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Twitter has captured the interest of the scientific community not only forits massive user base and content, but also for its openness in sharing itsdata. Twitter shares a free 1% sample of its tweets through the "StreamingAPI", a service that returns a sample of tweets according to a set ofparameters set by the researcher. Recently, research has pointed to evidence ofbias in the data returned through the Streaming API, raising concern in theintegrity of this data service for use in research scenarios. While theseresults are important, the methodologies proposed in previous work rely on therestrictive and expensive Firehose to find the bias in the Streaming API data.In this work we tackle the problem of finding sample bias without the need for"gold standard" Firehose data. Namely, we focus on finding time periods in theStreaming API data where the trend of a hashtag is significantly different fromits trend in the true activity on Twitter. We propose a solution that focuseson using an open data source to find bias in the Streaming API. Finally, weassess the utility of the data source in sparse data situations and for usersissuing the same query from different regions.

When is it Biased? Assessing the Representativeness of Twitter's Streaming API