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Abstract:
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In recent applied work on the Twitter media ecosystem, we have found that Twitter metadata (such as follows, likes, quotes, retweets, mentions, etc) is often more informative than the actual content of tweets themselves. The metadata, in some sense, is the right data to use for many inference tasks. In particular, we find that embedding the Twitter following graph is highly informative. However, collecting the following graph is rather challenging due to API rate limits, and storing graphs can also be challenging. We present some computational infrastructure to make access and storage of this high signal data more straightforward, and suggest that research progress would be well served by an increased focus on instrumentation.
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