relationship between the content of tweets and the outcomes of NBA Playoff games. Bollen et al. [18] showed that tweet sentiments, aggregately seen as a reflection of public mood levels, can be used to predict the incidence of socio-political, cultural, and economic events. Mitchell et al. [19] used geo-tagged tweets across the United States to estimate localized changes in a variety of sociometric indices such as happiness, the level of education, and obesity rates. 

The quasi-realtime nature of Twitter also makes it a potentially valuable resource for the detection and management of emergency situations [20]. For example, Robinson et al. [21] described an earthquake detection system, while Sakaki et al. [22] used a spatio-temporal model of tweet frequencies to infer the location of the epicentre of an earthquake. 

Closer in spirit to the nature of work in the present paper is the corpus of work on the use of Twitter in the domain of health care. For example, Paul and Dredze [23] used tweets to extract words related to symptoms and treatments, and a topic model to associate them with the corresponding ailments. In their subsequent work [24], the model was extended to track the spread of illnesses over time, measure behavioural risk factors, and analyse symp- toms and medication usage. The use of LDA-based health topic modelling was explored by Prier et al. [25]. Dementia-related tweets were the focus of work by Robillard et al. [26] who collected relevant tweets over a 24h period using keyword filtering and used them to discover the dominant chatter themes. Similarly, Scanfeld et al. [27] used Twitter to analyse the patterns of antibiotic use. Jashnisky et al. [28] studied the relationship between Twit- ter conversations deemed to reflect high suicide risk and actual suicide rates in the United States. They demonstrated that high risk individuals may be recognized from their social media status. Lastly, Himelboim and Han [29] examined the connectivity patterns of Twitter users interacting within a specific online community of cancer-affected individuals. 

In contrast to the sporadic studies on different diseases described above, the use of Twit- ter data in the management of highly contagious diseases like influenza has attracted a more concentrated research effort. For example, Culotta [30] investigated whether the frequency of influenza epidemic-related tweets can be related to ‘ground truth’ data from centres for disease control and prediction. Achrekar et al. [31] showed that the emergence and the 

