@inproceedings{46319,
  abstract     = {{The detection of orchestrated and potentially manipulative campaigns in social media is far more meaningful than an- alyzing single account behaviour but also more challenging in terms of pattern recognition, data processing, and com- putational complexity. While supervised learning methods need an enormous amount of reliable ground truth data to find rather inflexible patterns, classical unsupervised learn- ing techniques need a lot of computational power to handle large amount of data. This makes them infeasible for real- time analysis. In this work, we demonstrate the applicability of text stream clustering for the real-time detection of coordi- nated campaigns.}},
  author       = {{Assenmacher, D and Adam, L and Trautmann, Heike and Grimme, C}},
  booktitle    = {{Proceedings of the Florida Artificial Intelligence Research Society Conference}},
  title        = {{{Towards Real-Time and Unsupervised Campaign Detection in Social Media}}},
  year         = {{2020}},
}