most cited"Sharing, Not Showing Off": How BeReal Approaches Authentic Self-Presentation on Social Media Through Its Design

14 citations · 19 across the 8 of their papers we have counts for

collaborators

8 papers

cs.HC2025

Challenges and Opportunities for Participatory Design of Conversational Agents for Young People's Wellbeing

Natalia Kucirkova, Alexis Hiniker, Megumi Ishikawa +3

This paper outlines the challenges and opportunities of research on conversational agents with children and young people across four countries, exploring the ways AI technologies c…

cs.CY2025

Understanding Privacy Norms Around LLM-Based Chatbots: A Contextual Integrity Perspective

Sarah Tran, Hongfan Lu, Isaac Slaughter +9

LLM-driven chatbots like ChatGPT have created large volumes of conversational data, but little is known about how user privacy expectations are evolving with this technology. We co…

cs.HC20241 cited

The Engagement-Prolonging Designs Teens Encounter on Very Large Online Platforms

Yixin Chen, Yue Fu, Zeya Chen +2

In the attention economy, online platforms are incentivized to design products that maximize user engagement, even when such practices conflict with users' best interests. We condu…

cs.HC20244 cited

Creativity in the Age of AI: Evaluating the Impact of Generative AI on Design Outputs and Designers' Creative Thinking

Yue Fu, Han Bin, Tony Zhou +5

As generative AI (GenAI) increasingly permeates design workflows, its impact on design outcomes and designers' creative capabilities warrants investigation. We conducted a within-s…

cs.HC202414 cited

"Sharing, Not Showing Off": How BeReal Approaches Authentic Self-Presentation on Social Media Through Its Design

JaeWon Kim, Robert Wolfe, Ishita Chordia +2

Adolescents are particularly vulnerable to the pressures created by social media, such as heightened self-consciousness and the need for extensive self-presentation. In this study,…

cs.CL2024

ML-EAT: A Multilevel Embedding Association Test for Interpretable and Transparent Social Science

Robert Wolfe, Alexis Hiniker, Bill Howe

This research introduces the Multilevel Embedding Association Test (ML-EAT), a method designed for interpretable and transparent measurement of intrinsic bias in language technolog…