# Personality Trait Analysis in Social Networks Based on Weakly Supervised Learning of Shared Images
## 1 Introduction  
Social media, as a major platform for communication and information exchange, constitutes a rich repository of the opinions and sentiments of 3.6 billion users regarding a vast spectrum of topics. In addition, image sharing on social networks has increased exponentially in the past years; officiallyin 2020, Instagram reached 500 million daily active users (1 billion monthly active users) uploading more than 100 million photos and videos per day. However, current machine learning tools for trends discovery and post analysis are still based mainly on texts, likes behaviour and browsing logs [1; 2; 3; 4], though the automatic analysis of such a vast amount of images and videos is becoming a strong feature thanks to the recent advances in deep learning, specifically with the use of Convolutional Neural Networks (CNNs) for image analysis [5]. Such machine learning frameworks have been also applied to psychological assessment [6], first by finding correlations between text and personality [7; 8], and later by demonstrating that some image features are related to the personality of users in social networks [9]. A recent overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches vcan be found in [10]. The main hypothesis of this work is that the relationship between text and sentiments conveyed based on personality, as observed by previous researchers like [11], translates well into a relationship between images and personality when we consider the images as being conditioned on specific word use, without any personality annotation. [PERSON] in [11] proved that there exist words that correlate with different personality traits based on statistical evidence (see Table 1 in which 22 words per trait were randomly selected from those identified by [PERSON] [11]). For example, a neurotic personality trait correlates positively with negative emotion words such as 'awful' or 'terrible', whereas an extroverted personality trait correlates positively with words reflecting social settings or experiences like 'bar', 'drinking' and 'crowd'. Considering this relationship between text and personality, and the fact that posted images have a relationship with their accompanying text, we propose a methodology which, taking advantage of such existing text-personality correlations, exploits the relationship between texts and images in social networks to determine those images most correlated with sentiments conveyed according to personality traits. The final aim was to train a weakly-supervised image-based model for personality and sentiment assessment, that can be used even when textual data is not available. We considered the human personality characterization called the _Big Five_ model of personality for this research [12; 13; 14]. The Big Five model is a well-researched personality model, which has been shown to be consistent across age, gender, language and culture [15; 16]. In essence, this model distinguishes five main different human personality dimensions: Openness to experience (O), Conscientiousness (C), Extraversion (E), Agreeableness (A) and Neuroticism (N); hence, it is often referred to as _OCEAN_. These five personality traits have been already related to text [11] and images [17] uploaded by users. Therefore, personality might be an important factor in the underlying distribution of the users' public posts on social media, and thus, it is possible to infer some degree of personality knowledge from such data. However, previous literature on personality assessment from image data has relied on filling personality questionnaires [8; 9; 11; 17], thus slowing down research since it takes a long time to collect data in this way.