based model for sentiment analysis of social media text . In 8th International Conference on Weblogs and Social Media (ICWSM\u201914) . C. J. Hutto and E. E. Gilbert. 2014. 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