Mining Social Media-Finding Stories in Internet Data

Brief Contents
Contents in Detail
Acknowledgments
Introduction
	What Is Data Analysis?
	Who Is This Book For?
	Conventions Used in This Book
	What This Book Covers
		Part I: Data Mining
		Part II: Data Analysis
	Downloading and Installing Python
		Installing on Windows
		Installing on macOS
	Getting Help When You’re Stuck
	Summary
Part I: Data Mining
Chapter 1: The Programming Languages You’ll Need to Know
	Frontend Languages
		How HTML Works
		How CSS Works
		How JavaScript Works
	Backend Languages
		Using Python
		Getting Started with Python
		Working with Numbers
		Working with Strings
		Storing Values in Variables
		Storing Multiple Values in Lists
		Working with Functions
		Creating Your Own Functions
		Using Loops
		Using Conditionals
	Summary
Chapter 2: Where to Get Your Data
	What Is an API?
	Using an API to Get Data
		Getting a YouTube API Key
		Retrieving JSON Objects Using Your Credentials
	Answering a Research Question Using Data
		Refining the Data That Your API Returns
	Summary
Chapter 3: Getting Data with Code
	Writing Your First Script
	Running a Script
	Planning Out a Script
	Libraries and pip
	Creating a URL-based API Call
	Storing Data in a Spreadsheet
		Converting JSON into a Dictionary
		Going Back to the Script
	Running the Finished Script
	Dealing with API Pagination
	Templates: How to Make Your Code Reusable
		Storing Values That Change in Variables
		Storing Code in a Reusable Function
	Summary
Chapter 4: Scraping Your Own Facebook Data
	Your Data Sources
	Downloading Your Facebook Data
	Reviewing the Data and Inspecting the Code
		Structuring Information as Data
		Scraping Automatically
	Analyzing HTML Code to Recognize Patterns
		Grabbing the Elements You Need
		Extracting the Contents
	Writing Data into a Spreadsheet
		Building Your Rows List
		Writing to Your .csv File
	Running the Script
	Summary
Chapter 5: Scraping a Live Site
	Messy Data
		Ethical Considerations for Data Scraping
		The Robots Exclusion Protocol
		The Terms of Service
		Technical Considerations for Data Scraping
		Reasons for Scraping Data
	Scraping from a Live Website
		Analyzing the Page’s Contents
		Storing the Page Content in Variables
		Making the Script Reusable
		Practicing Polite Scraping
	Summary
Part II: Data Analysis
Chapter 6: Introduction to Data Analysis
	The Process of Data Analysis
	Bot Spotting
	Getting Started with Google Sheets
	Modifying and Formatting the Data
	Aggregating the Data
		Using Pivot Tables to Summarize Data
		Using Formulas to Do Math
	Sorting and Filtering the Data
	Merging Data Sets
	Other Ways to Use Google Sheets
	Summary
Chapter 7: Visualizing Your Data
	Understanding Our Bot Through Charts
		Choosing a Chart
		Specifying a Time Period
		Making a Chart
	Conditional Formatting
		Single-Color Formatting
		Color Scale Formatting
	Summary
Chapter 8: Advanced Tools for Data Analysis
	Using Jupyter Notebook
		Setting Up a Virtual Environment
		Organizing the Notebook
		Installing Jupyter and Creating Your First Notebook
		Working with Cells
	What Is pandas?
		Working with Series and Data Frames
		Reading and Exploring Large Data Files
		Looking at the Data
		Viewing Specific Columns and Rows
	Summary
Chapter 9: Finding Trends in Reddit Data
	Clarifying Our Research Objective
	Outlining a Method
	Narrowing the Data’s Scope
		Selecting Data from Specific Columns
		Handling Null Values
		Classifying the Data
	Summarizing the Data
		Sorting the Data
		Describing the Data
	Summary
Chapter 10: Measuring the Twitter Activity of Political Actors
	Getting Started
		Setting Up Your Environment
		Loading the Data into Your Notebook
	Lambdas
	Filtering the Data Set
	Formatting the Data as datetimes
	Resampling the Data
	Plotting the Data
	Summary
Chapter 11: Where to Go from Here
	Coding Styles
	Statistical Analysis
	Other Kinds of Analyses
	Conclusion
Index