# Personality Trait Analysis in Social Networks Based on Weakly Supervised Learning of Shared Images
## 2 Related Work
### Image-Based Personality Inference  
An early attempt to model personality from images was presented by [PERSON] et al. [19], where they showed that users better agreed with the targets' self-reported personalities when the profile picture was an outdoor picture of a smiling human face. [PERSON] et al. [9] proved that there were visual patterns that correlated with the personality traits of 300 Flickr users and, thus, that the personality traits of those users could be inferred from the images they tagged as favorites. To do so, they used aesthetic (colors, edges, entropy, etc) and content features (objects, faces). [PERSON] et al. [20] improved the low level features used in a previous work by changing the usual Features-to-Personality (F2P) approach to a two step approach: Features-to-Answers (F2A) + Answers-to-Personality (A2P). Instead of building a model that directly maps features extracted from an image to a personality, with this approach the features are first mapped to the answers of the questionnaire BFI-10 for personality assessment [21]. Then, the answers are mapped to a personality. Besides this two-step approach, they also added new semantic features to extract from the images, like _Black & White vs. Color image_, _Gender identification_ and _Scene recognition_. [PERSON] et al. [22] proposed a new set of features that better encode the information of the image used to infer the personality of the user who favourited it. They proposed to describe each image using 82 different features, divided into four major categories: _Color_, _Composition_, _Textural Properties_ and _Faces_.Their method proved to be suitable to map an image to a personality trait, but it worked better for attributed personality traits rather than self-assessed personality. Following the trend of finding personality-associated features, [PERSON] et al. [23] showed that it is possible to predict the personality of Instagram users by analyzing the color features of their uploaded images. In the same vein, [PERSON] et al. [24] showed that certain image features, such as color or semantics, or indoors/outdoors, play an important role in determining the personality of Facebook users based on their profile images. Since a Convolutional Neural Network won the Imagenet competition in 2012 [5], the computer vision field has moved from designing the hand-made image features to learning them in an end-to-end deep learning model. Likewise, the feasibility of using deep learning to automatically learn features that are good to estimate personality traits from pictures has been already demonstrated by the same work by [PERSON] et al. [17]. In their work, they presented the dataset _PsychoFlickr_, which consists of a collection of images favourited by 300 users from the site _Flickr.com_, with each user tagging 200 images as favorites, adding up to a total of 60,000 images. Additionally, the Big Five traits personality profiles of each user were provided. There were two different versions of the personality profile for each user; one collected through a self-assessment questionnaire answered by the user, and one attributed by a group of 12 assessors who had evaluated the image set of the user. Subsequently, the authors fine-tuned a CNN pre-trained on the large dataset of object classifications known as Imagenet [25], to capture the aesthetic attributes of the images in order to be able to estimate the personality traits associated with those images. For each of the Big Five traits they trained a CNN model with a binary classifier. Then, each different CNN estimate of the images was defined as \"high\" or \"low\" for the trait the model was trained for. The study of the personality conveyed by images has not only been used to infer the personality of users, but also to analyze how brands express and shape their identity through social networks. [PERSON] [26] analyzed the pictures posted to Instagram to interpret the identity of each brand along five dimensions of personality: sincerity, excitement, competence, sophistication, and ruggedness.