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The essence of images users post and share at their social media platform is motivated and elucidated by their individual psychological constructs which are designated as personality traits. In this research, we investigate how social media profile pictures differ based on the personality of the users posting them at their social networking sites. In our experiment, we use profile images from Twitter platform whose personality we predicted based on 1.7 million data points. We conducted our analysis on users faces by extracting unique 50 facial features in order to examine the relationship between personality and profile picture. Our results reveal notable variations in profile picture selection between different personality traits. For example, high to openness males have a high surprise emotion in their profile pictures and a low smile value, while high in openness females have a high happiness emotion in their profile pictures and a high smile value. Finally, various machine learning approaches were investigated to test the effectiveness of these facial features in predicting users' psychological traits. Our results show that training personality models on a granularity based on gender gains higher accuracy. To our knowledge, this work is the first attempt of using ensemble learning methods for personality prediction task from users profile picture.
Tareaf et al. (Sun,) studied this question.