Do more with Twitter data: Evaluating time series and identifying trends

Tuesday, 20 March 2018

Welcome to our series, Do More with Twitter Data, where our data scientists work through examples and share their learnings and tips for getting the most out of Twitter data using the APIs. Each post in the series centers around a real-life project and provides MIT-licensed code that you can use to bootstrap your projects with our enterprise and premium APIs. You can see previous posts in this series here and here

People commonly use Twitter data to identify various trends. In this next example in our series, we’ll introduce an overview of methods for working with Twitter data as a time series to detect trends. We’ll begin by looking at the volume of Tweets that discuss Taylor Swift in 2017, and discuss the following:

  • Using the Search API Counts endpoint
  • Basic operations and transformations for time series
  • Detrending time series to understand local variations
  • Using threshold-based methods to detect trends or bursts
  • Contextualizing a time series
  • Quickly expanding to other pop stars and topics

As in our previous posts, the example is written in Python, but the techniques are language agnostic and can be implemented readily in other languages with good data and machine-learning library support.

Please go here to see the full example, or if you’d like to run it locally, the repo is available on Github.

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