Computational personality recognition in social media

Abstract

A variety of approaches have been recently proposed to automatically infer users' personality from their user generated content in social media. Approaches differ in terms of the machine learning algorithms and the feature sets used, type of utilized footprint, and the social media environment used to collect the data. In this paper, we perform a comparative analysis of state-of-the-art computational personality recognition methods on a varied set of social media ground truth data from Facebook, Twitter and YouTube. We answer three questions: (1) Should personality prediction be treated as a multi-label prediction task (i.e., all personality traits of a given user are predicted at once), or should each trait be identified separately? (2) Which predictive features work well across different online environments? and (3) What is the decay in accuracy when porting models trained in one social media environment to another?

Personality and Behavior in Role-based Online Games

“…In our samples, males show lower agreeableness than females; players in region EUW1 (i.e., European West) show lower agreeableness than players from other regions. Results also show that agreeableness is negatively correlated with utility in LoL, which is different from the case in a real-life scenario [5]. Additionally, since people with high agreeableness are expected to cooperate well with teammates, it is reasonable to find that they are characterized by a strong killing behavior and fewer deaths, which is related to good cooperation with teammates and a key component to achieve a high winning rate.…”

Preface to the special issue on personality in personalized systems

“…The papers in this special issue (Farnadi et al 2016;Lepri et al 2016), as well as in related literature (Kosinski et al 2013), show that digital traces of user behavior are a good source for automatic personality detection. There is still room for improvement as there are other modalities that have not shown their full potential yet, such as image-based personality recognition (see (Segalin et al 2016;Celli et al 2014) as early work on the subject), voice-based personality recognition (Mohammadi and Vinciarelli 2015) and fusing various modalities (Skowron et al 2016).…”

“…This special issue contains articles that describe the state of the art in (i) automatic and unobtrusive personality acquisition from social media (Farnadi et al 2016) and mobile phone data (Lepri et al 2016) (ii) personality-based user models in behavioral change applications (Lepri et al 2016), intelligent tutoring systems (Harley et al 2016) and games (Cowley and Charles 2016), and (iii) usage of personality in recommender systems (Fernández-Tobías et al 2016).…”

“…The paper Computational Personality Recognition in Social Media by Farnadi et al (2016) deals with the automatic inference of personality traits from users' social media traces. As such, it lies at the base of the chain of personality usage in personalized systems.…”

VirtualIdentity: Privacy preserving user profiling

“…without any attempts for privacy preservation. In this paper we use SVMs, which are known as state-of-the-art classification techniques for detecting age, gender and personality traits from text and images [35], [24], [9], [25], [26].…”