TY - GEN
T1 - FediData
T2 - 34th ACM International Conference on Information and Knowledge Management, CIKM 2025
AU - Gao, Min
AU - Du, Haoran
AU - Wen, Wen
AU - Duan, Qiang
AU - Wang, Xin
AU - Chen, Yang
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/11/10
Y1 - 2025/11/10
N2 - Recently, decentralized online social networks (DOSNs) such as Mastodon have emerged quickly, bringing new opportunities for studies in user behavior modeling and multi-modal learning. However, their decentralized architecture presents two key challenges: 1) Distributed data and inconsistent access strategies across several individual instances make a unified collection difficult; 2) user-generated content (UGC) contains multiple modalities while lacking standard organization and high-quality annotation. To address these issues, we constructed FediData, a comprehensive multi-modal dataset from Mastodon. Our dataset integrates user profiles, text, images, and social interactions. To validate FediData's usefulness, we designed and analyzed several tasks and systematically evaluated the performance of existing state-of-the-art methods. Our analysis reveals the unique challenges of DOSNs and highlights the value of FediData in DOSN-related studies. We believe FediData could serve as a foundational dataset for advancing user behavior analytics, multi-modal learning, and future decentralized web research. All data and documentation are available in a Zenodo repository at https://zenodo.org/records/15621243 (DOI: 10.5281/zenodo.15621243).
AB - Recently, decentralized online social networks (DOSNs) such as Mastodon have emerged quickly, bringing new opportunities for studies in user behavior modeling and multi-modal learning. However, their decentralized architecture presents two key challenges: 1) Distributed data and inconsistent access strategies across several individual instances make a unified collection difficult; 2) user-generated content (UGC) contains multiple modalities while lacking standard organization and high-quality annotation. To address these issues, we constructed FediData, a comprehensive multi-modal dataset from Mastodon. Our dataset integrates user profiles, text, images, and social interactions. To validate FediData's usefulness, we designed and analyzed several tasks and systematically evaluated the performance of existing state-of-the-art methods. Our analysis reveals the unique challenges of DOSNs and highlights the value of FediData in DOSN-related studies. We believe FediData could serve as a foundational dataset for advancing user behavior analytics, multi-modal learning, and future decentralized web research. All data and documentation are available in a Zenodo repository at https://zenodo.org/records/15621243 (DOI: 10.5281/zenodo.15621243).
UR - https://www.scopus.com/pages/publications/105023184575
UR - https://www.scopus.com/pages/publications/105023184575#tab=citedBy
U2 - 10.1145/3746252.3761634
DO - 10.1145/3746252.3761634
M3 - Conference contribution
AN - SCOPUS:105023184575
T3 - CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management
SP - 6372
EP - 6376
BT - CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery, Inc
Y2 - 10 November 2025 through 14 November 2025
ER -