@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} }