Surpassing the Limit: Keyword Clustering to Improve Twitter Sample Coverage

Social media services have become a prominent source of re-search data for both academia and corporate applications. Data from social media services is easy to obtain, highly structured, and comprises opinions from a large number of extremely diverse groups. The microblogging site, Twitter, has garnered a particularly large following from researchers by offering a high volume of data streamed in real time. Unfortunately, the methods in which Twitter selects data to disseminate through the stream are either vague or unpub-lished. Since Twitter maintains sole control of the sampling process, it leaves us with no knowledge of how the data that we collect for research is selected. Additionally, past re-search has shown that there are sources of bias present in Twitters dissemination process. Such bias introduces noise into the data that can reduce the accuracy of learning mod-els and lead to bad inferences. In this work, we take an initial look at the efficiency of Twitter limit track as a sam-ple population estimator. After that, we provide methods to mitigate bias by improving sample population coverage using clustering techniques