Effects of Publicity and Complexity in Reader Polarization (2025)

Abstract: We investigate how Goodreads rating distributions reflect variations in audience reception across literary works. By examining a large-scale dataset of novels, we analyze whether metrics such as the entropy or standard deviation of rating distributions correlate with textual features – including perplexity, nominal ratio, and syntactic complexity. These metrics reveal a disagreement continuum: more complex texts – i.e., more cognitively demanding books, with a more canon-like textual profile – generate polarized reader responses, while mainstream works produce more uniform reactions. We compare evaluation patterns across canonical and non-canonical works, bestsellers, and prize-winners, finding that textual complexity drives rating polarization even when controlling for publicity effects. Our findings demonstrate that linguistically unpredictable texts, particularly those with higher nominal density and dependency distance, generate divergent reader evaluations. This challenges conventional literary success metrics and suggests that the shape of rating distributions offers valuable insights beyond average scores. We hope our approach establishes a productive framework for understanding how literary features influence reception and how disagreement metrics can enhance our understanding of public literary judgment.

## introduction several computational literary studies estimate literary success using quantitative proxies such as reader evaluation @xcite , sales data @xcite , or number of prizes received @xcite . these studies often default to goodreads' within-platform metrics -such as the number of ratings or the average rating -since the goodreads platform aggregates opinions from millions of diverse, lay readers, offering a democratic measure of literary judgment @xcite . however, while these metrics capture important aspects of popularity and appreciation, they typically focus on central tendencies. our study proposes to advance knowledge on reader appreciation by examining the full distribution of ratings, rather than solely relying on the average. specifically, by analyzing the distribution of ratings via rating entropy and standard deviation, we aim to refine our understanding of literary success, testing three interrelated hypotheses. first, we hypothesize a positive relationship between rating count and rating distribution entropy (h1), suggesting that books with a higher number of ratings tend to exhibit a broader spread of opinions -a phenomenon we refer to as the "publicity effect", observed in other studies @xcite . second, we posit that as a book attracts a more diverse or polarized audience, the relation between average rating and rating count will decouple (h2), resulting in little or no direct correlation between these two metrics. this decoupling implies that popularity (as measured by rating count) does not necessarily equate to higher average appreciation. third, and central to our contribution, is our hypothesis regarding textual complexity (h3). prior studies have observed that highly complex texts tend to be less popular, attracting relatively fewer readers due to their demanding nature @xcite . however, there are notable exceptions where complex texts, often deemed canonical, incite particularly polarized responses among those who do engage with them. this phenomenon may be very similar to the "publicity effect" -where @xcite suggest that the popular status of a book leads to more readers, including those not predisposed to like them -in the sense that canonical books, by their canonical status, will find more readers not predisposed to like themespecially if we consider reading assignments in educational settings. we hypothesize that books with more canonical, or more demanding, textual profiles will not only have fewer ratings overall but will also exhibit higher rating distribution entropy or variance. in this sense, a strong textual effect might emerge that runs counter to -or nuancesthe "publicity effect". while the effect suggests that increased exposure leads to a wider range of opinions, the textual effect posits that inherent complexity can independently drive polarization, even in a smaller, more select readership. furthermore, we propose using rating distribution entropy as an alternative measure of literary judgment. this metric captures not only popularity or general preference but also the uncertainty or divergence in readers' evaluations. by investigating how this measure correlates with a suite of textual features connected to cognitively demanding textual profiles -such as perplexity, nominal ratio, and dependency distance -we seek to determine whether textual complexity itself plays a significant role in shaping reader disagreement. in doing so, our study endeavors to bridge the gap between traditional popularity metrics and nuanced literary analysis, ultimately providing a richer understanding of how textual characteristics influence reader reception. ## previous works goodreads' average rating has been employed in various studies as a proxy for reader appreciation @xcite @xcite , while rating count is often used to gauge the popularity of books @xcite . prior research has examined aspects such as goodreads' social function @xcite , its connection to offline literary culture @xcite , and cross-platform metrics @xcite . several studies suggest that these withinplatform metrics capture different forms of appreciation @xcite . for example, @xcite observed that winning a literary prize can lead to an increase in rating count alongside a decrease in average rating, possibly due to shifts in reader expectations. as such, while avg. rating and rating count usually exhibit a positive relationship @xcite , increases in audience polarization may change the relationship between the two metrics. similarly, @xcite demonstrated how amazon bestsellers receive more ratings on goodreads and have a higher entropy in their rating distributions, indicative of a more polarized audience. here, we refer to the phenomenon where increased popularity coincides with heightened disagreement as the "publicity effect". in addition, research into the relationship between textual features and reader responses has shown that books with more difficult or canonical textual profiles tend to be received in a more polarized manner @xcite . across different forms of appreciation too, canonical books tend to show a more diverse standing. for example, they often secure more literary prizes yet score lower on goodreads and are less frequently held in libraries @xcite . as studies consistently find that books associated with literary prestige display greater stylistic and syntactic complexity as well as higher information density @xcite @xcite , greater audience disagreement may be an effect of their textual complexity imposing a higher cognitive demand on the reader. for example, @xcite indicates that more challenging novels in terms of readability tend to garner less favorable success on goodreads. ## data we used two datasets of literary novels for our analysis: a larger dataset with only metadata and a smaller curated one with access to full texts for the examination of textual features. we restricted our study to the novel (i.e., not considering poetry or short stories) to maximize the comparability of our datapoints. 1 goodreads book graph dataset (@xmath0, 297). this dataset indexes the goodreads data of approximately 2 million titles and was compiled in 2017. 2 we used the metadata (not including shelving and reader interaction) and reduced the dataset significantly by removing anything not tagged as literary, a novel, and by removing titles with less than 10 ratings. 3 curated corpus (@xmath1, 939). to gauge the relation of goodreads data to textual features, we used a corpus for which we had access to the full texts of novels -a subset of what is known as the chicago corpus. the corpus indexes 9,089 english-language novels of various genres, published in the us between 1880 and 2000 and covers 3,150 authors (see ## text-extrinsic relations 4.1 relation between goodreads metrics we show the correlation between goodreads metrics in the large dataset in fig. 1 . we do not find a correlation between rating count and avg. rating, suggesting that books that are popular in the sense that they are rated more often do not also receive a higher score. this supports h2, i.e., that the relationship between avg. rating and rating count decouples -perhaps as the audiences become more polarized due to a "publicity effect". in fact, we do see a moderate correlation between the entropy of the rating distribution of books and the number of ratings (ρ .22). in other words, books that are rated more often -i.e., are more disseminated or popular -also have a higher diversity in the rating they receive, suggesting a larger but more uncertain audience, in support of h1. more rated books also tend to have a more uncertain reception, speaking for a "publicity effect". moreover, we see that avg. rating has a robust negative correlation with rating distribution entropy (ρ -.77), suggesting that raters seem to agree more on high values (for the distribution of both entropy and avg. rating, see appendix, figs. 7 8 ). for the curated corpus (fig. 2 ), we see a similar correlation between rating distribution entropy and avg. rating (ρ -.54). however, we do not see a correlation -or a very weak one -between the entropy of rating distribution and rating count (ρ .09). this lack of correlation suggests that a "publicity effect" may not be as visible in a highly curated corpus, where all books may be above a certain threshold of popularity already. moreover, we see another discrepancy observed between the correlations of the large dataset and the curated corpus, namely that we here do see a correlation between rating count and avg. rating (ρ .37), suggesting that the amount of ratings given is often accompanied by higher scores. ## text-intrinsic relations our analysis reveals a complex interplay between intrinsic textual features and reader responses as captured by goodreads metrics. in particular, we find that measures of stylistic and syntactic complexity are strongly associated with the variability of readers' evaluations, thereby offering insight into the underlying cognitive and interpretive processes foot_4 involved in literary appreciation. we highlight some of the relationships between text complexity and varied reception observed in figs. 5 6 . note that we might expect a diachronic change here, i.e., older books could be more challenging for modern readers and language models, potentially affecting human scoring and perplexity computed by lms. we checked for a difference in the correlations by comparing the full corpus to a smaller set of the last 50 years of the corpus @xcite . the correlations between features of textual complexity and goodreads metrics remain similar in both sets (full and recent set), i.e., correlations observed in the full set either remain or increase in the set of more recent novels. perplexity even shows an increase in its correlation with rating distribution entropy and sd, so we might assume that a recency bias of the model does not significantly impact our results. results of the more recent subset of novels can be found in appendix a (figure 12 ). ## discussion & conclusion our analysis reveals important relationships between goodreads metrics, audience reception patterns, and textual features, showing how different dimensions of literary appreciation interact. at a large scale (book graph dataset), we observe the "publicity effect" suggested in previous studies @xcite , confirming our hypothesis (h1): books with higher rating counts consistently demonstrate more diverse audience opinions, as measured by increased entropy in their rating distributions (fig. 1 ). in other words, books with greater visibility encounter more heterogeneous evaluation. the lack of correlation between average rating and rating count in the large dataset confirms our second hypothesis (h2), indicating that books with higher visibility don't necessarily receive higher average scores. this decoupling suggests that popularity and appreciation represent distinct dimensions of literary reception. still, this pattern shifts in the smaller, curated corpus, where we observe a positive correlation between rating count and avg. rating, as well as a slighter correlation between rating count and avg. rating, likely reflecting the already-established status of works in this more curated corpus.