Facebook Profiles and Usage as Indicators of Personality

The online social networking website, Facebook, has greatly changed the way the world communicates. Face-to-face interactions have been replaced by wall posts, status updates and friends liking posts or leaving comments. This study looks at how certain cues on Facebook profiles relate to personality traits, specifically, extraversion, conscientiousness and emotional stability. Three hypotheses focused on profile photos and how frequently the users change their photo. I predicted that 1) extraversion scores would be higher for participants who use a party scene as their profile photo, 2) conscientiousness scores would be lower for these same participants, and 3) the emotional stability scores would be negatively related to profile photo changing frequency. A total of 170 first year college students at Bryant University were surveyed about personality traits and Facebook usage. Out of this sample, 59 users provided access to their profiles and profile picture for data coding. The first hypothesis, that extraversion and party photos are positively related, was supported. The other two were not. However, additional analyses using the self-reported behaviors from the Facebook usage survey identified several other Facebook characteristics and behaviors that could be used as an indicator for each of the three personality traits studied

Are All Successful Communities Alike? Characterizing and Predicting the Success of Online Communities

The proliferation of online communities has created exciting opportunities to study the mechanisms that explain group success. While a growing body of research investigates community success through a single measure -- typically, the number of members -- we argue that there are multiple ways of measuring success. Here, we present a systematic study to understand the relations between these success definitions and test how well they can be predicted based on community properties and behaviors from the earliest period of a community's lifetime. We identify four success measures that are desirable for most communities: (i) growth in the number of members; (ii) retention of members; (iii) long term survival of the community; and (iv) volume of activities within the community. Surprisingly, we find that our measures do not exhibit very high correlations, suggesting that they capture different types of success. Additionally, we find that different success measures are predicted by different attributes of online communities, suggesting that success can be achieved through different behaviors. Our work sheds light on the basic understanding of what success represents in online communities and what predicts it. Our results suggest that success is multi-faceted and cannot be measured nor predicted by a single measurement. This insight has practical implications for the creation of new online communities and the design of platforms that facilitate such communities.Comment: To appear at The Web Conference 201

How HEXACO personality traits predict different selfie-posting behaviors among adolescents and young adults

Selfies are usually defined as self-portrait photos shared on social networks. Recent studies investigated how personality traits, and specifically narcissism, can be associated to different kinds of selfies. The HEXACO model, a new theory on personality structure, investigates personality on six dimensions, among which there is the Honesty/Humility trait, found strongly and negatively associated to narcissism. Thus, this study aims to investigate how different kinds of selfies could be predicted by HEXACO personality traits, controlling for age, gender and sexual orientation. Participants were 750 adolescents and young adults (59.1% girls, N = 443) from 13 to 30 years (Mage = 20.96; SDage = 4.23) who completed an online survey composed by the Kinsey scale, three questions about the frequency of different kinds of selfies (i.e. own selfies, group selfies and selfies with partner) and 60-item Hexaco Personality Inventory-Revised. Results showed that females, adolescents and not- exclusively heterosexuals posted more own selfies, and that adolescents posted also more group selfies and selfies with partner. Moreover lower Honesty/Humility, lower Conscientiousness, higher Emotionality and higher Extraversion significantly predict both own selfies and group selfies. Finally, only lower Honesty/Humility and higher Emotionality predict selfies with partner. Results suggested a common pattern of personality traits that can explain selfies behaviors according to literature on HEXACO model. Specifically, these findings enlightened that Honesty/Humility and Emotionality traits seem to be relevant in understanding selfies. People who post more selfies are lower in Honesty/Humility, showing a strong sense of self-importance and feeling superior. Moreover, they show higher Emotionality that is related to looking for social reinforcement on social networks. Only for own and group selfies, people high in Extraversion probably feel self-confident in groups, also in the online dimension, and low extraverted people probably posted less frequently because they feel uncomfortable being at the center of attention. Finally, people with high Conscientiousness spend less time online because they consider social networks as a distraction from their tasks. Thus, HEXACO model allows to better understand which personality traits can predict different kinds of selfies. Limitations and implications for future research are discussed

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

Exploring personality-targeted UI design in online social participation systems

We present a theoretical foundation and empirical findings demonstrating the effectiveness of personality-targeted design. Much like a medical treatment applied to a person based on his specific genetic profile, we argue that theory-driven, personality-targeted UI design can be more effective than design applied to the entire population. The empirical exploration focused on two settings, two populations and two personality traits: Study 1 shows that users' extroversion level moderates the relationship between the UI cue of audience size and users' contribution. Study 2 demonstrates that the effectiveness of social anchors in encouraging online contributions depends on users' level of emotional stability. Taken together, the findings demonstrate the potential and robustness of the interactionist approach to UI design. The findings contribute to the HCI community, and in particular to designers of social systems, by providing guidelines to targeted design that can increase online participation. Copyright © 2013 ACM

The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits

Flickr allows its users to tag the pictures they like as “favorite”. As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-the art of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits)

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body