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

By: Kumar, Shamanth [author.].
Contributor(s): Morstatter, Fred [author.] | Liu, Huan [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Computer Science: Publisher: New York, NY : Springer New York : Imprint: Springer, 2014Description: X, 77 p. 26 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781461493723.Subject(s): Computer science | Database management | Data mining | Multimedia information systems | User interfaces (Computer systems) | Artificial intelligence | Computer Science | Data Mining and Knowledge Discovery | Multimedia Information Systems | User Interfaces and Human Computer Interaction | Artificial Intelligence (incl. Robotics) | Database ManagementAdditional physical formats: Printed edition:: No titleDDC classification: 006.312 Online resources: Click here to access online
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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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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