arXiv subjects

Yannis Theocharis

Publications and source records attributed to Yannis Theocharis.

2 recordsLinked to original sources

arXiv2026

PoliTok-DE: A Multimodal Dataset of Political TikToks and Deletions From Germany

We present PoliTok-DE, a large-scale multimodal dataset (video, audio, images, text) of TikTok posts from two German elections: the 2024 Saxony state election and the 2025 German federal election. The corpus contains over 930,000 posts, of which over 330,000 were later deleted from the platform (18.7% of Saxony posts, 39.7% of federal posts). In the federal-election collection, about two thirds of the deletions were creator withdrawals, and the platform-deletion rate we computed was 13.0% of all posts, more than an order of magnitude (14-19x) above the platform-wide rate TikTok reported. Posts were identified via the TikTok research API and complemented with web scraping to retrieve full multimodal media and metadata. PoliTok-DE supports social science research across substantive and methodological agendas: substantive work on intolerance and political communication, and methodological work on platform policies around deleted content and qualitative-quantitative multimodal research. To illustrate, we report a case study on intolerance and entertainment in an annotated subset of deleted posts: about one in five posts conveyed intolerance and a majority conveyed humor.

cs.SI↗

arXiv2021

Auditing the Biases Enacted by YouTube for Political Topics in Germany

With YouTube's growing importance as a news platform, its recommendation system came under increased scrutiny. Recognizing YouTube's recommendation system as a broadcaster of media, we explore the applicability of laws that require broadcasters to give important political, ideological, and social groups adequate opportunity to express themselves in the broadcasted program of the service. We present audits as an important tool to enforce such laws and to ensure that a system operates in the public's interest. To examine whether YouTube is enacting certain biases, we collected video recommendations about political topics by following chains of ten recommendations per video. Our findings suggest that YouTube's recommendation system is enacting important biases. We find that YouTube is recommending increasingly popular but topically unrelated videos. The sadness evoked by the recommended videos decreases while the happiness increases. We discuss the strong popularity bias we identified and analyze the link between the popularity of content and emotions. We also discuss how audits empower researchers and civic hackers to monitor complex machine learning (ML)-based systems like YouTube's recommendation system.

cs.HC↗