Book Image

Mastering Social Media Mining with R

Book Image

Mastering Social Media Mining with R

Overview of this book

With an increase in the number of users on the web, the content generated has increased substantially, bringing in the need to gain insights into the untapped gold mine that is social media data. For computational statistics, R has an advantage over other languages in providing readily-available data extraction and transformation packages, making it easier to carry out your ETL tasks. Along with this, its data visualization packages help users get a better understanding of the underlying data distributions while its range of "standard" statistical packages simplify analysis of the data. This book will teach you how powerful business cases are solved by applying machine learning techniques on social media data. You will learn about important and recent developments in the field of social media, along with a few advanced topics such as Open Authorization (OAuth). Through practical examples, you will access data from R using APIs of various social media sites such as Twitter, Facebook, Instagram, GitHub, Foursquare, LinkedIn, Blogger, and other networks. We will provide you with detailed explanations on the implementation of various use cases using R programming. With this handy guide, you will be ready to embark on your journey as an independent social media analyst.
Table of Contents (13 chapters)
Mastering Social Media Mining with R
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Spam detection


Spam detection is an important use case to deal with. With the growing number of users, the number of spam comments/messages is also increasing. Hence, it is important to build a model or a rule engine which would be capable of identifying the fraudulent user, posting some random message.

The implementation of this algorithm would be slightly difficult because there is no direct mechanism to tag a post as spam. In this section, we will teach you to build a basic model based on certain parameters as well as users' inputs to identify a spam post. This will definitely help you to understand the concept. Any such algorithm implemented would require a constant update since the spammers too, would change their strategy.

Implementing a spam detection algorithm

The following is a simple implementation of a spam detection algorithm using logistic regression. Let's see in detail what the code does to predict the spam messages as comments in the Facebook page posts:

page<- getPage("beach4all...