• The Crowdsourced “Classics” and the Revealing Limits of Goodreads Data

    Author(s):
    Maria Antoniak, Melanie Walsh (see profile)
    Date:
    2020
    Group(s):
    DH2020
    Subject(s):
    Reading, Social media, Social networks, Digital humanities
    Item Type:
    Presentation
    Meeting Title:
    DH2020
    Meeting Org.:
    ADHO
    Meeting Loc.:
    Cyberspace
    Meeting Date:
    July 2020
    Tag(s):
    reader-response, readership studies, goodreads, Reception studies
    Permanent URL:
    http://dx.doi.org/10.17613/7k61-eg23
    Abstract:
    This presentation draws from forthcoming work on the Goodreads "classics." Goodreads is the largest social networking site for readers on the internet (90 million users) and a subsidiary of Amazon. The “classics” are one of the most active Goodreads categories, with some of the most rated and reviewed books across the entire site. Why are the classics so popular on Goodreads? Which books have readers “shelved” as classics most often? What do the classics mean to contemporary readers? We use computational methods such as topic modeling to investigate these questions and more. We also interrogate the limits of Goodreads data and the influence of Goodreads/Amazon's proprietary algorithms on reviews. We find that reviews sorted by the default algorithm, for example, tend to be longer, more socially conscientious (e.g. include a spoiler alert), and written by a smaller set of Goodreads users. Extrapolating from these findings, we argue that computational methods can provide a way of documenting, understanding, and critiquing algorithmic culture and its effects.
    Metadata:
    xml
    Status:
    Published
    Last Updated:
    3 years ago
    License:
    Attribution

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