Sociability as a disposition describes a tendency to affiliate with others (vs. be alone).Yet, we know relatively little about how much social behavior people engage in during a typical day. One challenge to documenting behavioral sociability tendencies is the broad number of channels over which socializing can occur, both in-person and through digital media. To provide an assessment of individual differences in everyday social behavior patterns, here we used smartphone-based mobile sensing methods (MSMs) in four studies (total N = 1078) to collect real-world data about the sensed social behaviors of young adults across four communication channels: conversations, phone calls, text messages, and messaging and social media application use. To examine individual differences, we first focused on establishing between-person variability in daily social behavior, examining stability of and relationships among daily sensed social behavior tendencies. To explore factors that may explain the observed individual differences in sensed social behavior, we then expanded our focus to include other time estimates (e.g., times of the day, days of the week) and personality traits. In doing so, we present the first large-scale descriptive portrait of behavioral sociability patterns, characterizing the degree of social behavior young adults typically engaged in and mapping behavioral to self-reported personality dispositions. Our discussion focuses on how the observed sociability patterns compare to previous research on young adults' social behavior. We conclude by pointing to areas for future research aimed at understanding sociability using mobile sensing and other naturalistic observation methods for the assessment of social behavior.

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This chapter discusses main opportunities and challenges of assessing and utilizing personality traits in personalized interactive systems and services. This unique perspective arises from our long-term collaboration on research projects involving three groups from Human-Computer Interaction (HCI), Psychology, and Statistics. Currently, personalization in HCI is often based on past user behavior, preferences, and interaction context. We argue that personality traits provide a promising additional source of information for personalization, which goes beyond context-and device-specific behavior and preferences. We first give an overview of the well-established Big Five personality trait model from Psychology. We then present previous findings on the influence of personality in HCI associated with the benefits and challenges of personalization. These findings include the preference for interactive systems, filtering of information to increase personal relevance, communication behavior, and the impact on trust and acceptance. Moreover, we present first approaches of personality-based recommender systems. We then identify several opportunities and use cases for personality-aware personalization: (1) personal communication between users, (2) recommendations upon first use, (3) persuasive technology, (4) trust and comfort in autonomous vehicles, and (5) empathic intelligent systems.

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