MK
M.E. Kuipers
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
2 records found
1
Labeling AI-generated Content on Short-Form Video Platforms
A Cross-Platform Analysis of YouTube Shorts and TikTok
This thesis examines how AI-generated content labels are applied on two major short-form video platforms: TikTok and YouTube Shorts. As generative AI becomes more integrated into social media, platforms have introduced disclosure labels to support transparency and help users distinguish between real and synthetic content. However, little is known about how these labels are implemented in practice, whether they align with platform policies, and how they affect user engagement.
Using a dataset of 12,315 videos collected through hybrid scraping and API-based methods, this study compares labeling prevalence, source attribution, policy alignment, and potential effects on user metrics such as likes, comments, and views. The findings reveal notable differences: TikTok relies primarily on creator-applied labels with minimal platform enforcement, while YouTube Shorts applies more labels but does not disclose their source. In both cases, labeling practices are often inconsistent and not fully aligned with platform-specific guidelines or the technical capabilities of C2PA, the standard used for AI content detection.
Although some statistically significant differences in engagement were observed, especially on TikTok, the overall effect of labeling on user behavior was limited. The findings highlight key challenges in current AI labeling practices and point to a growing need for more consistent, transparent, and accountable moderation policies. While regulatory frameworks such as the EU’s AI Act and Digital Services Act aim to improve transparency in digital environments, this study shows that current platform practices often fall short of these goals, especially in terms of visibility, attribution, and enforcement of AI labels. ...
Using a dataset of 12,315 videos collected through hybrid scraping and API-based methods, this study compares labeling prevalence, source attribution, policy alignment, and potential effects on user metrics such as likes, comments, and views. The findings reveal notable differences: TikTok relies primarily on creator-applied labels with minimal platform enforcement, while YouTube Shorts applies more labels but does not disclose their source. In both cases, labeling practices are often inconsistent and not fully aligned with platform-specific guidelines or the technical capabilities of C2PA, the standard used for AI content detection.
Although some statistically significant differences in engagement were observed, especially on TikTok, the overall effect of labeling on user behavior was limited. The findings highlight key challenges in current AI labeling practices and point to a growing need for more consistent, transparent, and accountable moderation policies. While regulatory frameworks such as the EU’s AI Act and Digital Services Act aim to improve transparency in digital environments, this study shows that current platform practices often fall short of these goals, especially in terms of visibility, attribution, and enforcement of AI labels. ...
This thesis examines how AI-generated content labels are applied on two major short-form video platforms: TikTok and YouTube Shorts. As generative AI becomes more integrated into social media, platforms have introduced disclosure labels to support transparency and help users distinguish between real and synthetic content. However, little is known about how these labels are implemented in practice, whether they align with platform policies, and how they affect user engagement.
Using a dataset of 12,315 videos collected through hybrid scraping and API-based methods, this study compares labeling prevalence, source attribution, policy alignment, and potential effects on user metrics such as likes, comments, and views. The findings reveal notable differences: TikTok relies primarily on creator-applied labels with minimal platform enforcement, while YouTube Shorts applies more labels but does not disclose their source. In both cases, labeling practices are often inconsistent and not fully aligned with platform-specific guidelines or the technical capabilities of C2PA, the standard used for AI content detection.
Although some statistically significant differences in engagement were observed, especially on TikTok, the overall effect of labeling on user behavior was limited. The findings highlight key challenges in current AI labeling practices and point to a growing need for more consistent, transparent, and accountable moderation policies. While regulatory frameworks such as the EU’s AI Act and Digital Services Act aim to improve transparency in digital environments, this study shows that current platform practices often fall short of these goals, especially in terms of visibility, attribution, and enforcement of AI labels.
Using a dataset of 12,315 videos collected through hybrid scraping and API-based methods, this study compares labeling prevalence, source attribution, policy alignment, and potential effects on user metrics such as likes, comments, and views. The findings reveal notable differences: TikTok relies primarily on creator-applied labels with minimal platform enforcement, while YouTube Shorts applies more labels but does not disclose their source. In both cases, labeling practices are often inconsistent and not fully aligned with platform-specific guidelines or the technical capabilities of C2PA, the standard used for AI content detection.
Although some statistically significant differences in engagement were observed, especially on TikTok, the overall effect of labeling on user behavior was limited. The findings highlight key challenges in current AI labeling practices and point to a growing need for more consistent, transparent, and accountable moderation policies. While regulatory frameworks such as the EU’s AI Act and Digital Services Act aim to improve transparency in digital environments, this study shows that current platform practices often fall short of these goals, especially in terms of visibility, attribution, and enforcement of AI labels.
Circular City Index
MDP Project Barcelona
Student report
(2024)
-
M.E. Kuipers, C.F. Martens, D.A.H. Bozon, T.V.F. van Beeck, J.J. Hemmes, M. Kroesen, José A. Á. Antolínez
The Circular City Index (CCI) project for Barcelona, conducted in partnership with the Barcelona Supercomputing Center, aimed to assess urban circularity across multiple districts. Utilizing the City Circularity Index model as introduced by Muscillo et al. (2021), this research adapted the framework to Barcelona’s unique urban context, focusing on the four main components: Digitalization (D), Energy, Climate and Resources (ECR), Mobility (M), and Waste (W). The project benchmarks city performance against Sustainable Development Goals (SDGs) to guide strategic planning for a sustainable and resilient urban environment. Each district’s performance was measured through tailored Key Performance Indicators (KPIs) reflecting accessibility, infrastructure, and environmental data. Spatial and statistical analyses were applied to assess parameters such as waste collection accessibility, air quality, and renewable energy coverage. Data was gathered from open-source and municipal datasets, complemented by geographic analysis for a precise district-level understanding...
...
The Circular City Index (CCI) project for Barcelona, conducted in partnership with the Barcelona Supercomputing Center, aimed to assess urban circularity across multiple districts. Utilizing the City Circularity Index model as introduced by Muscillo et al. (2021), this research adapted the framework to Barcelona’s unique urban context, focusing on the four main components: Digitalization (D), Energy, Climate and Resources (ECR), Mobility (M), and Waste (W). The project benchmarks city performance against Sustainable Development Goals (SDGs) to guide strategic planning for a sustainable and resilient urban environment. Each district’s performance was measured through tailored Key Performance Indicators (KPIs) reflecting accessibility, infrastructure, and environmental data. Spatial and statistical analyses were applied to assess parameters such as waste collection accessibility, air quality, and renewable energy coverage. Data was gathered from open-source and municipal datasets, complemented by geographic analysis for a precise district-level understanding...