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.
Twitter, as one of the most popular micro-blogging services, provides large quantities of fresh information including real-time news, comments, conversation, pointless babble and advertisements. Twitter presents tweets in chronological order. Recently, Twitter introduced a new ranking strategy that considers popularity of tweets in terms of number of retweets. This ranking method, however, has not taken into account content relevance or the twitter account. Therefore a large amount of pointless tweets inevitably flood the relevant tweets. This paper proposes a new ranking strategy which uses not only the content relevance of a tweet, but also the account authority and tweet-specific features such as whether a URL link is included in the tweet. We employ learning to rank algorithms to determine the best set of features with a series of experiments. It is demonstrated that whether a tweet contains URL or not, length of tweet and account authority are the best conjunction.