Studying Personality through the Content of Posted and Liked Images on Twitter

Citation:
Guntuku, Sharath Chandra, Weisi Lin, Jordan Carpenter, Wee Keong Ng, Lyle Ungar, and Daniel Preotiuc-Pietro. Studying Personality through the Content of Posted and Liked Images on Twitter In Web Science., 2017.

Abstract:

Interacting with images through social media has become widespread due to ubiquitous Internet access and multimedia enabled devices. Through images, users generally present their daily activities, preferences or interests. This study aims to identify the way and extent to which personality differences measured as using the Big Five model are related to online image posting and liking. In two experiments, the larger consisting of ~$1.5 million Twitter images both posted and liked by ~4,000 users, we extract interpretable semantic concepts using large-scale image content analysis and analyze differences specific of each personality trait. Predictive results show that image content can predict personality traits, and that there can be significant performance gain by fusing the signal from both posted and liked images.

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