Felix Beierle - Integrating Psychoinformatics with Ubiquitous Social Networking: Advanced Mobile-Sensing Concepts and Applications (T-Labs Series in Telecommunication Services)

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There are fundamental differences in the way people experience behaviors and emotions. The field of psychometrics allows the quantifiable measurement of personality traits that describe such inter-personal differences. Vast amounts of psychological and psychoinformatical studies show that personality traits are not only associated with different everyday preferences, but also with the way people form interpersonal bonds in social networks. However, these research results are rarely considered in the research and development of social networking services. We argue that such understanding of people, their behaviors, emotions, and how they form bonds with other people can be integrated in social networking services and may ultimately improve real-life social well-being. The objective of this doctoral thesis thus is the integration of psychoinformaticsthe intersection of psychology and computer sciencewith ubiquitous social networking.

Psychoinformatics uses objective measurements to research and deepen the understanding of personality-related differences in human experiences and behaviors. A common approach is mobile sensing. Such tracking of sensor data has serious implications regarding the privacy of users. However, currently, there are no structured approaches in dealing with these privacy concerns. Furthermore, when planning a psychoinformatical study, it is unclear to what extent demographical properties and personality traits play a role when collecting data from daily life scenarios. We developed a structured approach for privacy-awareness for mobile sensing in psychoinformatics and conduct a study on what data researchers can expect users to be willing to share. Our results can help researchers with study planning, and study participants benefit by increased privacy-awareness. While there have been several studies related to personality traits and smartphone data, there are knowledge gaps, specifically with regard to smartphone usage frequency and duration in relation to the users personality. We collected and analyzed data and fill this gap. Our results are valuable for psychologists, for example, when researching smartphone overuse. Additionally, our results helped researchers and software developers of mobile systems understand their user base better.

In particular, we designed, developed, and deployed the Android app TYDRTrack Your Daily Routinefor the collection of smartphone sensor and usage data. We developed and implemented PM-MoDaC, a full-scale privacy model for mobile data collection apps, consisting of nine different technical and design-related measures. We released TYDR on Google Play and attracted nearly 4,000 users. Evaluating our privacy model, we analyzed what data users are willing to share with researchers; for example, younger users tend to be less willing to share data. We conducted a study analyzing the relationship between personality traits and smartphone usage frequency and usage session duration. On average, the users in our sample (n = 526) used their smartphone 72 times per day, with a mean session duration of 3.7 minutes. Our study reveals that neurotic and extraverted users use their phones more frequently, while conscientious users have shorter session durations.