@inproceedings{temnikova-etal-2014-building,
    title = "Building a Crisis Management Term Resource for Social Media: The Case of Floods and Protests",
    author = "Temnikova, Irina  and
      Varga, Andrea  and
      Biyikli, Dogan",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Loftsson, Hrafn  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}`14)",
    month = may,
    year = "2014",
    address = "Reykjavik, Iceland",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/L14-1503/",
    pages = "740--747",
    abstract = "Extracting information from social media is being currently exploited for a variety of tasks, including the recognition of emergency events in Twitter. This is done in order to supply Crisis Management agencies with additional crisis information. The existing approaches, however, mostly rely on geographic location and hashtags/keywords, obtained via a manual Twitter search. As we expect that Twitter crisis terminology would differ from existing crisis glossaries, we start collecting a specialized terminological resource to support this task. The aim of this resource is to contain sets of crisis-related Twitter terms which are the same for different instances of the same type of event. This article presents a preliminary investigation of the nature of terms used in four events of two crisis types, tests manual and automatic ways to collect these terms and comes up with an initial collection of terms for these two types of events. As contributions, a novel annotation schema is presented, along with important insights into the differences in annotations between different specialists, descriptive term statistics, and performance results of existing automatic terminology recognition approaches for this task."
}