Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Modern Information Retrieval
Medical case-driven classification of microblogs: characteristics and annotation
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Epidemic intelligence: for the crowd, by the crowd
ICWE'12 Proceedings of the 12th international conference on Web Engineering
Tracking Twitter for epidemic intelligence: case study: EHEC/HUS outbreak in Germany, 2011
Proceedings of the 3rd Annual ACM Web Science Conference
Living analytics methods for the web observatory
Proceedings of the 22nd international conference on World Wide Web companion
A framework for detecting public health trends with Twitter
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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In the presence of sudden outbreaks, how can social media streams be used to strengthen surveillance capabilities? In May 2011, Germany reported one of the largest described outbreaks of Enterohemorrhagic Escherichia coli (EHEC). By end of June, 47 persons had died. After the detection of the outbreak, authorities investigating the cause and the impact in the population were interested in the analysis of micro-blog data related to the event. Since Thousands of tweets related to this outbreak were produced every day, this task was overwhelming for experts participating in the investigation. In this work, we propose a Personalized Tweet Ranking algorithm for Epidemic Intelligence (PTR4EI), that provides users a personalized, short list of tweets based on the user's context. PTR4EI is based on a learning to rank framework and exploits as features, complementary context information extracted from the social hash-tagging behavior in Twitter. Our experimental evaluation on a dataset, collected in real-time during the EHEC outbreak, shows the superior ranking performance of PTR4EI. We believe our work can serve as a building block for an open early warning system based on Twitter, helping to realize the vision of Epidemic Intelligence for the Crowd, by the Crowd.