@misc{AlhosseiniAlmodarresiYasinBinTareafNajafietal.2019,
  author    = {Alhosseini Almodarresi Yasin, Seyed Ali and Bin Tareaf, Raad and Najafi, Pejman and Meinel, Christoph},
  title     = {Detect me if you can},
  series = {Companion Proceedings of The 2019 World Wide Web Conference},
  journal   = {Companion Proceedings of The 2019 World Wide Web Conference},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  isbn      = {978-1-4503-6675-5},
  doi       = {10.1145/3308560.3316504},
  pages     = {148 -- 153},
  year      = {2019},
  abstract  = {Spam Bots have become a threat to online social networks with their malicious behavior, posting misinformation messages and influencing online platforms to fulfill their motives. As spam bots have become more advanced over time, creating algorithms to identify bots remains an open challenge. Learning low-dimensional embeddings for nodes in graph structured data has proven to be useful in various domains. In this paper, we propose a model based on graph convolutional neural networks (GCNN) for spam bot detection. Our hypothesis is that to better detect spam bots, in addition to defining a features set, the social graph must also be taken into consideration. GCNNs are able to leverage both the features of a node and aggregate the features of a node's neighborhood. We compare our approach, with two methods that work solely on a features set and on the structure of the graph. To our knowledge, this work is the first attempt of using graph convolutional neural networks in spam bot detection.},
  language  = {en}
}