Predicting Personality from Micro-Blogs using Supervised Machine Learning Models - Chandra Shaker Reddy, P; Sucharitha, Yadala; Surya Narayana, G (International Journal of Computer Sciences and Engineering, 2021)
Social media is a place where users present themselves to the world, revealing personal details and insights into their lives. We are beginning to understand how some of this information can be utilized to improve the users‟ experiences with interfaces and with one another. In this paper, we are interested in the personality of users. Personality has been shown to be relevant to many types of interactions; it has been shown to be useful in predicting job satisfaction, professional and romantic relationship success, and even preference for different interfaces. Until now, to accurately gauge users‟ personalities, they needed to take a personality test. This made it impractical to use personality analysis in many social media domains. In this paper, we present a method by which a user‟s personality can be accurately predicted through the publicly available information on their Twitter profile. We will describe the type of data collected, our methods of analysis, and the results of predicting personality traits through machine learning. We then discuss the implications this has for social media design, interface design, and broader domains.