A Novel Adaptive Conditional Probability-Based Predicting Model for User’s Personality Traits

Abstract

With the pervasive increase in social media use, the explosion of users’ generated data provides a potentially very rich source of information, which plays an important role in helping online researchers understand user’s behaviors deeply. Since user’s personality traits are the driving force of user’s behaviors, hence, in this paper, along with social network features, we first extract linguistic features, emotional statistical features, and topic features from user’s Facebook status updates, followed by quantifying importance of features via Kendall correlation coefficient. And then, on the basis of weighted features and dynamic updated thresholds of personality traits, we deploy a novel adaptive conditional probability-based predicting model which considers prior knowledge of correlations between user’s personality traits to predict user’s Big Five personality traits. In the experimental work, we explore the existence of correlations between user’s personality traits which provides a better theoretical support for our proposed method. Moreover, on the same Facebook dataset, compared to other methods, our method can achieve anF1-measure of 80.6% when taking into account correlations between user’s personality traits, and there is an impressive improvement of 5.8% over other approaches.

What Makes Consumers’ Intention to Purchase Paid Stickers in Personal Messenger? The Role of Personality and Motivational Factors

“…However, this cannot be forced, so data will only be obtained from those willing to participate in the survey. Many studies predict personality through the way people write mails, use apps, and perform social media activities ( Shen et al, 2013 ; Wang et al, 2015 ; Tandera et al, 2017 ; Peltonen et al, 2020 ). Through the data collected from users who have agreed to provide information, service providers can predict their personalities and recommend suitable stickers.…”