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Olivia Nemes Nemeth

dblp:337/9444 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0000-8764-8413ORCID · reported

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

Human-computer interaction and ubiquitous computing · 1 · 1 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% Human-AI interaction · 44% Design research and methods · 13%

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

TopicWeightPapersLastEvidence papers
Human-AI interaction
recommender system
0.812024
Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations · CHI 2024
Collaborative and social computing
social media
0.812024
Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations · CHI 2024

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

quantitative analysis · 0.8data donation · 0.8
YearPublicationVenuePosition
2024 Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations
abstract
Short-format videos have exploded on platforms like TikTok, Instagram, and YouTube. Despite this, the research community lacks large-scale empirical studies into how people engage with short-format videos and the role of recommendation systems that offer endless streams of such content. In this work, we analyze user engagement on TikTok using data we collect via a data donation system that allows TikTok users to donate their data. We recruited 347 TikTok users and collected 9.2M TikTok video recommendations they received. By analyzing user engagement, we find that the average daily usage time increases over the users’ lifetime while the user attention remains stable at around 45%. We also find that users like more videos uploaded by people they follow than those recommended by people they do not follow. Our study offers valuable insights into how users engage with short-format videos on TikTok and lessons learned from designing a data donation system.
Savvas Zannettou, Olivia Nemes Nemeth, Oshrat Ayalon, Angelica Goetzen, Krishna P. Gummadi, Elissa M. Redmiles, Franziska Roesner
CHI2