What your Facebook Profile Picture Reveals about your Personality

People spend considerable effort managing the impressions they give others. Social psychologists have shown that people manage these impressions differently depending upon their personality. Facebook and other social media provide a new forum for this fundamental process; hence, understanding people's behaviour on social media could provide interesting insights on their personality. In this paper we investigate automatic personality recognition from Facebook profile pictures. We analyze the effectiveness of four families of visual features and we discuss some human interpretable patterns that explain the personality traits of the individuals. For example, extroverts and agreeable individuals tend to have warm colored pictures and to exhibit many faces in their portraits, mirroring their inclination to socialize; while neurotic ones have a prevalence of pictures of indoor places. Then, we propose a classification approach to automatically recognize personality traits from these visual features. Finally, we compare the performance of our classification approach to the one obtained by human raters and we show that computer-based classifications are significantly more accurate than averaged human-based classifications for Extraversion and Neuroticism

What Twitter Profile and Posted Images Reveal About Depression and Anxiety

Previous work has found strong links between the choice of social media images and users' emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety labels, and extracted interpretable features of posted and profile pictures to uncover the associations with users' depression and anxiety, controlling for demographics. For depression, we find that profile pictures suppress positive emotions rather than display more negative emotions, likely because of social media self-presentation biases. They also tend to show the single face of the user (rather than show her in groups of friends), marking increased focus on the self, emblematic for depression. Posted images are dominated by grayscale and low aesthetic cohesion across a variety of image features. Profile images of anxious users are similarly marked by grayscale and low aesthetic cohesion, but less so than those of depressed users. Finally, we show that image features can be used to predict depression and anxiety, and that multitask learning that includes a joint modeling of demographics improves prediction performance. Overall, we find that the image attributes that mark depression and anxiety offer a rich lens into these conditions largely congruent with the psychological literature, and that images on Twitter allow inferences about the mental health status of users.Comment: ICWSM 201

First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

Recent Trends in Deep Learning Based Personality Detection

Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an 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. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection

Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media

A person's weight status can have profound implications on their life, ranging from mental health, to longevity, to financial income. At the societal level, "fat shaming" and other forms of "sizeism" are a growing concern, while increasing obesity rates are linked to ever raising healthcare costs. For these reasons, researchers from a variety of backgrounds are interested in studying obesity from all angles. To obtain data, traditionally, a person would have to accurately self-report their body-mass index (BMI) or would have to see a doctor to have it measured. In this paper, we show how computer vision can be used to infer a person's BMI from social media images. We hope that our tool, which we release, helps to advance the study of social aspects related to body weight.Comment: This is a preprint of a short paper accepted at ICWSM'17. Please cite that version instea

Gender, Personality, and Self Esteem as Predictors of Social Media Presentation

In an age when people make and maintain relationships in online environments, creating and sustaining impressions online becomes equally important. For a better understanding of social networking sites usage, the present study examines the influence of gender, personality, and selfesteem on social media presentation. The goal of this study is to investigate individuals‘ expressions through social media in order to determine if they reflect the author‘s gender, personality, and levels of self-esteem. Quantitative analysis was used in this study through survey and SPSS content analysis. The researcher found that neuroticism was related to selfpresentation, and agreeableness is related to Facebook friends. Personality traits were generally shown to be a stronger predictor of self-presentation on social media than gender or self-esteem, because the big five personality traits correspond well with functions of social media

#Clickbait: Social Media, Attraction, and Relational Development

In the age of social media networking and online dating, interpersonal communication has evolved. Today’s young adults have grown up during the evolution of social media. Social Penetration Theory (SPT) proposes that interpersonal relationships develop through self-disclosure (Punyanunt-Carter, 2019). As we move from public to more private information in the process of self-disclosure, we develop deeper and closer interpersonal relationships. The purpose of this study is to analyze attraction, perceived authenticity, and relational development on social media through the SPT lens. I argue that Twitter is a popular social media platform that encourages user authenticity and that social media users interpret relational closeness and form impressions of other social media users’ identities by analyzing disclosures. Twenty-one undergraduate college students participated in surveys and focus group interviews for the study. I surveyed participants about the perceived authenticity of social media users on Twitter, Instagram, and general social media platforms, as well as what attributes and qualities they examine while observing others’ social media profiles. Participants rated Twitter higher than Instagram in depicting social media users’ true, authentic selves. Results indicated that participants commonly observe the social media profiles of others to determine similar interests, beliefs, values, appearance, and social circles. Furthermore, I created a Twitter profile and asked participants to observe the profile and attempt to apply the steps of the SPT. All participants analyzed disclosures from the Twitter profile to form impressions of the profile user’s personality traits, values, and personal beliefs. By applying the SPT to social media, I explicate the common factors that influence attraction on social media and conclude that social media users analyze disclosures to form impressions and evaluate relational prototypes of others via social media