% Tags: Clustering
% Encoding: utf-8
@Article{Pohl2013b,
  author = {Pohl, Daniela and Bouchachia, Abdelhamid and Hellwagner, Hermann},
  journal = {International Journal of Information Systems for Crisis Response and Management (IJISCRAM)},
  title = {Supporting Crisis Management via Detection of Sub-Events in Social Networks},
  year = {2013},
  month = {jul},
  number = {3},
  pages = {20-36},
  volume = {5},
  address = {Hershey, PA, USA},
  language = {EN},
  publisher = {IGI Global}
}
@InProceedings{Pohl2013a,
  author = {Pohl, Daniela and Bouchachia, Abdelhamid and Hellwagner, Hermann},
  booktitle = {12th International Conference on Machine Learning and Applications},
  title = {Online Processing of Social Media Data for Emergency Management},
  year = {2013},
  address = {Los Alamitos, CA, USA},
  editor = {Tecuci, Gheorghe and Boicu, Mihai and Kubat, Miroslav},
  month = {dec},
  pages = {1-6},
  publisher = {IEEE},
  abstract = {Social media offers an opportunity for emergency management to identify issues that need immediate reaction. To support the effective use of social media, an analysis approach is needed to identify crisis-related hotspots. We consider in this investigation the analysis of social media (i.e., Twitter, Flickr and YouTube) to support emergency management by identifying sub-events. Sub-events are significant hotspots that are of importance for emergency management tasks. Aiming at sub-event detection, recognition and tracking, the data is processed online in real-time. We introduce an incremental feature selection mechanism to identify meaningful terms and use an online clustering algorithm to uncover sub-events on-the-fly. Initial experiments are based on tweets enriched with Flickr and YouTube data collected during Hurricane Sandy. They show the potential of the proposed approach to monitor sub-events for real-world emergency situations.},
  keywords = {Online Clustering, Sub-Event Detection, Crisis Management},
  language = {EN},
  location = {Miami, Florida, USA},
  pdf = {https://www.itec.aau.at/bib/files/Pohl_ICMLA13.pdf},
  talkdate = {2013.12.01},
  talktype = {poster}
}
@InProceedings{Pohl2012c,
  author = {Pohl, Daniela and Bouchachia, Abdelhamid and Hellwagner, Hermann},
  booktitle = {11th International Conference on Machine Learning and Applications},
  title = {Automatic Identification of Crisis-Related Sub-Events using Clustering},
  year = {2012},
  address = {Los Alamitos, CA, USA},
  editor = {Han, Jiawei and Khoshgoftaar, Taghi M and Zhu, Xingquan},
  month = {dec},
  pages = {333-338},
  publisher = {IEEE},
  abstract = {Social media are becoming an important instrument for supporting crisis management, due to their broad acceptance and the intensive usage of mobile devices for accessing them. Social platforms facilitate collaboration among the public during a crisis and also support after-the-fact analysis. Thus, social media are useful for the processes of understanding, learning, and decision making. In particular, having information from social networks in a suitable, ideally summarized, form can speed up such processes. The present study relies on Flickr and YouTube as social media and aims at automatically identifying individual sub-events within a crisis situation. The study applies a two-phase clustering approach to detect those sub-events. The first phase uses geo-referenced data to locate a sub-event, while the second phase uses the natural language descriptions of pictures and videos to further identify the ”what-about” of those sub-events. The results show high potential of this social media-based clustering approach for detecting crisis-related sub-events.},
  keywords = {Clustering, Sub-Event Detection, Crisis Management},
  language = {EN},
  location = {Boca Raton, Florida, USA},
  pdf = {https://www.itec.aau.at/bib/files/06406815.pdf},
  talkdate = {2012.12.12},
  talktype = {registered},
  url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6406815}
}