Psycho-demographic Analysis of the Facebook Rainbow Campaign

Yilun Wang, Himabindu Lakkaraju, Michal Kosinski and Jure Leskovec
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Yilun Wang: Stanford University
Himabindu Lakkaraju: Stanford University
Michal Kosinski: Stanford University
Jure Leskovec: Stanford University

Research Papers from Stanford University, Graduate School of Business

Abstract: Over the past decade, online social media has had a tremendous impact on the way people engage in social activism. For instance, about 26M Facebook users expressed their support in upholding the cause of marriage equality by overlaying their profile pictures with rainbow-colored filters. Similarly, hundreds of thousands of users changed their profile pictures to a black dot condemning incidents of sexual violence in India. This act of demonstrating support for social causes by changing online profile pictures is being referred to as pictivism. In this paper, we analyze the psycho-demographic profiles, social networking behavior, and personal interests of users who participated in the Facebook Rainbow campaign. Our study is based on a sample of about 800K detailed profiles of Facebook users combining questionnaire-based psychological scores with Facebook profile data. Our analysis provides detailed insights into psycho-demographic profiles of the campaign participants. We found that personality traits such as openness and neuroticism are both positively associated with the likelihood of supporting the campaign, while conscientiousness exhibited a negative correlation. We also observed that females, religious disbelievers, democrats and adults in the age group of 20 to 30 years are more likely to be a part of the campaign. Our research further confirms the findings of several previous studies which suggest that a user is more likely to participate in an online campaign if a large fraction of his/her friends are already doing so. We also developed machine learning models for predicting campaign participation. Users' personal interests, approximated by Facebook user like activity, turned out to be the best indicator of campaign participation. Our results demonstrated that a predictive model which leverages the aforementioned features accurately identifies campaign participants (AUC=0.76).

Date: 2016-10
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