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Twitter Data Analytics [electronic resource] / by Shamanth Kumar, Fred Morstatter, Huan Liu.

By: Contributor(s): Material type: TextTextSeries: SpringerBriefs in Computer SciencePublisher: New York, NY : Springer New York : Imprint: Springer, 2014Description: X, 77 p. 26 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781461493723
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 006.312 23
LOC classification:
  • QA76.9.D343
Online resources:
Contents:
Introduction -- Crawling Twitter Data -- Storing Twitter Data -- Analyzing Twitter Data -- Visualizing Twitter Data.
In: Springer eBooksSummary: 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.
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Item type Current library Call number Status Date due Barcode
E-Book E-Book Central Library Available E-40200

Introduction -- Crawling Twitter Data -- Storing Twitter Data -- Analyzing Twitter Data -- Visualizing Twitter Data.

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.

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