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Research with User-Generated Book Review Data: Legal and Ethical Pitfalls and Contextualized Mitigations

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Information for a Better World: Normality, Virtuality, Physicality, Inclusivity (iConference 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13971))

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Abstract

The growing quantity of user-generated book reviews has opened up unprecedented opportunities for empirical research on books, reading, and readership. While there is an abundance of literature addressing the legal and ethical use of user-generated and social media data in general, for user-generated book reviews, such discussions have been mostly absent. From a library and information sciences perspective, user-generated book reviews can pose novel challenges because each book reviewer may simultaneously be (1) a presumably anonymous and safe online user; and, (2) an identifiable reader who can suffer real harm, e.g., cyber doxing and personal attack. This user/reader duality can create conflicting recommendations regarding which legal or ethical guidelines to follow. According to our review, potential legal issues include copyright infringement and violations of terms of service/end-user license agreements and privacy rights, while ethical concerns are centered on users’ expectations, informed consent, and institutional reviews. This paper reviews (1) potential legal and ethical pitfalls in leveraging user-generated book reviews; and, (2) professional and scholarly references that might serve as useful guidelines to avoid or manage these pitfalls.

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Notes

  1. 1.

    In the context of this paper, user-generated book reviews include not only actual book reviews but also numerical ratings, crowdsourced tags, user-curated book lists, virtual collections of books, graphic content, etc.

  2. 2.

    For example, book reviews may contain user names that overlap with real names, email addresses, identifying parts of addresses, or workplaces.

  3. 3.

    Wattpad is a storytelling and social reading platform based in Canada [160].

  4. 4.

    EULA is a contract between the licensor and the licensee, which establishes the licensee’s right to use a proprietary product. TOS refers to a contract between a provider and a user which defines the rules that a user should follow in order to use a service. In our research contexts, we consider them interchangeable terms, as both of them specify the permissions and prohibitions for using the book review platforms’ service, products, and/or data.

  5. 5.

    The HathiTrust is a consortium of several hundred academic libraries that have collaborated (with scanning agencies like Google) to create a massive digital library [15, 61].

  6. 6.

    The Internet Archive is a large digital library that preserves and provides digitized content to the public [154].

  7. 7.

    Due to length constraints of this paper, we only discussed some of the articles that we reviewed for this paper. The full list of references is available at https://github.com/Yuerong2/iConference2023appendix/blob/main/iconference2023referencesAppendix.pdf. Our literature review is limited to empirical research on user-generated book reviews based on computational and/or qualitative methods. We did not consider theoretical work on user-generated book reviews without empirical data involved.

  8. 8.

    Amazon (Amazon.com: Books) is currently the largest online bookseller worldwide. Goodreads is one of the dominant social reading and book review platforms based in the United States, with 90 million registered members as of 2019. LibraryThing is one of the most impactful social cataloging platforms based in the United States, with 2.6 million users as of 2021 [54, 73, 95, 156,157,158,159].

  9. 9.

    In some publications, data collection methods are not explicitly specified, and general terms like “got”, “collected”, “downloaded” and “extracted” are used in lieu of providing more detailed collection method descriptions. [4, 27, 36, 64, 139].

  10. 10.

    Such considerations might not apply to studies on user-generated data. We elaborate on this issue in Sect. 3.4.

  11. 11.

    In the United States, an Institutional Review Board (IRB) is an administrative unit formally designated to review and monitor research activities using human research subjects. IRBs approve or disapprove research proposals prior to their initiation to ensure the rights and welfare of human research subjects [144].

  12. 12.

    Due to copyright and perform restrictions, it is recommendable to share only unique key identifiers for collected data items instead of actual datasets such that other researchers can rehydrate the data, which bears the risk of collecting incomplete datasets [32, 109].

  13. 13.

    The US copyright law demands consideration of four factors for determining whether fair use is applicable: purpose and character of the use; nature of the copyrighted work; amount and substantiality of the portion used; and the effect of the use upon the potential market for the copyrighted work. For research based on user-generated book reviews, the first two conditions of fair use may be less of a concern, but researchers should pay more attention to the third and fourth conditions.

  14. 14.

    “Transformative use” of the data alters original content to give it “new expression, meaning or message” [133]. “Non-consumptive use” refers to computer-assisted research, which has been found not to conflict with copyright holders’ interests. For instance, in transformative and non-consumptive research, digital humanities scholars can conduct computational text analysis of millions of books (copyrighted books included) without actually reading or re-disseminating (i.e., without human “consumption” of) any expressive content of those books [113].

  15. 15.

    It should be noted that “these state laws, however, are overridden or trumped by federal laws that allow federal agencies to seek library records” [21, 90]. They vary by state, however, they reflect a consensus that library users’ data are confidential and should only be disclosed under certain circumstances (e.g., with the user’s informed consent, under a court order, etc.).

  16. 16.

    Robots.txt files are developed and used primarily to inform search engines and web scrapers whether data on a webpage is prohibited or permitted for harvesting. They are widely adopted by the websites to regulate scraping, although their prohibitions “fall into a legal grey area” [123].

  17. 17.

    Accessed in August 2022.

  18. 18.

    In this case, hiQ scraped publicly available user data from LinkedIn’s website to supply its own business, in spite of LinkedIn’s no-data-scraping policies, letters specifically addressed to hiQ, and technical measures enacted against hiQ. LinkedIn claimed that hiQ’s scraping violated the CFAA, the Digital Millennium Copyright Act, and state trespass law, while hiQ denied these claims and asserted its right to scrape publicly accessible data [53].

  19. 19.

    However, in practice, it is difficult for researchers to verify whether the reviewers are indeed aware of the public accessibility of their data. Researchers should not make assumptions about users’ awareness.

  20. 20.

    Kosinski and colleagues argue that no consent is needed and user-generated online data can be conceptualized as archival data if (1) users consciously made their data public; (2) data collected is anonymized; (3) researchers do not interact with participants; and, (4) no identifiable user information is published. [87].

  21. 21.

    Different IRBs might make different decisions on requests for exemption based on specific research proposals. For instance, we learned from our own research experience that analysis of publicly available and de-identified book review data without any interaction with the reviewers is mostly likely to be considered “Not Human Subjects Research” (NHSR) by the IRB at our home institution [142]. In this case, researchers who believe their work does not require IRB review or oversight should submit a request to their institution’s IRB for a designation as Not Human Subjects Research. They might also consider asking for an Exempt Status determination, in which case they are performing Human Subjects research but are exempt from regular oversight.

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  1. School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, USA

    Yuerong Hu, Glen Layne-Worthey, Alaine Martaus, J. Stephen Downie & Jana Diesner

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Hu, Y., Layne-Worthey, G., Martaus, A., Downie, J.S., Diesner, J. (2023). Research with User-Generated Book Review Data: Legal and Ethical Pitfalls and Contextualized Mitigations. In: Sserwanga, I., et al. Information for a Better World: Normality, Virtuality, Physicality, Inclusivity. iConference 2023. Lecture Notes in Computer Science, vol 13971. Springer, Cham. https://doi.org/10.1007/978-3-031-28035-1_13

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