Jake M Hofman

Biographic Data

ID4974038
NAMEJake M Hofman
GIVEN NAMESJake M
FAMILY NAMEHofman
SIGNATUREHOFMAN J M
AFFILIATIONSMicrosoft (United States)
ORCID0000-0002-9364-9604
VERIFIEDYes
TOTAL WORKS7
TOTAL CITATIONS0
WORKS AS AUTHOR7
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2010
LATEST PUBLICATION YEAR2026
H-INDEX0

  • Effects of LLM use and note-taking on reading comprehension and memory

    Open AccessPia Kreijkes, Viktor Kewenig et al.ARTICLEComputers & Education2026

    Students' rapid uptake of Generative Artificial Intelligence tools, particularly large language models (LLMs), raises urgent questions about their effects on learning. We compared the impact of LLM use to that of traditional note-taking, or a combination of both, on secondary school students' reading comprehension and retention. We conducted a pre-registered, randomised controlled experiment with within- and between-participant design elements in…

  • Integrating explanation and prediction in computational social science

    Open AccessJake M Hofman, Duncan J Watts et al.ARTICLENature2021

  • Prediction and explanation in social systems

    Open AccessJake M Hofman, Amit Sharma et al.ARTICLEScience2017

    Historically, social scientists have sought out explanations of human and social phenomena that provide interpretable causal mechanisms, while often ignoring their predictive accuracy. We argue that the increasingly computational nature of social science is beginning to reverse this traditional bias against prediction; however, it has also highlighted three important issues that require resolution. First, current practices for evaluating predicti…

  • The Structural Virality of Online Diffusion

    Open AccessSharad Goel, Ashton Anderson et al.ARTICLEManagement Science2016

    Viral products and ideas are intuitively understood to grow through a person-to-person diffusion process analogous to the spread of an infectious disease; however, until recently it has been prohibitively difficult to directly observe purportedly viral events, and thus to rigorously quantify or characterize their structural properties. Here we propose a formal measure of what we label “structural virality” that interpolates between two conceptual…

  • Who says what to whom on twitter

    Open AccessShaomei Wu, Jake M Hofman et al.CONFERENCEProceedings of the 20th…2011

    We study several longstanding questions in media communications research, in the context of the microblogging service Twitter, regarding the production, flow, and consumption of information. To do so, we exploit a recently introduced feature of Twitter known as "lists" to distinguish between elite users - by which we mean celebrities, bloggers, and representatives of media outlets and other formal organizations - and ordinary users. Based on this…

  • Everyone's an influencer

    Open AccessEytan Bakshy, Jake M Hofman et al.CONFERENCEProceedings of the fourth ACM…2011

    In this paper we investigate the attributes and relative influence of 1.6M Twitter users by tracking 74 million diffusion events that took place on the Twitter follower graph over a two month interval in 2009. Unsurprisingly, we find that the largest cascades tend to be generated by users who have been influential in the past and who have a large number of followers. We also find that URLs that were rated more interesting and/or elicited more pos…

  • Predicting consumer behavior with Web search

    Open AccessSharad Goel, Jake M Hofman et al.ARTICLEProceedings of the National…2010

    Recent work has demonstrated that Web search volume can “predict the present,” meaning that it can be used to accurately track outcomes such as unemployment levels, auto and home sales, and disease prevalence in near real time. Here we show that what consumers are searching for online can also predict their collective future behavior days or even weeks in advance. Specifically we use search query volume to forecast the opening weekend box-office …