Extroverts Tweet Differently from Introverts in Weibo

Being dominant factors driving the human actions, personalities can be excellent indicators in predicting the offline and online behavior of different individuals. However, because of the great expense and inevitable subjectivity in questionnaires and surveys, it is challenging for conventional studies to explore the connection between personality and behavior and gain insights in the context of large amount individuals. Considering the more and more important role of the online social media in daily communications, we argue that the footprint of massive individuals, like tweets in Weibo, can be the inspiring proxy to infer the personality and further understand its functions in shaping the online human behavior. In this study, a map from self-reports of personalities to online profiles of 293 active users in Weibo is established to train a competent machine learning model, which then successfully identifies over 7,000 users as extroverts or introverts. Systematical comparisons from perspectives of tempo-spatial patterns, online activities, emotion expressions and attitudes to virtual honor surprisingly disclose that the extrovert indeed behaves differently from the introvert in Weibo. Our findings provide solid evidence to justify the methodology of employing machine learning to objectively study personalities of massive individuals and shed lights on applications of probing personalities and corresponding behaviors solely through online profiles.Comment: Datasets of this study can be freely downloaded through: https://doi.org/10.6084/m9.figshare.4765150.v

Predicting Personality from Book Preferences with User-Generated Content Labels

Psychological studies have shown that personality traits are associated with book preferences. However, past findings are based on questionnaires focusing on conventional book genres and are unrepresentative of niche content. For a more comprehensive measure of book content, this study harnesses a massive archive of content labels, also known as 'tags', created by users of an online book catalogue, Goodreads.com. Combined with data on preferences and personality scores collected from Facebook users, the tag labels achieve high accuracy in personality prediction by psychological standards. We also group tags into broader genres, to check their validity against past findings. Our results are robust across both tag and genre levels of analyses, and consistent with existing literature. Moreover, user-generated tag labels reveal unexpected insights, such as cultural differences, book reading behaviors, and other non-content factors affecting preferences. To our knowledge, this is currently the largest study that explores the relationship between personality and book content preferences

Development and Maintenance of Self-Disclosure on Facebook: The Role of Personality Traits

This study explored the relationships between Facebook self-disclosure and personality traits in a sample of Italian users. The aim was to analyze the predictive role of Big Five personality traits on different parameters of breadth and depth of selfdisclosed behaviors online. Facebook users, aged between 18 and 64 years of age (Mage = 25.3 years, SD = 6.8; N = 958), of which 51% were female, voluntarily completed an online survey assessing personality traits and Facebook self-disclosure. Results at a series of hierarchical regression analyses significantly corroborated the hypotheses that high extroverted and openness people tend to disclose on Facebook a significant amount of personal information, whereas high consciousness and agreeableness users are less inclined to do it. Furthermore, more extroverts and agreeableness people develop less intimacy on Facebook, differently from those with high levels of openness. Results also corroborated the hypothesis of a full mediation of time usage in the relationship between personality factors such as extroversion and conscientiousness with breadth of Facebook self-disclosure. Overall, according to the findings of the current study, personality traits and Facebook self-disclosure become central both as predictive variables for depicting the different profiles of potential addicted and as variables to help educators, teachers, and clinicians to develop training or therapeutic programs aimed at preventing the risk of Internet addiction. Limitations of the study are discussed, and directions for future research are suggested

Towards Psychometrics-based Friend Recommendations in Social Networking Services

Two of the defining elements of Social Networking Services are the social profile, containing information about the user, and the social graph, containing information about the connections between users. Social Networking Services are used to connect to known people as well as to discover new contacts. Current friend recommendation mechanisms typically utilize the social graph. In this paper, we argue that psychometrics, the field of measuring personality traits, can help make meaningful friend recommendations based on an extended social profile containing collected smartphone sensor data. This will support the development of highly distributed Social Networking Services without central knowledge of the social graph.Comment: Accepted for publication at the 2017 International Conference on AI & Mobile Services (IEEE AIMS

Big-Five Personality Prediction Based on User Behaviors at Social Network Sites

Many customer services are already available at Social Network Sites (SNSs), including user recommendation and media interaction, to name a few. There are strong desires to provide online users more dedicated and personalized services that fit into individual's need, usually strongly depending on the inner personalities of the user. However, little has been done to conduct proper psychological analysis, crucial for explaining the user's outer behaviors from their inner personality. In this paper, we propose an approach that intends to facilitate this line of research by directly predicting the so called Big-Five Personality from user's SNS behaviors. Comparing to the conventional inventory-based psychological analysis, we demonstrate via experimental studies that users' personalities can be predicted with reasonable precision based on their online behaviors. Except for proving some former behavior-personality correlation results, our experiments show that extraversion is positively related to one's status republishing proportion and neuroticism is positively related to the proportion of one's angry blogs (blogs making people angry).Comment: 7 pages, predicting user's Big-five personality based on Social Network behavior

Inferring Human Traits From Facebook Statuses

This paper explores the use of language models to predict 20 human traits from users' Facebook status updates. The data was collected by the myPersonality project, and includes user statuses along with their personality, gender, political identification, religion, race, satisfaction with life, IQ, self-disclosure, fair-mindedness, and belief in astrology. A single interpretable model meets state of the art results for well-studied tasks such as predicting gender and personality; and sets the standard on other traits such as IQ, sensational interests, political identity, and satisfaction with life. Additionally, highly weighted words are published for each trait. These lists are valuable for creating hypotheses about human behavior, as well as for understanding what information a model is extracting. Using performance and extracted features we analyze models built on social media. The real world problems we explore include gendered classification bias and Cambridge Analytica's use of psychographic models.Comment: Submitted to the International Conference on Social Informatics 201

Identifying Social Satisfaction from Social Media

We demonstrate the critical need to identify social situation and instability factors by acquiring public social satisfaction in this research. However, subject to the large amount of manual work cost in subject recruitment and data processing, conventional self-reported method cannot be implemented in real time or applied in large scale investigation. To solve the problem, this paper proposed an approach to predict users' social satisfaction, especially for the economy-related satisfaction based on users' social media records. We recruited 2,018 Cantonese active participants from each city in Guangdong province according to the population distribution. Both behavioral and linguistic features of the participants are extracted from the online records of social media, i.e., Sina Weibo. Regression models are used to predict Sina Weibo users' social satisfaction. Furthermore, we consult the economic indexes of Guangdong in 2012, and calculate the correlations between these indexes and the predicted social satisfaction. Results indicate that social satisfaction can be significantly expressed by specific social media features; local economy satisfaction has significant positive correlations with several local economy indexes, which supports that it is reliable to predict social satisfaction from social media.Comment: 8 pages, 2 figure

Confiding in and Listening to Virtual Agents: The Effect of Personality

We present an intelligent virtual interviewer that engages with a user in a text-based conversation and automatically infers the user's psychological traits, such as personality. We investigate how the personality of a virtual interviewer influences a user's behavior from two perspectives: the user's willingness to confide in, and listen to, a virtual interviewer. We have developed two virtual interviewers with distinct personalities and deployed them in a real-world recruiting event. We present findings from completed interviews with 316 actual job applicants. Notably, users are more willing to confide in and listen to a virtual interviewer with a serious, assertive personality. Moreover, users' personality traits, inferred from their chat text, influence their perception of a virtual interviewer, and their willingness to confide in and listen to a virtual interviewer. Finally, we discuss the implications of our work on building hyper-personalized, intelligent agents based on user traits

The effect of personality on collaborative task performance and interaction

Collocated, multi-user technologies, which support group-work are becoming increasingly popular. Examples include MERL's Diamondtouch and Microsoft's Surface, both of which have evolved from research prototypes to commercial products. Many applications have been developed for such technologies which support the work and entertainment needs of small groups of people. None of these applications however, have been studied in terms of the interactions and performances of their users with regards to their personality. In this paper, we address this research gap by conducting a series of user studies involving dyads working on a number of multi-user applications on the DiamondTouch tabletop device

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field