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Mastering Social Media Mining with Python

You're reading from   Mastering Social Media Mining with Python Unearth deeper insight from your social media data with advanced Python techniques for acquisition and analysis

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Product type Paperback
Published in Jul 2016
Publisher Packt
ISBN-13 9781783552016
Length 338 pages
Edition 1st Edition
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Author (1):
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Marco Bonzanini Marco Bonzanini
Author Profile Icon Marco Bonzanini
Marco Bonzanini
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Table of Contents (10) Chapters Close

Preface 1. Social Media, Social Data, and Python FREE CHAPTER 2. #MiningTwitter – Hashtags, Topics, and Time Series 3. Users, Followers, and Communities on Twitter 4. Posts, Pages, and User Interactions on Facebook 5. Topic Analysis on Google+ 6. Questions and Answers on Stack Exchange 7. Blogs, RSS, Wikipedia, and Natural Language Processing 8. Mining All the Data! 9. Linked Data and the Semantic Web

Users, friends, and followers

One of the main differences between Twitter and other popular social networks is the way users can connect. Relationships on Twitter are, in fact, not necessarily bidirectional. A user can choose to subscribe to other users' tweets, becoming their follower, but the act of following might not be reciprocated. This is very different from what happens with other social networks such as Facebook and LinkedIn, where the relationship has to be confirmed by both parties before taking place.

Using the Twitter terminology, the two directions of the relationship (people I follow versus the people who follow me) have different names. The people I follow are referred to as friends, while the people who follow me are referred to as my followers. When the relationship is bidirectional, the user is commonly described as a mutual friend.

Back to the Twitter API

The Twitter API provides several endpoints to retrieve information about followers, friends, and user profiles...

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