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Writing Paranoid Code (Computing Twitter Influence, Part 2)

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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Mining the Social Web Like a Pro: Four Steps to Success [Slides]

[Update – 8 October 2013: The data journalism team at La Nación expanded upon the analysis presented in the slides and put together a really nice article that tells a story about the data. Definitely check it out, and if you don’t read Spanish, try translating with Chrome or paste the URL into Google Translate.] I […]

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

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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Surprising Stats From Mining One Million Tweets About #Syria

I’ve been filtering Twitter’s firehose for tweets about “#Syria” for about the past week in order to accumulate a sizable volume of data about an important current event. As of Friday, I noticed that the tally has surpassed one million tweets, so it seemed to be a good time to apply some techniques from Mining […]

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What Are People Tweeting About #Syria in Your Neck of the Woods?

Tweets about #Syria from all around the world

Update: Sept 9, 2013 – View Surprising Stats From Mining One Million Tweets About #Syria for a more comprehensive analysis of content in this post. In the opening chapter of Mining the Social Web, 2nd Edition we begin the journey into mining social data by way of Twitter. To paraphrase some of the content from an […]

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