paid course

Analyzing Social Media Data in Python

In this course, you'll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.

  • 4 hours
  • 14 Videos
  • 51 Exercises
  • 1,749 Participants
  • 4,000 XP
Alex Hanna
Alex Hanna

Computational Social Scientist

Alex Hanna is a computational social scientist working in the areas of politics, natural language processing, and fairness in machine learning and artificial intelligence.

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Collaborator(s)
  • Greg Wilson

    Greg Wilson

  • Kara Woo

    Kara Woo

  • David Campos

    David Campos

  • Shon Inouye

    Shon Inouye

  • Eunkyung Park

    Eunkyung Park

Prerequisites

Course Description

Twitter produces hundreds of million messages per day, with people around the world discussing sports, politics, business, and entertainment. You can access thousands of messages flowing in this stream in a matter of minutes. In this course, you will learn how to collect Twitter data and analyze tweet text, Twitter networks, and the geographical origin of the tweet. We'll be doing this with datasets on tech companies, data science hashtags, and the 2018 State of the Union address. Using these methods, you will be able to inform business and political decision-making by discovering the prevalence of important topics, the diversity of discussion networks, and a topic's geographical reach.

In this course, you'll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.

Course Outline

  1. 1

    Basics of Analyzing Twitter Data

    Free

    Why analyze Twitter, how to access Twitter APIs, and understanding Twitter JSON.

  2. Processing Twitter text

    How to process Twitter text.

  3. Twitter Networks

    Network analysis with Twitter data.

  4. Putting Twitter data on the map

    How to map Twitter data.

Course Instructor

Alex Hanna
Alex Hanna

Computational Social Scientist

Alex Hanna is a computational social scientist working in the areas of politics, natural language processing, and fairness in machine learning and artificial intelligence.

See More
Collaborator(s)
  • Greg Wilson

    Greg Wilson

  • Kara Woo

    Kara Woo

  • David Campos

    David Campos

  • Shon Inouye

    Shon Inouye

  • Eunkyung Park

    Eunkyung Park

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