KnowMe and ShareMe: Understanding Automatically Discovered Personality Traits from Social Media andUser Sharing Preferences

There is much recent work on using the digital footprints left by people on social media to predict personal traits and gain a deeper understanding of individuals. Due to the veracity of social media, imperfections in prediction algorithms, and the sensitive nature of one’s personal traits, much research is still needed to better understand the effectiveness of this line of work, including users’ preferences of sharing their computationally derived traits. In this paper, we report a two-part study involving 256 participants, which (1) examines the effectiveness of automatically deriving three types of personality traits from Twitter, including Big 5 personality, basic human values, and fundamental needs, and (2) investigates users’ opinions of using and sharing these traits. Our findings show that for over 80.8% of participants, all three types of traits derived from Twitter are significantly correlated with the participants’ psycho-metric test scores. The results also indicate over 61.5% users are willing to share their derived traits in workplace and that a number of factors significantly influence their sharing preferences. Since our findings demonstrate the feasibility and effectiveness of automatically inferring a user’s personal traits from social media, we discuss their implications for designing a new generation of privacy-preserving, hyper-personalized systems.

By: Liang Gou, Michelle X. Zhou, Huahai Yang

Published in: RJ10516 in 2013


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