@misc{BinTareafBergerHennigetal.2019,
  author    = {Bin Tareaf, Raad and Berger, Philipp and Hennig, Patrick and Meinel, Christoph},
  title     = {Personality exploration system for online social networks},
  series = {2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)},
  journal   = {2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-5386-7325-6},
  doi       = {10.1109/WI.2018.00-76},
  pages     = {301 -- 309},
  year      = {2019},
  abstract  = {User-generated content on social media platforms is a rich source of latent information about individual variables. Crawling and analyzing this content provides a new approach for enterprises to personalize services and put forward product recommendations. In the past few years, brands made a gradual appearance on social media platforms for advertisement, customers support and public relation purposes and by now it became a necessity throughout all branches. This online identity can be represented as a brand personality that reflects how a brand is perceived by its customers. We exploited recent research in text analysis and personality detection to build an automatic brand personality prediction model on top of the (Five-Factor Model) and (Linguistic Inquiry and Word Count) features extracted from publicly available benchmarks. The proposed model reported significant accuracy in predicting specific personality traits form brands. For evaluating our prediction results on actual brands, we crawled the Facebook API for 100k posts from the most valuable brands' pages in the USA and we visualize exemplars of comparison results and present suggestions for future directions.},
  language  = {en}
}
@misc{BinTareafBergerHennigetal.2018,
  author    = {Bin Tareaf, Raad and Berger, Philipp and Hennig, Patrick and Meinel, Christoph},
  title     = {ASEDS},
  series = {IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS))},
  journal   = {IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS))},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {978-1-5386-6614-2},
  doi       = {10.1109/HPCC/SmartCity/DSS.2018.00143},
  pages     = {860 -- 866},
  year      = {2018},
  abstract  = {The Massive adoption of social media has provided new ways for individuals to express their opinion and emotion online. In 2016, Facebook introduced a new reactions feature that allows users to express their psychological emotions regarding published contents using so-called Facebook reactions. In this paper, a framework for predicting the distribution of Facebook post reactions is presented. For this purpose, we collected an enormous amount of Facebook posts associated with their reactions labels using the proposed scalable Facebook crawler. The training process utilizes 3 million labeled posts for more than 64,000 unique Facebook pages from diverse categories. The evaluation on standard benchmarks using the proposed features shows promising results compared to previous research. The final model is able to predict the reaction distribution on Facebook posts with a recall score of 0.90 for "Joy" emotion.},
  language  = {en}
}
@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}
}