Projects

This project introduces a novel approach to studying social media habits through predictive modeling of sequential smartphone user behaviors. While much of the literature on media and technology habits has relied on self-report questionnaires and simple behavioral frequency measures, we examine an important yet understudied aspect of media and technology habits: their embeddedness in repetitive behavioral sequences. Leveraging Long Short-Term Memory (LSTM) and transformer neural networks, we show that (i) social media use is predictable at the within and between-person level and that (ii) there are robust individual differences in the predictability of social media use. We examine the performance of several modeling approaches, including (i) global models trained on the pooled data from all participants, (ii) idiographic person-specific models, and (iii) global models fine-tuned on person-specific data. Neither person-specific modeling nor fine-tuning on person-specific data substantially outperformed the global models, indicating that the global models were able to represent a variety of idiosyncratic behavioral patterns. Additionally, our analyses reveal that the person-level predictability of social media use is not substantially related to the frequency of smartphone use in general or the frequency of social media use, indicating that our approach captures an aspect of habits that is distinct from behavioral frequency. Implications for habit modeling and theoretical development are discussed.

You can check out a preprint of the paper here. 

Collaborators: Joe Bayer, Sandra Matz, Yikun Chi, Sumer Vaid, Gabriella Harari

The success of online social platforms hinges on their ability to predict and understand user behavior at scale. Here, we present data suggesting that context-aware modeling approaches may offer a holistic yet lightweight and potentially privacy-preserving representation of user engagement on online social platforms. Leveraging deep LSTM neural networks to analyze more than 100 million Snapchat sessions from almost 80.000 users, we demonstrate that patterns of active and passive use are predictable from past behavior (R2=0.345) and that the integration of context information substantially improves predictive performance compared to the behavioral baseline model (R2=0.522). Features related to smartphone connectivity status, location, temporal context, and weather were found to capture non-redundant variance in user engagement relative to features derived from histories of in-app behaviors. Further, we show that a large proportion of variance can be accounted for with minimal behavioral histories if momentary context information is considered (R2=0.44). These results indicate the potential of context-aware approaches for making models more efficient and privacy-preserving by reducing the need for long data histories. Finally, we employ model explainability techniques to glean 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, underscoring the value of contextualized representations of user behavior for predicting user engagement on social platforms.

You can check out a preprint of the paper here. 

Collaborators: Yozen Liu, Francesco Barbieri, Raiyan Abdul Baten, Sandra Matz, Maarten Bos

Model Share AI

Machine learning (ML) has the potential to revolutionize a wide range of research areas and industries, but many ML projects never progress past the proof-of-concept stage. To address this issue, we introduce Model Share AI (AIMS), an easy-to-use MLOps platform designed to streamline collaborative model development, model provenance tracking, and model deployment, as well as a host of other functions aiming to maximize the real-world impact of ML research. AIMS features collaborative project spaces and a standardized model evaluation process that ranks model submissions based on their performance on unseen evaluation data, enabling collaborative model development and crowd-sourcing. Model performance and various model metadata are automatically captured to facilitate provenance tracking and allow users to learn from and build on previous submissions. Additionally, AIMS allows users to deploy ML models built in Scikit-Learn, TensorFlow Keras, PyTorch, and ONNX into live REST APIs and automatically generated web apps with minimal code. The ability to deploy models with minimal effort and to make them accessible to non-technical end-users through web apps has the potential to make ML research more applicable to real-world challenges.

A preprint of the paper is available here. 

Collaborators: Michael Parrott

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

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.

You can find the published paper here.

Collaborators: Sandrine Muller, Weichen Wang, Sandra Matz, Gabriella Harari

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.

The published paper is available here.

Collaborators: Andrew Kyngdon, David Stillwell