UnderstandinG Message Response Behaviors
Instant messaging plays a significant role in people's social and professional lives, but little is known about the factors shaping message response behaviors. In the present study, we investigate the determinants of message response behaviors from a predictive-explanatory perspective. Using a large and diverse sample of Snapchat users, we first show that message response times are highly predictable (AUC=0.97). Second, we employ ablation techniques to examine the contributions of several important groups of predictors: message attributes (such as message length, time sent, and location of sender, but not message content), user attributes, network communication patterns, and dyad-level communication patterns, as well as spatial and temporal context. The results indicate that dyad-specific communication patterns in conjunction with spatial and temporal context account for the largest share of explained target variance. Our findings are consistent with the idea of dyad-specific, context-contingent messaging habits, and a state-based view of message response behaviors. Our work has implications for the development of new systems and UX design. For example our models could facilitate context-aware delivery timing, message rankings, and availability status displays.
Collaborators: Ron Dotsch, Yozen Liu, Sandra Matz, Maarten Bos
User Engagement In Context
Predicting user behavior is critical for the success of online social platforms. We propose that user engagement on these platforms is habitual and context-contingent, and that the use of context information in predictive models offers a holistic yet light-weight and privacy-preserving representation of user engagement. Using deep neural networks in conjunction with ablation techniques, we demonstrate that patterns of active and passive use are predictable (R2 =0.522) and that the integration of context information can substantially improve the predictive performance compared to a behavioral baseline model. Our findings highlight the potential for more privacy-preserving predictive modeling by showing that a large proportion of the variance in user engagement can be explained with minimal behavioral histories if context information is considered. Finally, we employ model explainability techniques to gain preliminary insights into the underlying behavioral mechanisms. Our findings are consistent with the notion of context-contingent, habit-driven patterns of active and passive use, and highlight the value of contextualized representations of user behavior for the prediction of user engagement on social platforms.
Collaborators: Yozen Liu, Francesco Barbieri, Raiyan Abdul Baten, Sandra Matz, Maarten Bos
Organizational Fit AND JOB Tenure
Job tenure is an important organizational outcome. It is usually in the interest of both employees and employers that individuals stay in the organization for an extended time in order to be productive and contribute in meaningful ways. A potentially important factor influencing job tenure is organizational fit, the alignment of employees’ values and beliefs with those of the organization they work for. We analyze the effects of organizational fit on job tenure using a very large corpus of user data scraped from LinkedIn. The project will help to better understand how organizational fit affects job tenure and which variables moderate this effect. For example: Is organizational fit particularly important in certain companies or industries, and who are the people that profit the most/least from organizational fit?
Collaborators: Daniel Stein, Seung-Jae Bang, Huang Ke-Wei, Sandra Matz
Predicting The SPread of COVID-19
Social behaviors and compliance behaviors play a critical role in the transmission of COVID-19. Consequently, regional variation in personality traits that capture individual differences in these behaviors may offer new insight into the spread of COVID-19. We combine self-reported personality data (3.5M people), COVID-19 prevalence and death rates, and behavioral mobility observations (29M people) to show that regional personality differences in the US and Germany predict COVID-19-related outcomes and behaviors incremental to a conservative set of socio-demographic, economic, and pandemic-related control variables. Earlier onsets of COVID-19 and steeper initial growth rates were related to higher levels of Openness and lower levels of Neuroticism. We also show that (i) regional personality is associated with objective indicators of social distancing, (ii) the effects of regional personality can change over time (Openness), and that (iii) the effects of regional personality do not always converge with those observed at the individual level (Agreeableness and Conscientiousness).
Collaborators: Friedrich Götz, Tobias Ebert, Sandrine Müller, Jason Rentfrow, Sam Gosling, Martin Obschonka, Jeff Potter, Sandra Matz
Investigating the Relationships between Mobility and WellBeing
People interact with their physical environments every day by visiting different places and moving between them. Such mobility behaviours likely influence and are influenced by people's subjective well‐being. However, past research examining the links between mobility behaviours and well‐being has been inconclusive. Here, we provide a comprehensive investigation of these relationships by examining individual differences in two types of mobility behaviours (movement patterns and places visited) and their relationship to six indicators of subjective well‐being (depression, loneliness, anxiety, stress, affect, and energy) at two different temporal levels of analysis (two‐week tendencies and daily level). Using data from a large smartphone‐based longitudinal study (N = 1765), we show that (i) movement patterns assessed via GPS data (distance travelled, entropy, and irregularity) and (ii) places visited assessed via experience sampling reports (home, work, and social places) are associated with subjective well‐being at the between and within person levels. Our findings suggest that distance travelled is related to anxiety, affect, and stress, irregularity is related to depression and loneliness, and spending time in social places is negatively associated with loneliness. We discuss the implications of our work and highlight directions for future research on the generalizability to other populations as well as the characteristics of places.
Collaborators: Sandrine Muller, Weichen Wang, Sandra Matz, Gabriella Harari
Using Sensory Substitution to Improve decision Making
Our brains are able to juggle vast amounts of information unconsciously, but we often fail to make optimal decisions when faced with conscious decisions that require us, for example, to memorize and manipulate numbers or estimate probabilities. Here, we explore whether we can overcome some of these cognitive limitations in everyday decision making by enabling people to feel data in a more holistic way and rely on intuitive rather than deliberative cognitive processes. Specifically, we propose that sensory substitution - a concept used in neuroscience to describe the process of encoding a particular type of sensory information in a way that makes this information accessible to a different sensory modality - can improve the quality of decision making. To test this idea, we are (1) developing a tactile interface that enables us to convey complex information through vibrations, and (2) running a series of rigorous experiments to demonstrate that sensory substitution can not only expand our sensory horizon, but also improve decision making in the lab and in the real world.
Collaborators: Moran Cerf, Sandra Matz
Measuring Spatial Reasoning Ability with Minecraft
Video games are a promising tool for the psychometric assessment of cognitive abilities. They can present novel task types and answer formats, they can record process data, and they can be highly motivating for test takers. This paper introduces the first game-based intelligence assessment implemented in Minecraft, an exceptionally popular video game with 176m copies sold. We found that abilities measured with Minecraft and conventional tests were highly correlated at the latent level (r = .72). Furthermore we found that behavioral log-data collected from the game environment was highly predictive of performance in the Minecraft test and, to a lesser extent, also predicted scores in conventional tests. We identify a number of behavioral features associated with spatial reasoning ability, demonstrating the utility of analyzing granular behavioral data in addition to traditional response formats. Overall, our findings indicate that Minecraft is a suitable platform for game-based intelligence assessment and encourage future work aiming to explore game-based problem solving tasks that would not be feasible on paper or in conventional computer-based tests.
Collaborators: Andrew Kyngdon, David Stillwell
The Big Data Toolkit for Psychologists
I have summed up my approach to data analysis for a chapter of the APA handbook, The Psychology of Technology. It serves as a practical introduction for psychologists who want to use large data sets and data sets from nontraditional data sources in their research. First, the chapter discusses the concept of Big Data and reviews some of the theoretical challenges and opportunities that arise with the availability of ever larger amounts of data. Second, it discusses practical implications and best practices with respect to data collection, data storage, data processing, and data modeling for psychological research in the age of Big Data.
Collaborators: Sam Gosling, Zachariah Marrero