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Stevie Chancellor

McKnight Land Grant Professor

University of Minnesota

United States

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Stevie Chancellor is an Assistant Professor at the University of Minnesota, USA. Their research primarily focuses on the intersection of mental health and technology, exploring topics such as digital mental health data, machine learning in mental illness prediction, and the effects of online interaction on workplace bias. Recent publications include investigations into consent-forward paradigms and predictive techniques related to mental health on social media.

Articles (27)

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Collaborators (10)

Jayhyun Chang

Northwestern University

UNITED STATES

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Kenneth Holstein

Carnegie Mellon University

UNITED STATES

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Dan Adler

Assistant Professor

University of Michigan

UNITED STATES

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Zhiwei Steven Wu

Carnegie Mellon University

UNITED STATES

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Emily Tseng

Assistant Professor

University of Washington

UNITED STATES

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C. Estelle Smith

Assistant Professor

Colorado School of Mines

UNITED STATES

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Elaine Nsoesie

UNITED STATES

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Seraphina Yong

University of Minnesota

UNITED STATES

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Nick Haber

Stanford University

UNITED STATES

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Nicholas Vincent

ASsistant Professor

Simon Fraser University

CANADA

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Stevie Chancellor

Research Interests

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About

Articles (27)

Social and Emotional Uses of AI

Retheorizing Context Collapse for Social Media

Characterizing Delusional Spirals through Human-LLM Chat Logs

Online Communities as Collaborative Testbeds for Collective Alignment

AI CHAOS! 2nd Workshop on the Challenges for Human Oversight of AI Systems

Beyond Content Exposure: Systemic Factors Driving Moderators' Mental Health Crisis in Africa

Opportunities and Barriers for AI Feedback on Meeting Inclusion in Socioorganizational Teams

Unraveling Entangled Feeds: Rethinking Social Media Design to Enhance User Well-being

Large Language Models for Mental Health

'I'm Petting the Laptop, Which Has You Inside It': Reflecting on Lived Experiences of Online Friendship

Beyond the Individual: A Community-Engaged Framework for Ethical Online Community Research

Context Matters: Ethical Challenges in Research with Online Communities

Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks

Wisdom of the Crowd, Without the Crowd: A Socratic LLM for Asynchronous Deliberation on Perspectivist Data

Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers.

Secondary Stakeholders in AI: Fighting for, Brokering, and Navigating Agency

Observe, Ask, Intervene: Designing AI Agents for More Inclusive Meetings

Advancing a consent-forward paradigm for digital mental health data

"All of the White People Went First": How Video Conferencing Consolidates Control and Exacerbates Workplace Bias

Toward Practices for Human-Centered Machine Learning

Contextual Gaps in Machine Learning for Mental Illness Prediction: The Case of Diagnostic Disclosures

Peer Produced Friction: How Page Protection on Wikipedia Affects Editor Engagement and Concentration

Metaphors in moderation

Suicide risk and protective factors in online support forum posts

"The Smartest Decision for My Future"

Methods in predictive techniques for mental health status on social media

Beyond Parental Control

Collaborators (10)

Jayhyun Chang

Kenneth Holstein

Dan Adler

Zhiwei Steven Wu

Emily Tseng

C. Estelle Smith

Elaine Nsoesie

Seraphina Yong

Nick Haber

Nicholas Vincent

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