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20242026
most citedLLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

1 citations · 1 across the 7 of their papers we have counts for

collaborators

8 papers

cs.HC2026

Reconceptualizing Age Assurance as a Sociotechnical Problem: Connecting Evidence, Evaluation, Claims, and Decisions

Renkai Ma, Prakriti Dumaru, Thomas Synaepa-Addison +2

Age verification is often treated as a technical problem: can a system determine a child's age accurately? We argue this framing is too narrow. Age assurance becomes consequential…

cs.HC2026

Participation and Power: A Case Study of Using Ecological Momentary Assessment to Engage Adolescents in Academic Research

Ozioma C. Oguine, Elmira Rashidi, Pamela J. Wisniewski +1

Ecological Momentary Assessment (EMA) is widely used to study adolescents' experiences; yet, how the design of EMA platforms shapes engagement, research practices, and power dynami…

cs.CY2026

PRISM: Evaluating a Rule-Based, Scenario-Driven Social Media Privacy Education Program for Young Autistic Adults

Kirsten Chapman, Garrett Smith, Kaitlyn Klabacka +5

Young autistic adults may garner benefits through social media but also disproportionately experience privacy harms. Prior research found that these harms often stem from perceivin…

cs.HC2026

From "Fail Fast" to "Mature Safely:" Expert Perspectives as Secondary Stakeholders on Teen-Centered Social Media Risk Detection

Renkai Ma, Ashwaq Alsoubai, Jinkyung Katie Park +1

In addressing various risks on social media, the HCI community has advocated for teen-centered risk detection technologies over platform-based, parent-centered features. However, t…

cs.HC2025

Analyzing Social Media Claims regarding Youth Online Safety Features to Identify Problem Areas and Communication Gaps

Renkai Ma, Dominique Geissler, Stefan Feuerriegel +3

Social media platforms have faced increasing scrutiny over whether and how they protect youth online. While online risks to children have been well-documented by prior research, ho…

cs.CL2025★ 1 cited

LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models

Minqian Liu, Zhiyang Xu, Xinyi Zhang +8

Recent advancements in Large Language Models (LLMs) have enabled them to approach human-level persuasion capabilities. However, such potential also raises concerns about the safety…