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.
In the last years, the interest in automatic personality recognition has grown (see Vinciarelli and Mohammadi for a comprehensive survey @cite_0 ) and several works have started exploiting the wealth of data made available by social media @cite_41 @cite_22 @cite_44 @cite_12 , microphones and cameras @cite_39 @cite_59 @cite_43 @cite_33 , and mobile phones @cite_8 @cite_1 . Two works addressed the automatic recognition of personality traits from self-presentation videos @cite_13 @cite_37 . Biel @cite_13 used a dataset of 442 vlogs and asked external observers to rate vlogger's personality types; instead Batrinca @cite_55 recorded video self-presentations of 89 subjects in a lab setting, asking them to complete a self-assessed personality test.
@cite_37 @cite_22 @cite_33 @cite_8 @cite_41 @cite_55 @cite_1 @cite_39 @cite_0 @cite_44 @cite_43 @cite_59 @cite_13 @cite_12
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In this work, we investigate the relationships between social network structure and personality; we assess the performances of different subsets of structural network features, and in particular those concerned with ego-networks, in predicting the Big-5 personality traits. In addition to traditional survey-based data, this work focuses on social networks derived from real-life data gathered through smartphones. Besides showing that the latter are superior to the former for the task at hand, our results provide a fine-grained analysis of the contribution the various feature sets are able to provide to personality classification, along with an assessment of the relative merits of the various networks exploited. Psychological personality has been shown to affect a variety of aspects: preferences for interaction styles in the digital world and for music genres, for example. Consequently, the design of personalized user interfaces and music recommender systems might benefit from understanding the relationship between personality and use of social media. Since there has not been a study between personality and use of Twitter at large, we set out to analyze the relationship between personality and different types of Twitter users, including popular users and influentials. For 335 users, we gather personality data, analyze it, and find that both popular users and influentials are extroverts and emotionally stable (low in the trait of Neuroticism). Interestingly, we also find that popular users are imaginative' (high in Openness), while influentials tend to be organized' (high in Conscientiousness). We then show a way of accurately predicting a user's personality simply based on three counts publicly available on profiles: following, followers, and listed counts. Knowing these three quantities about an active user, one can predict the user's five personality traits with a root-mean-squared error below 0.88 on a @math scale. Based on these promising results, we argue that being able to predict user personality goes well beyond our initial goal of informing the design of new personalized applications as it, for example, expands current studies on privacy in social media. Personality plays an important role in the way people manage the images they convey in self-presentations and employment interviews, trying to affect the other"s first impressions and increase effectiveness. This paper addresses the automatically detection of the Big Five personality traits from short (30-120 seconds) self-presentations, by investigating the effectiveness of 29 simple acoustic and visual non-verbal features. Our results show that Conscientiousness and Emotional Stability Neuroticism are the best recognizable traits. The lower accuracy levels for Extraversion and Agreeableness are explained through the interaction between situational characteristics and the differential activation of the behavioral dispositions underlying those traits. This paper targets the automatic detection of personality traits in a meeting environment by means of audio and visual features; information about the relational context is captured by means of acoustic features designed to that purpose. Two personality traits are considered: Extraversion (from the Big Five) and the Locus of Control. The classification task is applied to thin slices of behaviour, in the form of 1-minute sequences. SVM were used to test the performances of several training and testing instance setups, including a restricted set of audio features obtained through feature selection. The outcomes improve considerably over existing results, provide evidence about the feasibility of the multimodal analysis of personality, the role of social context, and pave the way to further studies addressing different features setups and or targeting different personality traits. Personality is a psychological construct aimed at explaining the wide variety of human behaviors in terms of a few, stable and measurable individual characteristics. In this respect, any technology involving understanding, prediction and synthesis of human behavior is likely to benefit from Personality Computing approaches, i.e. from technologies capable of dealing with human personality. This paper is a survey of such technologies and it aims at providing not only a solid knowledge base about the state-of-the-art, but also a conceptual model underlying the three main problems addressed in the literature, namely Automatic Personality Recognition (inference of the true personality of an individual from behavioral evidence), Automatic Personality Perception (inference of personality others attribute to an individual based on her observable behavior) and Automatic Personality Synthesis (generation of artificial personalities via embodied agents). Furthermore, the article highlights the issues still open in the field and identifies potential application areas. Despite an increasing interest in understanding human perception in social media through the automatic analysis of users' personality, existing attempts have explored user profiles and text blog data only. We approach the study of personality impressions in social media from the novel perspective of crowdsourced impressions, social attention, and audiovisual behavioral analysis on slices of conversational vlogs extracted from YouTube. Conversational vlogs are a unique case study to understand users in social media, as vloggers implicitly or explicitly share information about themselves that words, either written or spoken cannot convey. In addition, research in vlogs may become a fertile ground for the study of video interactions, as conversational video expands to innovative applications. In this work, we first investigate the feasibility of crowdsourcing personality impressions from vlogging as a way to obtain judgements from a variate audience that consumes social media video. Then, we explore how these personality impressions mediate the online video watching experience and relate to measures of attention in YouTube. Finally, we investigate on the use of automatic nonverbal cues as a suitable lens through which impressions are made, and we address the task of automatic prediction of vloggers' personality impressions using nonverbal cues and machine learning techniques. Our study, conducted on a dataset of 442 YouTube vlogs and 2210 annotations collected in Amazon's Mechanical Turk, provides new findings regarding the suitability of collecting personality impressions from crowdsourcing, the types of personality impressions that emerge through vlogging, their association with social attention, and the level of utilization of nonverbal cues in this particular setting. In addition, it constitutes a first attempt to address the task of automatic vlogger personality impression prediction using nonverbal cues, with promising results.