Network Diversity and Affect Dynamics: The Role of Personality Traits

Abstract

People divide their time unequally among their social contacts due to time constraints and varying strength of relationships. It was found that high diversity of social communication, dividing time more evenly among social contacts, is correlated with economic well-being both at macro and micro levels. Besides economic well-being, it is not clear how the diversity of social communication is also associated with the two components of individuals’ subjective well-being, positive and negative affect. Specifically, positive affect and negative affect are two independent dimensions representing the experience (feeling) of emotions. In this paper, we investigate the relationship between the daily diversity of social communication and dynamic affect states that people experience in their daily lives. We collected two high-resolution datasets that capture affect scores via daily experience sampling surveys and social interaction through wearable sensing technologies: sociometric badges for face-to-face interaction and smart phones for mobile phone calls. We found that communication diversity correlates with desirable affect states–e.g. an increase in the positive affect state or a decrease in the negative affect state–for some personality types, but correlates with undesirable affect states for others. For example, diversity in phone calls is experienced as good by introverts, but bad by extroverts; diversity in face-to-face interaction is experienced as good by people who tend to be positive by nature (trait) but bad for people who tend to be not positive by nature. More broadly, the moderating effect of personality type on the relationship between diversity and affect was detected without any knowledge of the type of social tie or the content of communication. This provides further support for the power of unobtrusive sensing in understanding social dynamics, and in measuring the effect of potential interventions designed to improve well-being.

Sociometric Mapping for Predictive Performance Analysis: The Measurement of Attitudes of Social Acceptance or Rejection through Expressed Preferences among Members of a Social Grouping

“…As a consequence stopping to assist, or reconnect with these self-excluders through the adoption of mirrored self-exclusion, appeared an unattractive proposition and a high risk to their own chances of success. The view of Alshamsi et al where the strength of the group or atom can be captured for the greater good of everyone could be seen to be effective only when the whole atom has strong relationships, Alshamsi et al [3]. The perspective from the lecture team was twofold in that they wanted everyone in the class to have a positive experience and that they all achieved.…”

“…As with Remerean theory, and as stated earlier within this paper, different perspectives, such as Hale's from within the atom are noted, but not included for reason of scope and brevity. The role of network diversity and personality traits can be found amongst the work of Alshamsi et al, should the reader wish to explore the some of the issues and reframe the next phase of the research, Alshamsi et al [3].…”

“…Where un-orchestrated membership of a social atom is initially enforced by external actors' such as in the case of students in a Higher Education setting, membership strength of relationships is conditional upon certain factors that can be both individual and collectively centered, Hale [2]. The outcome of such relations allows the network effect of the group to either enhance or weaken the performance of the atom, Alshamsi et al [3]. Personality traits allow the actors of the atom to select the distance they want to enforce within the group and accordingly the end result could, potentially, be good, bad or varied depending on the circumstances.…”

Anomaly Detection in Elderly Daily Behavior in Ambient Sensing Environments

“…The use of pervasive computing devices has recently supported advances in understanding personality, preferences, emotions, human mobility and daily activity routines from longitudinal data [5,22,14,9,8]. A significant population that would benefit from accurate inferences and clear understanding of daily activity routines is the elderly population.…”

“…Multimodal and more seamless embedded sensors support advances in human behavior understanding, covering a diversity of challenging topics including personality, emotions, human mobility and activities in everyday life [5,22,14,9,8]. The learned lessons make room to implement convenient applications aiming to enhance day to day living of people through their different age span.…”