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Twitter Data Analytics

  • Shamanth Kumar
  • Fred Morstatter
  • Huan Liu

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-x
  2. Shamanth Kumar, Fred Morstatter, Huan Liu
    Pages 1-3
  3. Shamanth Kumar, Fred Morstatter, Huan Liu
    Pages 5-22
  4. Shamanth Kumar, Fred Morstatter, Huan Liu
    Pages 23-33
  5. Shamanth Kumar, Fred Morstatter, Huan Liu
    Pages 35-48
  6. Shamanth Kumar, Fred Morstatter, Huan Liu
    Pages 49-69
  7. Back Matter
    Pages 71-77

About this book

Introduction

This brief provides methods for harnessing Twitter data to discover solutions to complex inquiries. The brief introduces the process of collecting data through Twitter’s APIs and offers strategies for curating large datasets. The text gives examples of Twitter data with real-world examples, the present challenges and complexities of building visual analytic tools, and the best strategies to address these issues. Examples demonstrate how powerful measures can be computed using various Twitter data sources. Due to its openness in sharing data, Twitter is a prime example of social media in which researchers can verify their hypotheses, and practitioners can mine interesting patterns and build their own applications. This brief is designed to provide researchers, practitioners, project managers, as well as graduate students with an entry point to jump start their Twitter endeavors. It also serves as a convenient reference for readers seasoned in Twitter data analysis.

Keywords

Big data Data mining Social media Twitter analytics Visual analytics

Authors and affiliations

  • Shamanth Kumar
    • 1
  • Fred Morstatter
    • 2
  • Huan Liu
    • 3
  1. 1.Data Mining and Machine Learning LabArizona State UniversityTempeUSA
  2. 2.Data Mining and Machine Learning LabArizona State UniversityTempeUSA
  3. 3.Data Mining and Machine Learning LabArizona State UniversityTempeUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-9372-3
  • Copyright Information The Author(s) 2014
  • Publisher Name Springer, New York, NY
  • eBook Packages Computer Science
  • Print ISBN 978-1-4614-9371-6
  • Online ISBN 978-1-4614-9372-3
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site