How are you doing? : emotions and personality in Facebook

User generated content on social media sites is a rich source of information about latent variables of their users. Proper mining of this content provides a shortcut to emotion and personality detection of users without filling out questionnaires. This in turn increases the application potential of personalized services that rely on the knowledge of such latent variables. In this paper we contribute to this emerging domain by studying the relation between emotions expressed in approximately 1 million Facebook (FB) status updates and the users' age, gender and personality. Additionally, we investigate the relations between emotion expression and the time when the status updates were posted. In particular, we find that female users are more emotional in their status posts than male users. In addition, we find a relation between age and sharing of emotions. Older FB users share their feelings more often than young users. In terms of seasons, people post about emotions less frequently in summer. On the other hand, December is a time when people are more likely to share their positive feelings with their friends. We also examine the relation between users' personality and their posts. We find that users who have an open personality express their emotions more frequently, while neurotic users are more reserved to share their feelings

Computational personality recognition in social media

A variety of approaches have been recently proposed to automatically infer users' personality from their user generated content in social media. Approaches differ in terms of the machine learning algorithms and the feature sets used, type of utilized footprint, and the social media environment used to collect the data. In this paper, we perform a comparative analysis of state-of-the-art computational personality recognition methods on a varied set of social media ground truth data from Facebook, Twitter and YouTube. We answer three questions: (1) Should personality prediction be treated as a multi-label prediction task (i.e., all personality traits of a given user are predicted at once), or should each trait be identified separately? (2) Which predictive features work well across different on-line environments? (3) What is the decay in accuracy when porting models trained in one social media environment to another

Inferring Big 5 Personality from Online Social Networks

Thesis (Master's)--University of Washington, 2014Online social networks are very popular with millions of people creating online profiles and sharing personal information including their interests, activities, likes/dislikes and thoughts with their friends and family. This rich user generated content from social media makes them an ideal platform to study human behavior. In our research, we are interested in latent variables such as the long term personality traits and the short term emotional state of users. Proper mining of the user generated content can be used to identify personality traits of users without having them fill out questionnaires. These traits are shown to strongly influence a person's decisions, behavior and preferences for language, music, books etc. We explore the use of different machine learning techniques and feature selection methodologies for inferring users' personality traits using information available from their online profile. We study five multivariate regression algorithms and contrast them with a single target approach for predicting the scores. Additionally, we explore feature subset selection using correlation based heuristics and evaluate the quality of the feature space produced using two different machine learning algorithms: Linear Regression and Support Vector Regressors. The performance of the above techniques is evaluated on two different datasets: a myPersonality dataset collected from Facebook and a YouTube personality dataset collected from video posts of vloggers. All five multivariate as well as single target algorithms and correlation based feature selection methods outperformed the average baseline model for all five personality traits on both the datasets. Furthermore, we study the relation between emotions expressed in approximately 1 million Facebook (FB) status updates and the users' personality, age, gender and time of posting. We use this in establishing associations such as open personality users express emotions more frequently, while neurotic users are more reserved. With the ability to identify users' personality and emotions, advertisements could be tailored based on the user's personality type since personality and/or emotion-aware interfaces are more persuasive

A multivariate regression approach to personality impression recognition of vloggers

Research in psychology has suggested that behavior of individuals can be explained to a great extent by their underlying personality traits. In this paper, we focus on predicting how the personality of YouTube video bloggers is perceived by their viewers. Our approach to personality recognition is multimodal in the sense that we use audio-video features, as well as textual (emotional and linguistic) features extracted from the transcripts of vlogs. Based on these features, we predict the extent to which the video blogger is perceived to exhibit each of the traits of the Big Five personality model. In addition, we explore 5 multivariate regression techniques and contrast them with a single target approach for predicting personality impression scores. All 6 algorithms are able to outperform the average baseline model for all 5 personality traits on a dataset of 404 YouTube videos. This is interesting because previously published methods for the same dataset show an improvement over the baseline for the majority of personality traits, but not for all simultaneously

How are you doing? Emotions and personality in Facebook

User generated content on social media sites is a rich source of information about latent variables of their users. Proper mining of this content provides a shortcut to emotion and personality detection of users without filling out questionnaires. This in turn increases the application potential of personalized services that rely on the knowledge of such latent variables. In this paper we contribute to this emerging domain by studying the relation between emotions expressed in approximately 1 million Facebook (FB) status updates and the users' age, gender and personality. Additionally, we investigate the relations between emotion expression and the time when the status updates were posted. In particular, we find that female users are more emotional in their status posts than male users. In addition, we find a relation between age and sharing of emotions. Older FB users share their feelings more often than young users. In terms of seasons, people post about emotions less frequently in summer. On the other hand, December is a time when people are more likely to share their positive feelings with their friends. We also examine the relation between users' personality and their posts. We find that users who have an open personality express their emotions more frequently, while neurotic users are more reserved to share their feelings.status: publishe

Computational personality recognition in social media

A variety of approaches have been recently proposed to automatically infer users’ personality from their user generated content in social media. Approaches differ in terms of the machine learning algorithms and the feature sets used, type of utilized footprint, and the social media environment used to collect the data. In this paper, we perform a comparative analysis of state-of-the-art computational personality recognition methods on a varied set of social media ground truth data from Facebook, Twitter and YouTube.We answer three questions: (1) Should personality prediction be treated as a multi-label prediction task (i.e., all personality traits of a given user are predicted at once), or should each trait be identified separately? (2) Which predictive features work well across different on-line environments? and (3) What is the decay in accuracy when porting models trained in one social media environment to another?status: publishe