  Tracking Twitter for public health has shown great potential. However, most
recent work has been focused on correlating Twitter messages to influenza
rates, a disease that exhibits a marked seasonal pattern. In the presence of
sudden outbreaks, how can social media streams be used to strengthen
surveillance capacity? In May 2011, Germany reported an outbreak of
Enterohemorrhagic Escherichia coli (EHEC). It was one of the largest described
outbreaks of EHEC/HUS worldwide and the largest in Germany. In this work, we
study the crowd's behavior in Twitter during the outbreak. In particular, we
report how tracking Twitter helped to detect key user messages that triggered
signal detection alarms before MedISys and other well established early warning
systems. We also introduce a personalized learning to rank approach that
exploits the relationships discovered by: (i) latent semantic topics computed
using Latent Dirichlet Allocation (LDA), and (ii) observing the social tagging
behavior in Twitter, to rank tweets for epidemic intelligence. Our results
provide the grounds for new public health research based on social media.
