Archives
What Do Tim O’Reilly, Lady Gaga, and Marissa Mayer All Have In Common?
Posted on November 22, 2013 4 Comments
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 […]
Super Simple Storage for Social Web Data with MongoDB (Computing Twitter Influence, Part 4)
Posted on November 20, 2013 3 Comments
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 […]
How to Deliver a Successful Tech Workshop with Vagrant and AWS
Posted on November 15, 2013 1 Comment
At Strata, I delivered workshop called Mining the Social Web with IPython Notebook, and in order to ensure that the workshop would meet its objectives and be a smashing success, I knew that a few constraints had to be considered: Everyone must be able to follow along with the examples. (The goal of the workshop is […]
An Approximate Solution for TL;DR [~50 Year Old Text Summarization Hack Presented as a ~1.7MB Animated GIF]
Posted on November 13, 2013 1 Comment
Suffering from information overload? Too much TL;DR happening in your life? Attention span just isn’t what it used to be? Watch this short ~30 second screencast (a ~1.7MB animated GIF) that demonstrates a 50+ year old hack for summarizing news articles and other types of online content. After all, it seemed fitting that the presentation of a […]
Getting Started with Twitter’s API: From Zero to Firehose in ~2.5 Minutes
Posted on November 12, 2013 4 Comments
Mining the Social Web‘s goal is to teach you how to transform curiosity into insight, and its virtual machine features two IPython Notebooks that are designed to get you up and running with Twitter’s API as quickly as possible. The following ~2.5 minute screencast shows how to generate OAuth credentials, establish a Twitter API connection, and make API […]
Mining Social Web APIs with IPython Notebook [Slides]
Posted on October 30, 2013 1 Comment
Thanks so much to everyone who attended the Mining Social Web APIs with IPython Notebook workshop. It was really inspiring to see so many of you get your hands dirty hacking on data (as opposed to just talking or thinking about it.) It’s a lot of work to design a 3 hour workshop for such […]
Now Serving: Full-Text Sampler in IPython Notebook Format
Posted on October 25, 2013 3 Comments
The 2nd Edition of Mining the Social Web has officially soft-launched (the “hard-launch is at my Strata workshop next week), and as of late last week you could download either a PDF file or view an ebook excerpt of the first chapter that introduces data mining with Twitter’s API. Additionally, as of just a few hours ago, the full-text of the first […]
How To Harvest Millions of Twitter Profiles Without Violating the ToS (Computing Twitter Influence, Part 3)
Posted on October 22, 2013 1 Comment
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 […]
Writing Paranoid Code (Computing Twitter Influence, Part 2)
Posted on September 23, 2013 1 Comment
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 […]
Arriving at a Base Influence Metric (Computing Twitter Influence, Part 1)
Posted on September 19, 2013 2 Comments
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 […]
Mining the Social Web 