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Investigation of Goodreads’ reviews: Kakutanied, deceived or simply honest?

Lala Hajibayova (Kent State University, Kent, Ohio, USA)

Journal of Documentation

ISSN: 0022-0418

Article publication date: 27 February 2019

Issue publication date: 3 May 2019




The purpose of this paper is to present an analysis of Goodreads’ user-generated book reviews from a linguistic perspective for insights into the psychological aspects of reviewers’ perceptions and behaviors. This examination of users’ language and perspectives may shed light on the role and value of user-generated reviews in complementing the traditional representation of resources and facilitating the discoverability of cultural objects.


This study involved a textual analysis of 474,803 unique reviews of Goodreads’ 2015 top-rated books generated by 9,335 Goodreads’ reviewers. In order to better understand the nuances of user-generated reviews, a content analysis was applied to 2,500 reviews of each of the five top-ranked titles in Goodreads’ Fiction Literature genre category.


The analysis of user-generated reviews demonstrates that language is a quite stable and reliable dimension across Goodreads’ users. The high rate of function words utilized, in particular I-words, coupled with positive emotion words, suggests that reviewers tended to convey their opinions in order to influence other individuals’ reading choices, or in Bourdieu’s (1985) terms, influence cultural production. In line with previous studies of user-generated reviews, the prevalence of positive reviews may also imply their unreliable nature. This study supports the importance of transparency regarding inclusion of user-generated reviews in traditional systems of knowledge representation, organization and discovery, such as WorldCat.


This study contributes to better understanding of linguistic characteristics of Goodreads’ reviews, including the role and value of user-generated reviews in complementing traditional representation of resources and facilitating discoverability of cultural objects.



The author would like to thank Dr Timothy Bowman for valuable assistance with data scraping, Dr Yin Zhang for helpful comments and suggestions on earlier versions of this paper, Dr Sharon Pugh for editing the multiple iterations of this work and Naydeen Buente for inspirations.


Hajibayova, L. (2019), "Investigation of Goodreads’ reviews: Kakutanied, deceived or simply honest?", Journal of Documentation, Vol. 75 No. 3, pp. 612-626.



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