This study explored the utility of Twitter metadata as it relates to influenza surveillance in Canada. Twitter metadata posted between July 2013 and August 2014 containing influenza-related keywords (e.g. influenza, flu, cough) was analyzed using a variety of methodologies. Predictive regression models demonstrated differential utility of specific keywords; Tweets containing several keywords were strongly associated with influenza activity (flu, influenza, grippe), whereas a weaker association was observed with Tweets containing other keywords (e.g. cough, fever). Correlation analysis demonstrated that non-retweets and Tweets that did not contain a URL link were better correlated with influenza cases than retweets and Tweets containing a URL link, respectively. Geospatial cluster analysis showed that Twitter metadata could be used to identify local clusters of influenza-related Twitter chatter; clusters matched traditional surveillance reports in both space and time. Geospatial cluster analysis also identified clusters in areas not reported by the national Fluwatch program.