# Encoding Text Information with Graph Convolutional Networks for Personality Recognition
## 1 Introduction  
Personality is the combination of a person's emotion, behavior, and motivation and the characteristics of a person's thought patterns. The ability to detect one's personality traits automatically has important research significance and value [1]. Our personality has a major impact on our lives, affecting our life choices, health, and well-being, along with our desires and preferences [2]. In marketing, it can be useful in recommending books, music, etc., to users based on their personality. In the field of psychology, it may be applied to social network data to understand user behavior. In recruiting, it can also help job recruiters select appropriate employees [3; 4; 5]. Additionally, personality, as a stable set of psychological characteristics, has been widely used in the field of security research to prevent social engineering attacks. In the field of security, personality is used to discover the principles of how attackers recognize and use personality. Blocking or interfering with personality recognition is an important prerequisite for defending against social engineering attacks. Personality theory is divided into six schools of psychoanalysis, traits, biology, humanism, behaviorism, and cognition [6; 7]. The most commonly used personality model is the Big Five personality traits model, which describes personality in five aspects: extroversion, neuroticism, agreeableness, conscientiousness, and openness [8], as detailed in Table 1. The Big Five personality model is a well-experimented and well-scrutinized standard of personality structure used by researchers in recent years [9; 10]. Therefore, the majority of the studies use the Big Five personality measure for classification. Personality is a stable psychological characteristic. The user's behavior state will vary with the time and environment, but the user's personality is stable. According to the different behaviors of users, we extract feature information to identify the user's personality. Traditionally, personality recognition depends on the user's profile information, status updates, texts, and so on [11; 12; 13; 14]. Most of this information comes from textual expression. Analyzing the text data sent by the user is the most effective and accurate way to obtain the user's personality. Therefore, the analysis of user text data allows us to determine important personality traits. In recent years, there have been a number of studies that have applied machine learning and convolutional neural networks to personality recognition. Personality recognition from social media [15], especially Facebook sentiment analysis, has been widely welcomed. However, most studies on personality recognition are focused on feature extraction based on data collection and preprocessing [16; 17; 18]. Hence, research can focus on creating better models and architectures, not just on data acquisition and preprocessing. The classification algorithms of traditional machine learning and multilayer neural networks are mostly used to determine personality categories. However, the accuracy and F1-score of automatically classifying personality traits by analyzing user social information are currently low. The most important consideration is that although most researchers consider these five personality traits to be independent of each other, several studies [19; 20] claim that there is a certain correlation between these traits, and it is inaccurate to build five completely independent classifiers. Most studies ignore the important question of the correlation between the five personality traits. Consequently, for the above problems, this paper uses graph convolutional networks (GCN) to capture users' text information. It is a recently proposed neural architecture for representing relational information. We use the feature information sharing method to add correlations between the five personality traits in the model.