Publications

Peters, H., Matz, S. C. (2024). Large Language Models Can Infer Psychological Dispositions of Social Media Users. PNAS Nexus, 3(6), pgae231. (available here)

Matz, S. C., Teeny, J. D., Vaid, S. S., Peters, H., Harari, G. M., & Cerf, M. (2024). The potential of generative AI for personalized persuasion at scale. Scientific Reports, 14(1), 4692. (available here)

Peters, H., Bayer, J. B., Matz, S. C., Chi, Y., Vaid, S. S., Harari, G. M. (2024). Social Media Use is Predictable from App Sequences: Using LSTM and Transformer Neural Networks to Model Habitual Behavior. Computers in Human Behavior. (available here)

Peters, H., Liu, Y., Barbieri, F., Baten, R. A., Matz, S. C., & Bos, M. W. (2024, in press). Context-Aware Prediction of User Active and Passive User Engagement: Evidence from a Large Online Social Platform. Journal of Big Data, 11(110). (available here)

Peters, H., & Parrott, M. (2023). Model Share AI: An Integrated Toolkit for Collaborative Machine Learning Model Development, Provenance Tracking, and Deployment in Python. Python in Science (SciPy Proceedings).  (available here)

Peters, H., Hashemi, A., & Rae, J. (2024).Generalizable Error Modeling for Human Data Annotation: Evidence From an Industry-Scale Search Data Annotation Program. ACM Journal of Data and Information Quality, 16(3), 17:1-17:15 (available here)

Grunenberg, E., Peters, H., Francis, M. J., Back, M., & Matz, S. (2024). Machine Learning in Recruiting: Predicting Personality from CVs and Short Text Responses. Frontiers in Social Psychology. (available here)

Peters, H., Matz, S. C., & Cerf, M. (2023). Sensory substitution can improve decision-making. Computers in Human Behavior, 146, 107797. (available here)

Peters, H., Götz, F. M., Ebert, T., Müller, S. R., Rentfrow, P. J., Gosling, S. D., Obschonka, M., Ames, D., Potter, J., & Matz, S. C. (2023). Regional personality differences predict variation in early COVID-19 infections and mobility patterns indicative of social distancing. Journal of Personality and Social Psychology. (available here)

Baten, R. A., Liu, Y., Peters, H., Barbieri, F., Shah, N., Neves, L., & Bos, M. W. (2023). Predicting Future Location Categories of Users in a Large Social Platform. Proceedings of the International AAAI Conference on Web and Social Media, 17, 47–58. (available here)

Matz, S. C., Bukow, C. S., Peters, H., Deacons, C., Dinu, A., & Stachl, C. (2023). Using machine learning to predict student retention from socio-demographic characteristics and app-based engagement metrics. Scientific Reports, 13(1). (available here)

Peters, H., Marrero, Z., & Gosling, S. D. (2022). The Big Data toolkit for psychologists: Data sources and methodologies. In The psychology of technology: Social science research in the age of Big Data (pp. 87–124). American Psychological Association. (available here)

Peters, H., Kyngdon, A., & Stillwell, D. (2021). Construction and validation of a game-based intelligence assessment in Minecraft. Computers in Human Behavior, 119, 106701. (available here)

Müller, S. R., Chen, X., Peters, H., Chaintreau, A., & Matz, S. C. (2021). Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples. Scientific Reports, 11(1), 14007. (available here)

Müller, S. R., Peters, H., Matz, S. C., Wang, W., & Harari, G. M. (2020). Investigating the Relationships between Mobility Behaviours and Indicators of Subjective Well–Being Using Smartphone–Based Experience Sampling and GPS Tracking. European Journal of Personality, 34(5), 714–732. (available here)

Peters, H., Cerf, M.,  Matz, S. C. (2024). Large Language Models Can Infer Personality from Free-Form User Interactions. arXiv:2405.13052. (available here)

Matz, S.c., Peters, H., Eastwick, P., Finkel, E. (2024). Do Large Language Models Understand Verbal Indicators of Romantic Attraction?. psyarxiv/fqkgn. (available here)

Peters, H. (2024. A Practical Approach to Deploying LLM Chatbots for Human-AI Interaction Research. psyarxiv/mhs37. (available here)

Peters, H., & Matz, S. C. (2024) Large Language Models Can Infer Psychological Dispositions of Social Media Users. European Conference on Personality, Symposium: Personality Computing - New Perspectives on Machine Learning Methods in Personality Research, Berlin, Germany.

Peters, H., & Parrott, M. (2024). Model Share AI: An Integrated Toolkit for Collaborative Machine Learning Model Development, Provenance Tracking, and Deployment in Python. Python in Science Conference. Tacoma, WA.

Peters, H., Liu, Y., Barbieri, F., Baten, R., Matz, S.C., S., Bos, M. (2023). User Engagement in Context - Predicting Patterns of Active and Passive Use on Snapchat. SPSP Annual Convention, Symposium: Contextualizing Psychological States and Digital Media Behaviors in Everyday Life. Atlanta, GA.

Peters, H., Dotsch, R., Liu, Y., Matz, S. C., & Bos, M. W. (2022). Understanding the Determinants of Message-Response Behaviors in Instant Messaging. New Directions in the Psychology of Technology, Philadelphia, PA, USA.

Peters, H., Götz, F., Ebert, T., Müller, S., Rentfrow, J., Gosling, S., Obschonka, M., Ames, D., Potter, J., & Matz, S. (2021). Regional personality differences predict variation in COVID-19 infections and social distancing behavior. Academy of Management.

Peters, H., Götz, F., Ebert, T., Müller, S., Rentfrow, J., Gosling, S., Obschonka, M., Potter, J., & Matz, S. (2021). Regional Personality Differences Predict Variation in COVID-19 Infections, Deaths and Social Distancing Behavior. SPSP Annual Convention, Symposium on New Frontiers in Geographical Psychology.

Peters, H., Götz, F., Ebert, T., Müller, S., Rentfrow, J., Gosling, S., Obschonka, M., Potter, J., & Matz, S. (2021). Regional Personality Differences Predict the Early Spread of COVID-19. SPSP Preconference Media and Technology.

Peters, H., Mueller, S., Matz, S., Wang, W., Harari, G. (2020). Investigating the Relationships Between Mobility Behaviors and Subjective Well-Being Among Young Adults. SPSP Annual Convention, New Orleans, LA, USA.

Peters, H., Kyngdon, A., Stillwell, D. (2019) Psychometric Testing in Minecraft / Project Malmo: Using an AI Experimentation Platform for Human Intelligence Assessment. SPSP Preconference Psychology of Media and Technology, Portland, OR, USA.

Peters, H., Kyngdon, A., Stillwell, D. (2017). Creating a Psychometric Assessment with Project Malmo. Designing Tasks for the Future of AI - Project Malmo Workshop, Long Beach, CA, USA.

Peters, H., Kyngdon, A., Stillwell, D. (2017) Creating an Educational Assessment in Minecraft. International Conference on Educational Data Mining and Applications, Beijing, China.

https://www.ejp-blog.com/blog/2020/5/11/a-conversation-with-sandrine-mueller-and-heinrich-peters

https://www.jbs.cam.ac.uk/insight/2022/how-minecraft-can-be-used-in-assessment/