Bio


Gabriella Harari is an Assistant Professor in the Department of Communication at Stanford University, where she directs the Media and Personality Lab.

She studies how personality is expressed in the physical and digital contexts of everyday life. Much of her research is focused on understanding what digital technologies reveal about who we are, and how use of digital technologies shapes who we are. Her current projects analyze people’s everyday behavioral patterns (e.g., social interactions, mobility) and environmental contexts (e.g., places visited, social media platforms) to show how they are associated with individual differences in personality and well-being.

Harari takes an ecological approach to conducting her research, emphasizing the importance of studying people and their behavior in natural contexts. To that end, she conducts intensive longitudinal field studies and is interested in mobile sensing methods and analytic techniques that combine approaches from the social and computer sciences. For example, methodologies she uses in her work in include surveys, experience sampling, longitudinal modeling, mobile sensing, data mining, and machine learning.

Harari completed a Postdoctoral Fellowship and earned her PhD at the Department of Psychology at The University of Texas at Austin. She completed her BA in Psychology & Humanities from Florida International University, where she was also a Ronald E. McNair Scholar. Her work has been published in academic outlets such as Perspectives in Psychological Science, Journal of Personality and Social Psychology, and the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). Her work has also been supported by the National Science Foundation and Stanford HAI Seed Grant Awards.

Academic Appointments


Professional Education


  • BA, Florida International University, Psychology & Humanities (2011)
  • PhD, The University of Texas at Austin, Psychology (2016)

2021-22 Courses


Stanford Advisees


All Publications


  • Personality-Place Transactions: Mapping the Relationships Between Big Five Personality Traits, States, and Daily Places JOURNAL OF PERSONALITY AND SOCIAL PSYCHOLOGY Matz, S. C., Harari, G. M. 2021; 120 (5): 1367-1385

    Abstract

    People actively select their environments, and the environments they select can alter their psychological characteristics in the moment and over time. Such dynamic person-environment transactions are likely to play out in the context of daily life via the places people spend time in (e.g., home, work, or public places like cafes and restaurants). This article investigates personality-place transactions at 3 conceptual levels: stable personality traits, momentary personality states, and short-term personality trait expressions. Three 2-week experience sampling studies (2 exploratory and 1 confirmatory with a total N = 2,350 and more than 63,000 momentary assessments) were used to provide the first large-scale evidence showing that people's stable Big Five traits are associated with the frequency with which they visit different places on a daily basis. For example, extraverted people reported spending less time at home and more time at cafés, bars, and friends' houses. The findings also show that spending time in a particular place predicts people's momentary personality states and their short-term trait expression over time. For example, people reported feeling more extraverted in the moment when spending time at bars/parties, cafés/restaurants, or friends' houses, compared with when at home. People who showed preferences for spending more time in these places also showed higher levels of short-term trait extraversion over the course of 2 weeks. The findings make theoretical contributions to environmental psychology, personality dynamics, as well as the person-environment transactions literature, and highlight practical implications for a world in which the places people visit can be easily captured via GPS sensors. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

    View details for DOI 10.1037/pspp0000297

    View details for Web of Science ID 000668228800010

    View details for PubMedID 32496085

  • Who uses what and how often?: Personality predictors of multiplatform social media use among young adults JOURNAL OF RESEARCH IN PERSONALITY Vaid, S. S., Harari, G. M. 2021; 91
  • Personality Sensing for Theory Development and Assessment in the Digital Age EUROPEAN JOURNAL OF PERSONALITY Harari, G. M., Vaid, S. S., Mueller, S. R., Stachl, C., Marrero, Z., Schoedel, R., Buehner, M., Gosling, S. D. 2020

    View details for DOI 10.1002/per.2273

    View details for Web of Science ID 000571158800001

  • Investigating the Relationships Between Mobility Behaviours and Indicators of Subjective Well-Being Using Smartphone-Based Experience Sampling and GPS Tracking EUROPEAN JOURNAL OF PERSONALITY Mueller, S. R., Peters, H., Matz, S. C., Wang, W., Harari, G. M. 2020; 34 (5): 714–32

    View details for DOI 10.1002/per.2262

    View details for Web of Science ID 000585339000008

  • Predicting personality from patterns of behavior collected with smartphones. Proceedings of the National Academy of Sciences of the United States of America Stachl, C., Au, Q., Schoedel, R., Gosling, S. D., Harari, G. M., Buschek, D., Volkel, S. T., Schuwerk, T., Oldemeier, M., Ullmann, T., Hussmann, H., Bischl, B., Buhner, M. 2020

    Abstract

    Smartphones enjoy high adoption rates around the globe. Rarely more than an arm's length away, these sensor-rich devices can easily be repurposed to collect rich and extensive records of their users' behaviors (e.g., location, communication, media consumption), posing serious threats to individual privacy. Here we examine the extent to which individuals' Big Five personality dimensions can be predicted on the basis of six different classes of behavioral information collected via sensor and log data harvested from smartphones. Taking a machine-learning approach, we predict personality at broad domain ([Formula: see text] = 0.37) and narrow facet levels ([Formula: see text] = 0.40) based on behavioral data collected from 624 volunteers over 30 consecutive days (25,347,089 logging events). Our cross-validated results reveal that specific patterns in behaviors in the domains of 1) communication and social behavior, 2) music consumption, 3) app usage, 4) mobility, 5) overall phone activity, and 6) day- and night-time activity are distinctively predictive of the Big Five personality traits. The accuracy of these predictions is similar to that found for predictions based on digital footprints from social media platforms and demonstrates the possibility of obtaining information about individuals' private traits from behavioral patterns passively collected from their smartphones. Overall, our results point to both the benefits (e.g., in research settings) and dangers (e.g., privacy implications, psychological targeting) presented by the widespread collection and modeling of behavioral data obtained from smartphones.

    View details for DOI 10.1073/pnas.1920484117

    View details for PubMedID 32665436

  • Sensing Sociability: Individual Differences in Young Adults' Conversation, Calling, Texting, and App Use Behaviors in Daily Life JOURNAL OF PERSONALITY AND SOCIAL PSYCHOLOGY Harari, G. M., Mueller, S. R., Stachl, C., Wang, R., Wang, W., Buehner, M., Rentfrow, P. J., Campbell, A. T., Gosling, S. D. 2020; 119 (1): 204–28

    Abstract

    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 social behavior tendencies is the broad number of channels over which socializing can occur, both in-person and through digital media. To examine individual differences in everyday social behavior patterns, here we used smartphone-based mobile sensing methods (MSMs) in four studies (total N = 926) to collect real-world data about young adults' social behaviors across four communication channels: conversations, phone calls, text messages, and use of messaging and social media applications. 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 to which young adults engaged in social behaviors and mapping these behaviors onto 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. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

    View details for DOI 10.1037/pspp0000245

    View details for Web of Science ID 000543795500010

    View details for PubMedID 31107054

  • A process-oriented approach to respecting privacy in the context of mobile phone tracking. Current opinion in psychology Harari, G. M. 2019; 31: 141–47

    Abstract

    Mobile phone tracking poses challenges to individual privacy because a phone's sensor data and metadata logs can reveal behavioral, contextual, and psychological information about the individual who uses the phone. Here, I argue for a process-oriented approach to respecting individual privacy in the context of mobile phone tracking by treating informed consent as a process, not a mouse click. This process-oriented approach allows individuals to exercise their privacy preferences and requires the design of self-tracking systems that facilitate transparency, opt-in default settings, and individual control over personal data, especially with regard to: (1) what kinds of personal data are being collected and (2) how the data are being used and shared. In sum, I argue for the development of self-tracking systems that put individual user privacy and control at their core, while enabling people to harness their personal data for self-insight and behavior change. This approach to mobile phone privacy is a radical departure from current standard data practices and has implications for a wide range of stakeholders, including individual users, researchers, and corporations.

    View details for DOI 10.1016/j.copsyc.2019.09.007

    View details for PubMedID 31693976

  • Smartphone sensing methods for studying behavior in everyday life CURRENT OPINION IN BEHAVIORAL SCIENCES Harari, G. M., Mueller, S. R., Aung, M. H., Rentfrow, P. J. 2017; 18: 83–90
  • Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts. GigaScience Wu, C., Fritz, H., Bastami, S., Maestre, J. P., Thomaz, E., Julien, C., Castelli, D. M., de Barbaro, K., Bearman, S. K., Harari, G. M., Cameron Craddock, R., Kinney, K. A., Gosling, S. D., Schnyer, D. M., Nagy, Z. 2021; 10 (6)

    Abstract

    BACKGROUND: As mobile technologies become ever more sensor-rich, portable, and ubiquitous, data captured by smart devices are lending rich insights into users' daily lives with unprecedented comprehensiveness and ecological validity. A number of human-subject studies have been conducted to examine the use of mobile sensing to uncover individual behavioral patterns and health outcomes, yet minimal attention has been placed on measuring living environments together with other human-centered sensing data. Moreover, the participant sample size in most existing studies falls well below a few hundred, leaving questions open about the reliability of findings on the relations between mobile sensing signals and human outcomes.RESULTS: To address these limitations, we developed a home environment sensor kit for continuous indoor air quality tracking and deployed it in conjunction with smartphones, Fitbits, and ecological momentary assessments in a cohort study of up to 1,584 college student participants per data type for 3 weeks. We propose a conceptual framework that systematically organizes human-centric data modalities by their temporal coverage and spatial freedom. Then we report our study procedure, technologies and methods deployed, and descriptive statistics of the collected data that reflect the participants' mood, sleep, behavior, and living environment.CONCLUSIONS: We were able to collect from a large participant cohort satisfactorily complete multi-modal sensing and survey data in terms of both data continuity and participant adherence. Our novel data and conceptual development provide important guidance for data collection and hypothesis generation in future human-centered sensing studies.

    View details for DOI 10.1093/gigascience/giab044

    View details for PubMedID 34155505

  • Personality Research and Assessment in the Era of Machine Learning EUROPEAN JOURNAL OF PERSONALITY Stachl, C., Pargent, F., Hilbert, S., Harari, G. M., Schoedel, R., Vaid, S., Gosling, S. D., Buehner, M. 2020

    View details for DOI 10.1002/per.2257

    View details for Web of Science ID 000535945200001

  • Social Sensing: Assessing Social Functioning of Patients Living with Schizophrenia using Mobile Phone Sensing Wang, W., Mirjafari, S., Harari, G., Ben-Zeev, D., Brian, R., Choudhury, T., Hauser, M., Kane, J., Masaba, K., Nepal, S., Sano, A., Scherer, E., Tseng, V., Wang, R., Wen, H., Wu, J., Campbell, A., ACM ASSOC COMPUTING MACHINERY. 2020
  • Personality trait predictors and mental well-being correlates of exercise frequency across the academic semester. Social science & medicine (1982) Kroencke, L. n., Harari, G. M., Katana, M. n., Gosling, S. D. 2019; 236: 112400

    Abstract

    Regular exercise is frequently recommended as a means of combating the negative effects of stress on mental health. But, among college students, exercise frequency remains below recommended levels.To better understand exercising behaviors in college students, we examined how exercise patterns change across an academic semester and how these changes relate to personality traits and mental well-being.We conducted two longitudinal experience sampling studies, using data from four cohorts of students, spanning four semesters (Fall 2015 - Spring 2017). In Study 1, a large sample of United States college students (cohort 1; N = 1126) reported the number of days they exercised and their levels of happiness, stress, sadness, and anxiety each week over the course of one academic semester (13 weeks). Study 2 (cohorts 2-4; N = 1973) was conducted to replicate our exploratory results from Study 1.Using latent growth curve modeling, we observed the same normative pattern of change across both studies: The average student exercised twice during the first week of the semester and showed consistent decreases in exercise frequency in following weeks. Across both studies, higher initial levels of exercise frequency at the start of the semester were consistently related to higher extraversion, higher conscientiousness, and lower neuroticism. Furthermore, exercise frequency and mental well-being fluctuated together after controlling for time trends in the data: In weeks during which students exercised more than predicted, they also reported being happier and less anxious.We contextualize the findings with regard to past research and discuss how they can be applied in behavior change interventions to promote students' well-being.

    View details for DOI 10.1016/j.socscimed.2019.112400

    View details for PubMedID 31336217

  • Smartphones in Personal Informatics: A Framework for Self-Tracking Research with Mobile Sensing DIGITAL PHENOTYPING AND MOBILE SENSING: NEW DEVELOPMENTS IN PSYCHOINFORMATICS Vaid, S. S., Harari, G. M., Baumeister, H., Montag, C. 2019: 65–92
  • Inference of Big-Five Personality Using Large-scale Networked Mobile and Appliance Data Tong, C., Harari, G. M., Chieh, A., Bellahsen, O., Vegreville, M., Roitmann, E., Lane, N. D., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 530
  • An Evaluation of Students' Interest in and Compliance With Self-Tracking Methods: Recommendations for Incentives Based on Three Smartphone Sensing Studies SOCIAL PSYCHOLOGICAL AND PERSONALITY SCIENCE Harari, G. M., Mueller, S. R., Mishra, V., Wang, R., Campbell, A. T., Rentfrow, P. J., Gosling, S. D. 2017; 8 (5): 479–92
  • Using Human Raters to Characterize the Psychological Characteristics of GPS-based Places Muller, S. R., Matz, S., Mascolo, C., Harari, G. M., Khambatta, P., Gosling, S. D., Mehrotra, A., Musolesi, M., Rentfrow, P. J., ACM ASSOC COMPUTING MACHINERY. 2017: 157–60
  • Participants' Compliance and Experiences with Self-Tracking Using a Smartphone Sensing App Harari, G. M., Wang, R., Wang, W., Campbell, A. T., Mueller, S. R., ACM ASSOC COMPUTING MACHINERY. 2017: 57–60