Abstract
Because of its richness and availability, micro-blogging has become an ideal platform for conducting psychological research. In this paper, we proposed to predict active users' personality traits through micro-blogging behaviors. 547 Chinese active users of micro-blogging participated in this study. Their personality traits were measured by the Big Five Inventory, and digital records of micro-blogging behaviors were collected via web crawlers. After extracting 845 micro-blogging behavioral features, we first trained classification models utilizing Support Vector Machine (SVM), differentiating participants with high and low scores on each dimension of the Big Five Inventory. The classification accuracy ranged from 84% to 92%. We also built regression models utilizing PaceRegression methods, predicting participants' scores on each dimension of the Big Five Inventory. The Pearson correlation coefficients between predicted scores and actual scores ranged from 0.48 to 0.54. Results indicated that active users' personality traits could be predicted by micro-blogging behaviors.
Highlights
Personality refers to ‘‘the consistent behavior patterns and interpersonal processes originating within the individual’’ [1]
547 Chinese active users of micro-blogging participated in this study. Their personality traits were measured by the Big Five Inventory, and digital records of micro-blogging behaviors were collected via web crawlers
This study aimed to examine the relationship between personality traits and digital records of micro-blogging behaviors based on over 500 samples, which were derived from a total of 1,953,485 active users
Summary
Personality refers to ‘‘the consistent behavior patterns and interpersonal processes originating within the individual’’ [1]. Previous studies mostly focus on correlations between personality traits and web use behaviors Such correlation-based conclusions could not be used directly to predict web users’ personality traits, and measures of web use behaviors rely on self-report technique. Quercia proposed to predict web users’ personality traits through three features (i.e., following, followers and listed counts) available on profiles of Twitter [25] These studies were limited by small sizes and sampling techniques. This study aimed to examine the relationship between personality traits and digital records of micro-blogging behaviors based on over 500 samples, which were derived from a total of 1,953,485 active users. Differing from previous studies, we only focus on active users, whose digital records of micro-blogging behaviors might be rich enough for further analyses. It was hypothesized that active users’ personality traits could be predicted by their micro-blogging behaviors
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