Scalable Event-Based Clustering of Social Media Via Record Linkage Techniques


  • Timo Reuter CITEC, University of Bielefeld
  • Philipp Cimiano CITEC, University of Bielefeld
  • Lucas Drumond University of Hildesheim
  • Krisztian Buza University of Hildesheim
  • Lars Schmidt-Thieme University of Hildesheim


We tackle the problem of grouping content available in social media applications such as Flickr, Youtube, Panoramino etc. into clusters of documents describing the same event. This task has been referred to as event identification before. We present a new formalization of the event identification task as a record linkage problem and show that this formulation leads to a principled and highly efficient solution to the problem. We present results on two datasets derived from Flickr — and upcoming — comparing the results in terms of Normalized Mutual Information and F-Measure with respect to several baselines, showing that a record linkage approach outperforms all baselines as well as a state-of-the-art system. We demonstrate that our approach can scale to large amounts of data, reducing the processing time considerably compared to a state-of-the-art approach. The scalability is achieved by applying an appropriate blocking strategy and relying on a Single Linkage clustering algorithm which avoids the exhaustive computation of pairwise similarities.




How to Cite

Reuter, T., Cimiano, P., Drumond, L., Buza, K., & Schmidt-Thieme, L. (2021). Scalable Event-Based Clustering of Social Media Via Record Linkage Techniques. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 313-320. Retrieved from