Ruby's egocentric (network) commentary (original) (raw)

Network Approaches to Representing and Understanding Personality Dynamics

From its emergence at the beginning of the 20th century, personality scientists pursued two goals – a nomothetic approach that investigated the structure of individual differences between people in a population and an idiographic approach that explored variation within a person relative to him or herself. Implicit in both was an assumption that dynamic processes underlay the emergence of personality within and across people, but available methods at the time precluded testing dynamic questions. In this chapter, we first track the how the history of both nomothetic idiographic perspectives impacted the study of personality structure and dynamics. Next, we review findings and unanswered contemporary questions regarding nomothetic and idiographic structure, processes, and dynamics. Finally, we conclude by arguing for an idiographic network approach to understanding personality based in dynamic systems theory. We provide both theoretical questions for future research, some of which were...

Personality in the context of social networks

… of the Royal …, 2010

There is great interest in environmental effects on the development and evolution of animal personality traits. An important component of an individual's environment is its social environment. However, few studies look beyond dyadic relationships and try to place the personality of individuals in the context of a social network. Social network analysis provides us with many new metrics to characterize the social fine-structure of populations and, therefore, with an opportunity to gain an understanding of the role that different personalities play in groups, communities and populations regarding information or disease transmission or in terms of cooperation and policing of social conflicts. The network position of an individual is largely a consequence of its interactive strategies. However, the network position can also shape an individual's experiences (especially in the case of juveniles) and therefore can influence the way in which it interacts with others in future. Finally, over evolutionary time, the social fine-structure of animal populations (as quantified by social network analysis) can have important consequences for the evolution of personalities-an approach that goes beyond the conventional game-theoretic analyses that assumed random mixing of individuals in populations.

Personality and personal network type

Personality and Individual Differences, 2008

The association between personality and personal relationships is mostly studied within dyadic relationships. We examined these variables within the context of personal network types. We used Latent Class Analysis to identify groups of students with similar role relationships with three focal figures. We performed Latent Class Logistic Regression to explore the relationships of the latent classes with the Big Five personality factors. Personality was assessed with the Five Factor Personality Inventory. We found three personal network types: a primarily family oriented network, a primarily peer oriented network, and a mixed family/peer oriented network. We found significant associations between personality and personal network type. Extraverted students were more likely to have a primarily peer oriented network relative to a primarily family oriented network. Autonomous students were more likely to have a primarily family oriented network relative to a primarily peer oriented network. Autonomous students were also more likely to have a mixed family/peer oriented network relative to a primarily peer oriented network. Conscientious students were more likely to have a primarily family oriented network relative to a mixed family/peer oriented network.

Towards a dynamic view of personality

Proceedings of the 15th ACM on International conference on multimodal interaction - ICMI '13, 2013

A new perspective in the automatic recognition of personality is proposed; shifting our focus from the traditional goal of using behaviors to infer about personality traits, to the classification of excerpts of social behavior into personality states. The personality states are specific behavioral episodes that can be described as having the same content as traits wherein a person behaves more or less introvertedly/ extravertedly, more or less neurotically etc depending on the social situation. Exploiting the SociometricBadge Corpus, a first step towards addressing this new perspective is presented, starting from the automatic classification of personality states from multimodal behavioral cues. The effectiveness of these cues as well as of other situational characteristics are investigated for the sake of personality state classification. Moreover, a first approach towards the automatic discovery of situational characteristics is proposed.

Fundamental Questions in Personality

Abstract The network perspective represents a novel contribution to personality theory by conceptualizing personality traits as emerging from the mutual dependencies between fundamental and causal affective, behavioral, and cognitive components. We argue that incorporating a more nuanced biological and developmental perspective to causality and a more precise approach to affective, behavioral, cognitive, and motivational components may serve to enrich the network perspective.

From sociometry to network diary - Measuring personal networks

Acta Medicinae et Sociologica, 2011

There are many studies which deal with personal networks. We have known many methods to measure ego-centred networks. In this study I would like to introduce an alternative data collecting method to measure personal networks which is network diary. Firstly I would like to give a brief summary of methods which were used during data collection in the field of ego-centred networks. Then I would like to show the new method and some results of it. Finally I would try to sum up the advantages and disadvantages of this method.

Identifying and validating personality traits-based homophilies for an egocentric network

Social network sites (SNS) have touched the lives of millions of people around the world. People share interests, ideas, photos, activities in the social networks with their family, colleagues, friends and acquaintances. However, the degree of interactions among members widely varies. According to a sociology principle, people with similar personality often interact with each other more frequently. A group of connected people with similar personality traits is termed as a homophily. In this paper, we develop a method to identify homophilies by analyzing the Big5 personality traits of users from their interactions in an egocentric network like Facebook. We observe that our homophilies correctly cluster ranged from 73 to 87 % users for different personality traits. We also present a novel validation technique to verify those extracted homophilies in real life. Note that we are the first to validate the extracted homophilies and compare those with baseline techniques from SNS usage in real life using an interview-based method. We notice that our validation results show different agreements ranged from 0.207 (fair) to 0.709 (substantial) among the raters of those homo-philies in real-life .

Ego-centric Graphlets for Personality and Affective States Recognition

2013 International Conference on Social Computing, 2013

Do we tend to perceive ourselves more creative when surrounded by creative people? Or rather the opposite holds? Such information is very valuable to understand how to optimize work processes and boost people's productivity along with their happiness and satisfaction. Exploiting real-life data, collected over a period of six weeks in a research institution by means of wearable sensors, in this work we provide insights on human behavior dynamics in the workplace. We explore the use of graphlets, i.e. small induced subgraphs of a network, to encode the local structure of the interaction network of a subject, enriched with affective and personality states of his/her interaction partners. Our analysis shows that graphlets of increasing complexity, encoding non-trivial interaction patterns, are beneficial to affective and personality states recognition performance. We also find that different sensory channels, measuring proximity/co-location or face-to-face interactions, have different predictive power for distinct states.