Revisiting Algorithmic Audits of TikTok: Poor Reproducibility and Short-term Validity of Findings.

Matej Mosnar|Adam Skurla|Branislav Pecher|Matus Tibensky|Jan Jakubcik|Adrian Bindas|Peter Sakalik|Ivan Srba


Anthology ID:DBLP:conf/sigir/MosnarSPTJBSS25
Volume:Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, Padua, Italy, July 13-18, 2025
Year:2025
Venue:Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
Publisher:ACM
Pages:3357-3366
URL:https://doi.org/10.1145/3726302.3730293
DOI:https://doi.org/10.1145/3726302.3730293
DBLP:conf/sigir/MosnarSPTJBSS25
BibTeX:
@inproceedings{mosnar-2025-revisiting, author = {Matej Mosnar and Adam Skurla and Branislav Pecher and Matus Tibensky and Jan Jakubcik and Adrian Bindas and Peter Sakalik and Ivan Srba}, editor = {Nicola Ferro and Maria Maistro and Gabriella Pasi and Omar Alonso and Andrew Trotman and Suzan Verberne}, title = {{Revisiting Algorithmic Audits of TikTok: Poor Reproducibility and Short-term Validity of Findings}}, booktitle = {{Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, Padua, Italy, July 13-18, 2025}}, pages = {3357--3366}, publisher = {ACM}, year = {2025}, url = {https://doi.org/10.1145/3726302.3730293}, doi = {https://doi.org/10.1145/3726302.3730293} }