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What Do Tim O’Reilly, Lady Gaga, and Marissa Mayer All Have In Common?

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This post examines the followers of some popular Twitter users as the final installment of a multi-part series about exploring Twitter influence by asking the (Freakonomics-inspired) question, What do Tim O’Reilly, Lady Gaga, and Marissa Mayer all have in common? Although it may initially seem like an obnoxious question to ask, some of the answers may intrigue you […]

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Super Simple Storage for Social Web Data with MongoDB (Computing Twitter Influence, Part 4)

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In the last few posts for this series on computing twitter influence, we’ve reviewed some of the considerations in calculating a base metric for influence and how to acquire the necessary data to begin analysis. This post finishes up all of the prerequisite machinery before the real data science fun begins by introducing MongoDB as a […]

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How To Harvest Millions of Twitter Profiles Without Violating the ToS (Computing Twitter Influence, Part 3)

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In the last post in this continuing series on computing Twitter influence, we developed a wrapper function called make_twitter_request that handles the various sorts of HTTP error codes and network failures that you are likely to experience as you aspire to acquire non-trivial amounts of data from Twitter’s API. Although you are somewhat unlikely to […]

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Writing Paranoid Code (Computing Twitter Influence, Part 2)

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In the previous post of this series, we aspired to compute the influence of a Twitter account and explored some relevant variables to arriving at a base metric. This post continues the conversation by presenting some sample code for making “reliable” requests to Twitter’s API to facilitate the data collection process. Given a Twitter screen […]

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Arriving at a Base Influence Metric (Computing Twitter Influence, Part 1)

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This post introduces a series that explores the problem of approximating a Twitter account’s influence. With the ubiquity of social media and its effects on everything from how we shop to how we vote at the polls, it’s critical that we be able to employ reasonably accurate and well-understood measurements for approximating influence from social media […]

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