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Two 1%s Don’t Make a Whole: Comparing Simultaneous Samples from Twitter’s Streaming API

  • Kenneth Joseph
  • Peter M. Landwehr
  • Kathleen M. Carley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8393)

Abstract

We compare samples of tweets from the Twitter Streaming API constructed from different connections that tracked the same popular keywords at the same time. We find that on average, over 96% of the tweets seen in one sample are seen in all others. Those tweets found only in a subset of samples do not significantly differ from tweets found in all samples in terms of user popularity or tweet structure. We conclude they are likely the result of a technical artifact rather than any systematic bias.

Practically, our results show that an infinite number of Streaming API samples are necessary to collect “most” of the tweets containing a popular keyword, and that findings from one sample from the Streaming API are likely to hold for all samples that could have been taken. Methodologically, our approach is extendible to other types of social media data beyond Twitter.

Keywords

Limit Notice Technical Artifact Social Medium Data Simultaneous Sample User Popularity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kenneth Joseph
    • 1
  • Peter M. Landwehr
    • 1
  • Kathleen M. Carley
    • 1
  1. 1.Carnegie Mellon UniversityPittsburghUSA

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