Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
Towards detecting influenza epidemics by analyzing Twitter messages
Proceedings of the First Workshop on Social Media Analytics
Twitter Informatics: Tracking and Understanding Public Reaction during the 2009 Swine Flu Pandemic
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Twitter catches the flu: detecting influenza epidemics using Twitter
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Towards personalized learning to rank for epidemic intelligence based on social media streams
Proceedings of the 21st international conference companion on World Wide Web
Tracking Twitter for epidemic intelligence: case study: EHEC/HUS outbreak in Germany, 2011
Proceedings of the 3rd Annual ACM Web Science Conference
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Event Based Epidemic Intelligence (e-EI) encompasses activities related to early warnings and their assessments as part of the outbreak investigation task. Recently, modern disease surveillance systems have started to also monitor social media streams, with the objective of improving their timeliness in detecting disease outbreaks, and producing warnings against potential public health threats. In this tutorial we show how social media analysis can be exploited for two important stages of e-EI, namely: (i) Early Outbreak Detection, and (ii) Outbreak Analysis and Control. We discuss techniques and methods for detecting health-related events from unstructured text and outline approaches, as well as the challenges faced in social media-based surveillance. In particular, we will show how using Twitter can help us to find early cases of an outbreak, as well as, understand the potential causes of contamination and spread from the perspective of the field practitioners.