Twitter as data / Zachary C. Steinert-Threlkeld.

By: Steinert-Threlkeld, Zachary C [author.]Material type: TextTextSeries: Cambridge elementsPublisher: Cambridge : Cambridge University Press, 2018Description: 1 online resource (112 pages) : digital, PDF file(s)Content type: text Media type: computer Carrier type: online resourceISBN: 9781108529327 (ebook)Subject(s): Twitter | Online social networks -- Research -- Methodolo | Social sciences -- Research -- MethodologyAdditional physical formats: Print version: : No titleDDC classification: 006.7/54 LOC classification: HM743.T95 | S74 2018Online resources: Click here to access online Summary: The rise of the internet and mobile telecommunications has created the possibility of using large datasets to understand behavior at unprecedented levels of temporal and geographic resolution. Online social networks attract the most users, though users of these new technologies provide their data through multiple sources, e.g. call detail records, blog posts, web forums, and content aggregation sites. These data allow scholars to adjudicate between competing theories as well as develop new ones, much as the microscope facilitated the development of the germ theory of disease. Of those networks, Twitter presents an ideal combination of size, international reach, and data accessibility that make it the preferred platform in academic studies. Acquiring, cleaning, and analyzing these data, however, require new tools and processes. This Element introduces these methods to social scientists and provides scripts and examples for downloading, processing, and analyzing Twitter data.
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Title from publisher's bibliographic system (viewed on 29 May 2018).

The rise of the internet and mobile telecommunications has created the possibility of using large datasets to understand behavior at unprecedented levels of temporal and geographic resolution. Online social networks attract the most users, though users of these new technologies provide their data through multiple sources, e.g. call detail records, blog posts, web forums, and content aggregation sites. These data allow scholars to adjudicate between competing theories as well as develop new ones, much as the microscope facilitated the development of the germ theory of disease. Of those networks, Twitter presents an ideal combination of size, international reach, and data accessibility that make it the preferred platform in academic studies. Acquiring, cleaning, and analyzing these data, however, require new tools and processes. This Element introduces these methods to social scientists and provides scripts and examples for downloading, processing, and analyzing Twitter data.

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