Workshop on Computational Personality Recognition: Shared Task

Gaining insight in a web user's personality is very valuable for applications that rely on personalisation, such as recommender systems and personalised advertising. In this paper we explore the use of machine learning techniques for inferring a user's personality traits from their Facebook status updates. Even with a small set of training examples we can outperform the majority class baseline algorithm. Furthermore, the results are improved by adding training examples from another source. This is an interesting result because it indicates that personality trait recognition generalises across social media platforms.

Here we present a method for detecting an individual's level of conscientiousness based on an analysis of the content of their Facebook status updates. Our model is based on the identification of semantic evidence of facets related to conscientiousness; an individual's belief of their control over events around them and their goal orientation. The model achieves a correlation of r=.27 on a subset of the Facebook data published for the myPersonality workshop, with an accuracy of 58.13% for detecting if an individual is above or below the median and 68.03% for those outside of one standard deviation. While we take a narrow approach and identify only one personality trait, the general methodology of directly looking for evidence of traits in an individual's utterances is applicable to discovering models for all of the personality traits.

For the natural and social interaction it is necessary to understand human behavior. Personality is one of the fundamental aspects, by which we can understand behavioral dispositions. It is evident that there is a strong correlation between users’ personality and the way they behave on online social network (e.g., Facebook). This paper presents automatic recognition of Big-5 personality traits on social network (Facebook) using users’ status text. For the automatic recognition we studied different classification methods such as SMO (Sequential Minimal Optimization for Support Vector Machine), Bayesian Logistic Regression (BLR) and Multinomial Naïve Bayes (MNB) sparse modeling. Performance of the systems had been measured using macro-averaged precision, recall and F1; weighted average accuracy (WA) and un-weighted average accuracy (UA). Our comparative study shows that MNB performs better than BLR and SMO for personality traits recognition on the social network data.