DocumentCode :
2774289
Title :
Predicting Personality from Twitter
Author :
Golbeck, Jennifer ; Robles, Cristina ; Edmondson, Michon ; Turner, Karen
Author_Institution :
Univ. of Maryland, College Park, MD, USA
fYear :
2011
fDate :
9-11 Oct. 2011
Firstpage :
149
Lastpage :
156
Abstract :
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 machine learning techniques that allow us to successfully predict personality. We then discuss the implications this has for social media design, interface design, and broader domains.
Keywords :
behavioural sciences computing; social networking (online); user interfaces; Twitter; interface design; job satisfaction; personal details; personality prediction; personality test; professional relationship success; romantic relationship success; social media design; Correlation; Facebook; Media; Pragmatics; Psychology; Twitter; personality; social media;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4577-1931-8
Type :
conf
DOI :
10.1109/PASSAT/SocialCom.2011.33
Filename :
6113107
Link To Document :
بازگشت