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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

Mining the conversation

After focusing on user profiles and how they are explicitly connected via follower/friend relationships, in this section, we will analyze a different type of interaction-the conversation. On Twitter, users can publish a tweet in reply to a particular piece of content. When two or more users follow up with this process, a proper conversation can unfold.

Figure 3.3 shows a conversation represented as a network. Each node of the network is a tweet (uniquely identified by its ID) and each edge represents a reply to relationship.

This type of relationship has an explicit direction as it can only go in one way (parent-child relationship). For example, if tweet 2 is a reply to tweet 1, we cannot see tweet 1 being a reply to tweet 2. The cardinality of this relationship is always one, meaning that a given tweet can be a reply to one and only one tweet (but we can have multiple tweets in reply to a given one, making the relationship a one-to-many). Moreover, cycles are not...

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