ENTRIES TAGGED "data mining"

Investigating the Twitter Interest Graph

Why Is Twitter All the Rage?

I’m presenting a short webcast entitled Why Twitter Is All the Rage: A Data Miner’s Perspective that is loosely adapted from material that appears early in Mining the Social Web (2nd Ed). I wanted to share out the content that inspired the topic. The remainder of this post is a slightly abridged reproduction of a section that appears early in Chapter 1. If you enjoy it, you can download all of Chapter 1 as a free PDF to learn more about mining Twitter data.
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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 name, it’s (theoretically) quite simple to get all of the account profiles that follow the screen name. Perhaps the most economical route is to use the GET /followers/ids API to request all of the follower IDs in batches of 5,000 per response, followed by the GET /users/lookup API to retrieve full account profiles for up to Y of those IDs in batches of 100 per response. Thus, if an account has X followers, you’d need to anticipate making ceiling(X/5000) API calls to GET /followers/ids and ceiling(X/100) API calls toGET /users/lookup. Although most Twitter accounts may not have enough followers that the total number of requests to each API resource presents rate-limiting problems, you can rest assured that the most popular accounts will trigger rate-limiting enforcements that manifest as an HTTP error in RESTful APIs.

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Computing Twitter Influence, Part 1: Arriving at a Base Metric

The subtle variables affecting a base metric

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 signals.

Unlike social networks such as LinkedIn and Facebook in which connections between entities are symmetric and typically correspond to a real world connection, Twitter’s underlying data model is fundamentally predicated upon asymmetric following relationships. Another way of thinking about a following relationship is to consider that it’s little more than a subscription to a feed about some content of interest. In other words, when you follow another Twitter user, you are expressing interest in that other user and are opting-in to whatever content it would like to place in your home timeline. As such, Twitter’s underlying network structure can be interpreted as an interest graph and mined for insights about the relative popularity of one user when compared to another.
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