The Classics “Shelf”: Genre, Hashtag, Advertising Keyword: This essay understands Goodreads users to be readers as well as “amateur critics”…
The Goodreads Algorithmic Echo Chamber: …The first key insight is that Goodreads purposely conceals and obfuscates its data from the public. The company does not provide programmatic (API) access to the full text of its reviews, as some websites and social media platforms do. To collect reviews, we thus needed to use a technique called “web scraping”, where one extracts data from the web, specifically from the part of a web page that users can see, as opposed to retrieving it from an internal source.39 The Goodreads web interface makes it difficult to scrape large amounts of review data, however. It’s not just difficult for researchers to collect Goodreads reviews. It’s difficult for anyone to interact with Goodreads reviews. Though more than 90 million reviews have been published on Goodreads in the site’s history, one can only view 300 reviews for any given book in any given sort setting, a restriction that was implemented in 2016. Previously, Goodreads users could read through thousands of reviews for any given book. Because there are a handful of ways to sort Goodreads reviews (eg. by publication date or by language), it is technically possible to read through 300 reviews in each of these sort settings. But even when accounting for all possible sort setting permutations, the number of visible and accessible Goodreads reviews is still only a tiny fraction of total Goodreads reviews. This throttling has been a source of frustration both for Goodreads users and for researchers.
| Variable | Oldest | Newest | Default | All |
|---|---|---|---|---|
| Number of Reviews | 42,311 reviews | 42,657 reviews | 42,884 reviews | 127,855 reviews |
| Mean Length of Reviews | 54.6 words | 91.8 words | 261.2 words | 136.3 words |
| Number of Unique Users | 24,163 users | 33,486 users | 17,362 users | 69,342 users |
| Mean Number of Reviews per User | 1.75 reviews/user | 1.27 reviews/user | 2.47 reviews/user | 1.84 reviews/user |
Working within these constraints, we collected ~900 unique reviews for each classic book—300 default sorted reviews, 300 newest reviews, and 300 oldest reviews—for a total of 127,855 Goodreads reviews. We collected these reviews regardless of whether the user explicitly shelved the book as a classic or not. We also explicitly filtered for English language reviews. Despite this filtering, a small number of non-English and multi-language reviews are included in the dataset, and they show up as outliers in some of our later results. Compared to the archives of most readership and reception studies, this dataset is large and presents exciting possibilities for studying reception at scale. But it is important to note that this dataset is not large or random enough to be a statistically representative sample of the “true” distribution of classics reviews on Goodreads. We believe our results provide valuable insight into Goodreads and the classics nonetheless.
Though the constraints of the Goodreads platform distort our dataset in certain ways, we tried to use this distortion to better scrutinize the influence of the web interface on Goodreads users. For example, the company never makes clear how it sorts reviews by default, but we found that reviews with a combination of more likes and more comments almost always appear above those with fewer—except in certain cases when there is, perhaps, another invisible social engagement metric such as the number of clicks, views, or shares that a review has received. Since we collected data in multiple sort settings, we are able to go further than this basic observation and investigate how exactly this default sorting algorithm shapes Goodreads users’ behavior, social interactions, and perceptions of the classics.
Based on our analysis, we found that the first 300 default visible reviews for any given book develop into an echo chamber. Once a Goodreads review appears in the default sorting, in other words, it is more likely to be liked and commented on, and more likely to stay there (Figure 6). Meanwhile the majority of reviews quickly age beyond “newest” status and become hidden from public view. These ‘liking’ patterns reveal that Goodreads users reinforce certain kinds of reviews, such as longer reviews (Figure 7), reviews that include a “spoiler alert” (Figure 9), and reviews written by a small set of Goodreads users who likely have many followers (Table 2). If a review is prominently displayed by the default sorting algorithm, its author may be more likely to go back and modify this review. More default-sorted reviews included the words “update” or “updated” than oldest or newest reviews (Figure 8).
In one especially interesting updated review, a Goodreads user raised her rating of Toni Morrison’s The Bluest Eye and apologized for the way that her original, more negative review offended others and reflected her white privilege, which other Goodreads users had pointed out.