Understanding how discussion dynamics shape team creativity has been limited by the difficulty of measuring process at scale.We introduce TRACE, a corpus of 309 group discussions from 103 teams (421 participants) across six creative problem-solving tasks.The dataset follows an input-process-output framework, integrating team composition (demographics, personalities), full discussion transcripts, and creativity outcomes.Using sentence embeddings and factor analysis, we identify four interpretable discussion dimensions:Coherence, Exploration, Convergence, and Participation.Analysis reveals a depth-breadth trade-off: coherent idea development inversely relates to semantic exploration.Larger teams explore more broadly but converge less effectively while team diversity shapes participation patterns more than discussion content.Novelty and usefulness in the creativity outcomes follow distinct pathways: Exploration and Convergence predict novelty, whereas Coherence predicts usefulness.These findings ground our understanding of how teams talk their way to creative solutions and provide guidance for designing multiagent systems.

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.

Profile images on social networks are users' opportunity to present themselves and to affect how others judge them. We examine what Facebook images say about users' perceived and measured intelligence. 1,122 Facebook users completed a matrices intelligence test and shared their current Facebook profile image. Strangers also rated the images for perceived intelligence. We use automatically extracted image features to predict both measured and perceived intelligence. Intelligence estimation from images is a difficult task even for humans, but experimental results show that human accuracy can be equalled using computing methods. We report the image features that predict both measured and perceived intelligence, and highlight misleading features such as "smiling'' and "wearing glasses'' that are correlated with perceived but not measured intelligence. Our results give insights into inaccurate stereotyping from profile images and also have implications for privacy, especially since in most social networks profile images are public by default.

Personality questionnaire items are normally written by experts in personality theory or psychometrics. This makes personality test development expensive, especially since as many as 40% of the items are discarded after quantitative analysis. Could the crowd create personality items that are as good as the ones developed by professionals? We analyze the psychometric quality of 164 Big Five personality items generated by the crowd (n=82) and show that non-expert item writers are capable of designing high quality personality items. The analysis revealed that 59% of the crowdsourced items had adequate psychometric properties, and thus could be used as an alternative instrument to measure Big Five personality traits. We conclude that crowdsourcing personality items is a possible strategy to generate new items rapidly. We also discuss some of the challenges of crowdsourcing general psychological items.

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?

H. Andrew Schwartz, Gregory Park, Maarten Sap, Evan Weingarten, Johannes Eichstaedt, Margaret Kern, David Stillwell, Michal Kosinski, Jonah Berger, Martin Seligman, Lyle Ungar. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.

We explore the factors that determine whether individuals are likely to experience intrinsic motivation in end-user programming (EUP). We report two experiments: one that tests whether there are reliable psychometric constructs that describe different aspects of intrinsic motivation, and one that tests whether these constructs are successful in predicting individuals' own self-reported intrinsic motivation after using a popular EUP product. We conclude that there are identifiable and distinct motivational factors in EUP, and that these are associated with particular psychometric personality traits. We offer several suggestions for future research that could apply these findings to improve uptake and quality of user experience for educational and general-purpose EUP applications.

Maarten Sap, Gregory Park, Johannes Eichstaedt, Margaret Kern, David Stillwell, Michal Kosinski, Lyle Ungar, Hansen Andrew Schwartz. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014.

Individual differences in personality affect users’ online activities as much as they do in the offline world. This work, based on a sample of over a third of a million users, examines how users’ behaviour in the online environment, captured by their website choices and Facebook profile features, relates to their personality, as measured by the standard Five Factor Model personality questionnaire. Results show that there are psychologically meaningful links between users’ personalities, their website preferences and Facebook profile features. We show how website audiences differ in terms of their personality, present the relationships between personality and Facebook profile features, and show how an individual’s personality can be predicted from Facebook profile features. We conclude that predicting a user’s personality profile can be applied to personalize content, optimize search results, and improve online advertising.

We study the relationship between Facebook popularity (number of contacts) and personality traits on a large number of subjects. We test to which extent two prevalent viewpoints hold. That is, popular users (those with many social contacts) are the ones whose personality traits either predict many offline (real world) friends or predict propensity to maintain superficial relationships. We find that the predictor for number of friends in the real world (Extraversion) is also a predictor for number of Facebook contacts. We then test whether people who have many social contacts on Facebook are the ones who are able to adapt themselves to new forms of communication, present themselves in likable ways, and have propensity to maintain superficial relationships. We show that there is no statistical evidence to support such a conjecture.