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Cheryl Siy

dblp:415/5615 · DBLP profile ↗
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3ranked-venue papers
3as first author
3since 2021 · last 2026
0009-0001-6453-1287ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 3 · 3 first-author · 3 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Collaborative and social computing · 44% Health and well-being technologies · 44% Human-AI interaction · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Collaborative and social computing › social media
short-form video platforms
1.012026
Endless Swipes and Recommendations: The Impact of Short-Form Video Platforms on Context-Switching and Children's Working Memory · CHI 2026
Human-AI interaction
recommender system
0.312026
Endless Swipes and Recommendations: The Impact of Short-Form Video Platforms on Context-Switching and Children's Working Memory · CHI 2026

Methods — techniques the papers use, named apart from their topics

factorial design · 1.0between-subjects experiment · 1.0
YearPublicationVenuePosition
2026 Endless Swipes and Recommendations: The Impact of Short-Form Video Platforms on Context-Switching and Children's Working Memory
abstract
Short-form video (SFV) platforms are increasingly popular, yet the rapid context switching and their potential effects on children’s cognitive functions are not well understood. In this work, we conducted a between-subjects experiment (N = 180) to examine how YouTube Shorts affects young children’s short-term memory (STM) and working memory (WM), measured using the forward and backward digit span tasks. The study focused on two core platform features of SFV: the easy-to-use swipe interface and the recommendation system. Using a 2 × 2 factorial design, we compared four SFV group conditions that varied by interaction mode and content source, complemented by two long-form video baseline conditions (one with constant context switching and one without). Our results show that the feature combinations and the baseline comparisons were not associated with changes in STM or WM. However, swipe interaction increased video switching, while recommendation-based content increased category switching. The higher combined levels of video and category switching across participants were associated with marginal effects on working memory performance, while STM remained unaffected.
Cheryl Siy, Kihoon Jung, Seokwoo Song, Kwan Hong Lee, John Kim 0001
CHI1
2025 E-litter: Nudging Email Usage Behavior One Byte at a Time MHCI007
abstract
The ubiquity of technology and the “zero-cost” nature of cloud services result in users overlooking the environmental impact of their online usage. While some cloud services (e.g., LLM inference) have significantly higher environmental impact, this work focuses on emails, a service widely used yet representing one of the smallest usages of cloud resources. Just as recycling a piece of paper may not have a great impact but can contribute to environmental awareness, deleting emails symbolizes a small yet meaningful behavior change - a ’gesture’ toward environmental responsibility. In this work, we propose E-litter, a mobile system combining a bulk-delete UI with eco-feedback to encourage email deletion. By representing email storage in tangible terms (e.g., sheets of paper), E-litter nudges users to become more aware of cloud usage. A user study with Gmail users shows that E-litter significantly increases email deletion, highlighting the potential of UI and eco-feedback in impacting cloud usage behavior.
Cheryl Siy, Gihong Do, Kihoon Jung, Kwan Hong Lee, John Kim 0001
Proc. ACM Hum. Comput. Interact.1
2025 One MBTI does not Fit All: Perceptions and Usage of MBTI in Social Media Profiles
abstract
The Myers-Briggs Type Indicator (MBTI) is a widely used personality assessment tool that groups individuals into 16 types (based on 4 dimensions). The popularity of MBTI and the availability of online MBTI assessments have led to the increasing usage of MBTI, including sharing and displaying their MBTI types as part of their online identity. This study investigates the trend of combining social media and personal assessment tools such as MBTI by exploring how people interpret their MBTI and how they form impressions and interact with others based on others' MBTI labels. Through a thematic analysis of posts from the subreddit r/mbti and a two-part online survey, people use MBTI for self-reflection, validation, and personal development, while often overestimating their type. The 4-letter MBTI also helps in understanding interpersonal relationships, but can reinforce stereotypes in first impressions. Based on these findings, we show how ''one MBTI (type) does not fit all'' and explore how modified MBTI stickers (with percentage information) can help reduce bias on social media profiles.
Cheryl Siy, Yuanxin Pang, Kihoon Jung, John Kim 0001
Proc. ACM Hum. Comput. Interact.1