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How to detect signal from noise and build powerful filtering rules


Hundreds of millions of Tweets are sent on Twitter every single day. While this creates rich insights and valuable business opportunities, it can also lead to challenges when it comes to extracting the right data. Building a strong ruleset is the key that gives you access to what you’re looking for and ensures that you generate valuable insights, both in terms of quality and quantity. 

In this step-by-step tutorial, we’ll outline how you can identify meaningful insights by building strong signal rules and applying tactics for reducing noise. We’ll also put each step into practice by covering a real-life example in the walkthrough section towards the end. 

This tutorial was written in the context of using the Twitter enterprise PowerTrack API, which allows you to filter all publicly available Tweets in real time. However, many of the tactics outlined below can also be used with other products and endpoints available with the Twitter API; for example, to curate your queries with the Search API.

Please note: The Search API only supports a subset of the filtering operators available with the PowerTrack API. 


This learning path contains the following articles, which are meant to be read in sequential order. You will find a link to the next article at the bottom of each article. 

Step 1: Understanding the basics of rules and query operators

Step 2: Identifying what “signal” means to you

Step 3: Building initial filters

Step 4: Identifying what “noise” means to you

Step 5: Refining your filters and building a strong ruleset

Walkthrough: What this means in practice

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