  Twitter is a social media giant famous for the exchange of short,
140-character messages called "tweets". In the scientific community, the
microblogging site is known for openness in sharing its data. It provides a
glance into its millions of users and billions of tweets through a "Streaming
API" which provides a sample of all tweets matching some parameters preset by
the API user. The API service has been used by many researchers, companies, and
governmental institutions that want to extract knowledge in accordance with a
diverse array of questions pertaining to social media. The essential drawback
of the Twitter API is the lack of documentation concerning what and how much
data users get. This leads researchers to question whether the sampled data is
a valid representation of the overall activity on Twitter. In this work we
embark on answering this question by comparing data collected using Twitter's
sampled API service with data collected using the full, albeit costly, Firehose
stream that includes every single published tweet. We compare both datasets
using common statistical metrics as well as metrics that allow us to compare
topics, networks, and locations of tweets. The results of our work will help
researchers and practitioners understand the implications of using the
Streaming API.
