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Effects of Sampling on Twitter Trend Detection

Much research has focused on detecting trends on Twitter, including health-related trends such as mentions of Influenza-like illnesses or their symptoms. The majority of this research has been conducted using Twitter{'}s public feed, which includes only about 1{\%} of all public tweets. It is unclear if, when, and how using Twitter{'}s 1{\%} feed has affected the evaluation of trend detection methods. In this work we use a larger feed to investigate the effects of sampling on Twitter trend detection. We focus on using health-related trends to estimate the prevalence of Influenza-like illnesses based on tweets. We use ground truth obtained from the CDC and Google Flu Trends to explore how the prevalence estimates degrade when moving from a 100{\%} to a 1{\%} sample. We find that using the 1{\%} sample is unlikely to substantially harm ILI estimates made at the national level, but can cause poor performance when estimates are made at the city level.

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