  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.

Social and Information Networks