Institution Università degli Studi di Verona (University of Verona), Universität Basel (University of Basel)
Universität Basel (University of Basel)
Universität Basel (University of Basel)
Universität Basel (University of Basel)
Universität Basel (University of Basel)
University of Milano-Bicocca
Huygens Institute for the History of the Netherlands (Huygens ING) - Royal Netherlands Academy of Arts and Sciences (KNAW)
Georg-August-Universität Göttingen (University of Gottingen)
Universität Basel (University of Basel)
Institution Università degli Studi di Verona (University of Verona)
Over the last decades, with technological advancements such as growing digitalization and the development of social media platforms, the act of reading has transformed into an interactive experience (Cordón-García et al., 2013; Merga, 2015), where the Internet plays a key role (Murray, 2018). Social reading platforms like Goodreads and Wattpad are online environments where millions of people from all over the world come to share their love for the written word. Members come together to discuss what they have read and what they judge as good or bad literature, they recommend books to one another, and even try their hand at writing fiction.
While a growing number of studies have been dedicated to this phenomenon (Nakamura, 2013; Ramdarshan Bold, 2016), so far only a few have adopted computational methods (Faggiolani and Vivarelli, 2016; Thelwall and Kousha, 2017) and none has combined these methods with empirical approaches to the study of literature and its effects.
As the online environment is very different from the literary field as we know it, showing new types of complex interactions, we need to explore social reading and writing from both social and content perspectives. Social questions that should be investigated include interaction among users, questions of power (sources of literary authority), the effects on literacy and on reading behaviors, the changing system of social values (and of aesthetic evaluation). Content questions include questions about style, about the distribution and originality of comments, about the affective, reflective or social nature of content.
With this panel, we will showcase the potential of studying social reading through the combination of multiple and interrelated approaches: from purely statistical, data-driven, and stylometric analyses, through qualitative and quantitative surveys of key users and a theory-driven qualitative taxonomy of reading valuation, towards a combination of the empirical and the computational, supported by a sound theoretical/methodological awareness. The substantial variety of case studies in four languages (English, German, Italian, and Dutch) will reflect the diversity of social reading, which can and should be studied from multiple points of view as well as with an array of methodological tools.
With this abstract, we focus on
, where social reading takes the form of a “discussion in the margin” (Stein, 2010), i.e. where texts are commented upon by millions of users, paragraph by paragraph. A central goal of our study is to understand how the corpus of the most appreciated texts on
Wattpad is different from the canonical corpus of Western literature. For a preliminary analysis, we focused on the categories of “Classics” and “Teen Fiction” (TF).
Analysis I. For a first understanding of readers’ engagement, we examined the progression of the number of comments over the 20 most-commented books for each genre. Figures 1 and 2 show that TF is more stable, whereas for many Classics the majority of comments is on the first chapters. Also the total numbers are very different: 2,569,405 comments for the first TF title and just 42,013 for
Pride and Prejudice.
Figure 1. Progression of the number of comments over the 20 most-commented Classics books
Figure 2. Progression of the number of comments over the 20 most-commented TF books
Analysis II. For a deeper understanding of reader response, we adopted sentiment analysis and compared the “emotional arcs” (Reagan et al., 2016) of both paragraphs and comments. The analysis was performed using the
package on 6 books per genre: all details and limitations of the approach are described in (Rebora and Pianzola, 2018). Figures 3 and 4 show a better attunement between paragraphs and comments in TF: the highest Pearson's correlation (0.807; p-value < 2.2e-16) was reached by TF title #3.
Figure 3. Emotional arcs for paragraphs and comments of six Classics books
Figure 4. Emotional arcs for paragraphs and comments of six TF books
Analysis III. To explore the relations between users while reading the books, we adopted network analysis (Scott, 2017) based on a very simple rule: the more two users reply to each other’s comments, the stronger is their connection. The visualizations were realized through the
platform. Figures 5 and 6 show the networks of the most commented Classics and TF titles. To make the samples comparable, the networks were reduced to the 1,000 strongest connections. As evident, Classics readers tend to group in a single cluster, while TF readers group in multiple sub-clusters. This phenomenon seems to suggest that reading classics enhances the formation of a more homogeneous community.
Figure 5. Network graph of the 1,000 strongest connections between
Pride and Prejudice readers
Figure 6. Network graph of the 1,000 strongest connections between
The Bad Boy’s Girl readers
Analysis IV. To visualize
Wattpad’s user distribution geographically, we analyzed a sample of 300,000 user profiles. Out of these, only 34.3% provide locations, many of which are fictitious (e.g. “Hogwarts” and “Wonderland”). We converted these locations into standardized state names with the help of the
Maps Place Autocomplete
API. Notwithstanding some errors (e.g. “Hell” was located in Denmark), the analysis provided confirmation to the supposed relevance of states like India and Philippines (Miller, 2015), together with the USA (see Fig. 7).
Figure 7. Geographic map of Wattpad users
All these analyses are to be considered as preliminary to a more extensive and detailed study, but they efficiently showcase the high potential of investigating the social reading phenomenon through visualizations.
Wattpad data urge us to rethink concepts like "world literature" and its dynamics. Of course, we cannot generalize these findings for books circulating through traditional publishing channels, but we can gain interesting insights for a more in-depth critical reflection on publishing and reading in the 21st century.
Sources of authority in online book reviews
It is a commonplace to state that with the advent of Amazon, book blogs and readers’ communities the role of traditional authorities in the literary field has become less important (McDonald, 2007). Rather than following the lead of professional critics, teachers or professors, readers are said to take advice from fellow readers. In my contribution to this panel, I will look at the evidence that a corpus of online Dutch book reviews can provide for answering the question which persons or institutions are considered authoritative by online reviewers.
In a pilot investigation, we have looked at references to possible authorities from a number of domains: traditional critics, newspapers, prizes, television programs, the book trade (publishers, booksellers, libraries), authors, teachers, websites and private contacts.
Reviews were downloaded from some prominent weblogs, some dedicated (mass) review sites, the online magazine 8weekly and, for contrast, the most prominent Dutch newspaper NRC (Boot, 2013). We used collections of search terms and regular expressions to search the downloaded reviews using AntConc. Irrelevant hits were removed manually. A subset of the remaining results was annotated and assigned a
type (type of person or institution that is assigned authority),
role (role of reference to authority in review, e.g. the authority is mentioned as supporting the reviewer’s view or as the one who advised the reviewer to read the book) and
agreement (whether the reviewer accepts the view of the authority). In all 1518 references to some form of authority were annotated. Because of many practical limitations and ad-hoc decisions, the procedure won’t give us any firm numbers but it does give a clear indication of sources of authority that are recognised in the online domain.
Figure 1. Main sources of authority in online reviews
The main findings are summarized in Figure 1. The main sources of authority in the online reviews are companies (publishers and bookshops) and literary prizes. Authors (of the reviewed book or others) are also important. Online critics (‘peers’) are not unimportant, but print critics are hardly mentioned. Personal contacts (family and friends) are more important than print critics. Frequently, people refer to a medium rather than to a critic by name, but here too, online media are more often mentioned than print media. Teachers are almost invisible.
Much can be said about this. For one thing, we counted all references equal but some will weigh heavier than others. There are also important differences between the online platforms. The collection we used did not include reviews from booksellers’ sites. For now, though, the most important conclusion is that no, traditional critics are not highly valued on online platforms; however, the beneficiaries are commercial companies rather than fellow users of online platforms.
Classifying the style(s) of criticism. A computational analysis of Italian book reviews
In this abstract, we use a corpus of book reviews (Salgaro and Rebora, 2018) to answer the following question: how do professional critics, journalists and passionate readers differ in the writing of reviews and what features can be used to identify them?
The corpus is divided into three subsets: reviews published on social reading platforms (source:
aNobii), in paper magazines (
Il Sole 24 Ore), and in scientific journals (
Osservatorio critico della germanistica, and
OBLIO). All sub-corpora have an approximate size of 650,000 tokens.
First, we adopted stylometry to classify the texts. As demonstrated by (Eder, 2015), the first element to influence the quality of a stylometric classification is text length. Preliminary tests—ran with the
Stylo R package (Eder et al., 2016) on 5,000-word-long text chunks—showed how Cosine Delta distance, based on just 50 MFW, was able to almost perfectly separate the three subgroups (see Fig. 1). Considered the high variance of text length in our corpus (mean = 259 words; SD = 363 words), we artificially generated a series of sub-corpora composed by text chunks of the same length (varying between 50 and 5,000 words) and we evaluated clustering quality through the adjusted Rand index in the
PyDelta Python library. Figure 2 confirms how Cosine Delta distance with 2,000 MFW is the best performing classifier (Evert et al., 2017), but also 200 MFW (i.e., mainly function words) reach a similar—and, in some cases, even better—efficiency. As for text length, clustering quality is quite poor below 1,000 words, while a plateau is reached at about 3,000 words.
Figure 1. Network graph of the corpus (Cosine Delta, 50 MFW, ForceAtlas2 in
Gephi). In green:
aNobii; in violet:
Sole 24 Ore; in red:
Figure 2. Clustering quality per slice length and MFW used (distance: Cosine Delta)
To improve the results for shorter chunks, we developed a framework for a machine learning classifier, by operationalizing a series of traditional definitions of literary criticism (e.g. Eco, 1979; Gardt, 1998; Rodler, 2004; Colussi, 2017). An extensive lexicon of literary criticism (Beck et al., 2007) was translated into Italian; selections of terms related to mental imagery and emotional aesthetic response were extracted from questionnaires and tools in empirical aesthetics (e.g. Knoop et al., 2016; Fialho et al., in press), translated into Italian, and expanded through the
fastText Italian word-embedding model (Joulin et al., 2017). These resources—together with selected features in the
LIWC Italian dictionary—were used to measure emotional and cognitive involvement with the reviewed text. The measurements were combined with the results of the stylometric analysis (Cosine Delta, 2,000 MFW) and used to train an SVM classifier.
For a corpus composed by 500-word-long chunks (the length of this abstract), the sole stylometric analysis reached an attribution accuracy of 90.1%, while the SVM classifier scored 93.2%. A slight but promising improvement, if we consider the simplicity of the framework—that can and should be refined further.
With this paper, we hope to have cast the groundwork for a research that might fruitfully combine computational methods and literary theory to study the “style of criticism” of professional and non-professional readers.
Shared Reading. Digital Reading and Writing of Literature
In the digital age, the practice and habits of reading change fundamentally. Not few speak of the end of the book and of (deep) reading (e.g., Wolf 2007). However, and contrary to this popular statement, reading and writing literature has never been practiced as intensely as within digital societies (Lauer 2018, 2019) – a development that involves in particular young, adolescent readers.
In preliminary studies, we categorized various reading and writing platforms and conducted a first study on Harry Potter-fanfiction, using a personality questionnaire (Paulus 2016). Results indicate a dominance of romance as well as of fantasy genres (see also Odag 2008) and that authors of fanfiction are usually between 14 and 20 years old. They show both introverted and extroverted personality traits and come from diverse educational backgrounds. Generally, they can be considered rather empathetic. In a similar vein, other reading research suggests that dealing with literary texts increases performance in Theory of Mind, and thus might the support the development of prosocial skills such as empathy (Kidd & Castano 2013).
Despite justified criticism on over-generalizations (e.g., van Kuijk et al. 2018), research on the emerging changes towards digital, joint reading of texts with presumably much lower literary demands and its potential implications is missing. This is the more surprising, since first explorative studies in schools indicate that digital formats can be used very well for education through writing and reading (Bertschi-Kaufmann & Graber 2007, Wampfler 2016) and not only shed light on, but also foster a variety of effects. Nevertheless, it is still unclear how and with what consequences digitization affects the reading behavior of adolescents, including complex processes such as (higher) language acquisition, establishments of social peer-groups and their interactions, as well as their understanding and appreciation of literature and the written word.
Our project focuses on the changing social processes that accompany the reading and writing of literature in the digital age. In doing so, we focus on the young readers and their distinct reading and writing socialization on digital reading (and writing) platforms such as Wattpad. Based on a social interactionist-reading model and the theoretical concept of “scaffolding” (see, for example, Bruner 1983, Nation 2018, Quasthoff 2011) the project integrates descriptive, qualitative and quantitative methods in a multi-methodological approach.
We present a questionnaire that combines established scales from social psychology with items used in reading research to explore online and offline reading habits. Using new methods of field research enables a better understanding of how intensively young readers think and feel about reading and writing literature and what kind of values are used to talk about literature in social media. To arrive at a more comprehensive picture of reading in the digital age, we provide a general theoretical framework that helps to integrate the thus acquired data.
Where’s your attention? An empirical assessment of Web 2.0 users’ literary values
J. Berenike Herrmann
Traditionally, reading fiction has been seen as a tool for honing crucial sense-making capacities, enabling an integrated sensual and intellectual personal development (
judicium sensitivum, Wolff, 1738; cf. Lauer, 2012). Web 2.0 users, on the other hand, are understood not so much as interested in the challenges and pleasures of literature-as-art, but in the easy gratification of popular genres, driven by an economy of attention (Franck, 2004). Users’ book reviews are held to show an affirmative bias, documenting the lack of a deep and discerning reading engagement (Ingold, 2014). Mirroring a stated trend towards a “digestible” documentary and authentic literature (Röhricht, 2016), lay reviewers are held to operate with a heavy content bias and to neglect formal criteria.
However, many of these verdicts lack comprehensive empirical support, leaving questions such as the following ones open: What do web 2.0 readers actually do when judging literary value? What are the grounds for value judgements? Do readers apply aesthetic and ethical premises? Do they prioritize content over formal criteria? In our paper, we will scrutinize a corpus of lay literature reviews published on the German reading platform lovelybooks.de for the premises underlying users’ valuation statements.
Using sentiment analysis and rule-based techniques as a bootstrap methodology for semi-automatic identification of valuation statements in our corpus (ca. 1 Mio reviews), our exploratory case study analyzes the lovelybooks.de categories ‘classics’ and ‘novels’ Combining quantitative and qualitative methods, we examine how users evaluate literary texts in terms of quality criteria such as subjectively perceived effects, but also more formal ones, including authorial style, literary character motivation and plot construction (motivation, narrative arc, suspense).
In addition to semi-automatic coding and running KWICs of recurrent phrases and keywords, we analyze a number of reviews as integral cases. How do users develop their (subjective) assessments? How do assessments appear in terms of tone and habitus? How do users prioritize the different categories for their evaluation? The qualitative coding is aimed at fine-tuning a coding schema that adapts the axiological model by Heydebrand & Winko (1996) for web 2.0 lay reviews.
Our first results do indeed point to users’ heightened subjectivity and a tendency to plain statements, reporting subjectively perceived effects (
a bit too melancholic, I found it a pity) and marking taste (
I liked it well). Yet, we found considerable attention to formal aspects such as plot construction (
some of the events were not plausible to me) and character motivation (
the characters were well crafted), as well as critical (not only affirmative) judgements. What is more, our results indicate that many users do apply a plain and subjectively toned language, but handle a reflected taxonomy of value criteria to support their taste judgements, expressed by statements such as
Noch schwerer wog bei der Sternevergabe … [The awarding of stars was even more influenced by…],
Ein Punkt, der mir sehr wichtig ist [A point that is very important to me]. We thus suggest that instead of propelling a decline of intellectual scrutiny and differentiation, online reading platforms offer a potential for personal insight and development. Our study is one of the first few data-driven approaches to scrutinize the literary values underlying people’s engagement with literary texts in a participatory culture.
Our research aims to complement computational linguistics with methods used in empirical literary studies to investigate the experience of “getting lost in a book”. Using software from the field of natural language processing, we match the 18 statements from the Story World Absorption Scale (SWAS, cf. Kuijpers et al., 2014) – a questionnaire used to identify absorbing reading experiences – to reader reviews posted on the online platform
. The SWAS taps into four different aspects of absorbed reading, namely sustained concentration (Attention), vivid imagery of the story world (Mental Imagery), feelings of empathy or sympathy for the characters of a story (Emotional Engagement) and the sensation of having made a deictic shift from real world to story world (Transportation). The reader reviews on Goodreads more often than not include descriptions of people’s experiences with certain books and evaluations of their reading experiences, and therefore it could be argued that they fall somewhere between accounts of reader response and literary criticism. They certainly constitute a rare treasure trove of qualitative, user-generated data on reading and reading evaluation.
The aims of our project are twofold: 1) validating the SWAS, and 2) enabling comparative analyses of absorption across different books, genres, and reader groups.
We have performed a manual analysis on 180 Goodreads reviews of three contemporary blockbuster novels (representative of the Fantasy, the Romance, and the Thriller genre), confirming that, in many cases, SWAS statements and particular sentences in Goodreads reviews overlap substantially.
writes: “I’m so absorbed in the world Martin produced out of his wits” (a sentence that matches with SWAS statement A3: “I felt absorbed in the story”); while
expresses her identification with the main character: “I went through all the emotional ups and downs right along with her” (matching with EE4: “I felt how the main character was feeling”). A total of 130 matching sentences were identified (for details, see Fig. 1).
Figure 1. SWAS/Goodreads matches.
EE1-EE5: Emotional Engagement,
MS1-MS3: Mental Imagery
In order to extend the analysis to the entire Goodreads corpus, which collects about 80 million book reviews, we combine two technologies: textual entailment detection software, i.e., EOP (Magnini et al., 2014) and text reuse detection software, i.e., TRACER (Büchler et al., 2018). Preliminary experiments (Rebora et al., 2018) show that both tools need adaptation and training for this specific task, as our best “out of the box” recall score is 0.28, while training on the manually-annotated reviews increases recall to 0.49.
Based on the 130 identified sentences plus the 18 SWAS statements, we defined a provisional “absorption lexicon” and expanded it through a word-embedding model (Mikolov et al., 2013) based on 2.5 million reviews on Goodreads (total tokens ~ 400 million). Figure 2 shows a synthetic representation of this lexicon.
Figure 2. Absorption lexicon (word dimensions symbolize their weights).
The lexicon was used to identify, through a standard “bag-of-words” approach, the reviews that showed the highest levels of absorption. The results, while significant in themselves (see Figs. 3 and 4 for sample visualizations), are used as the starting point for extensive semi-automatic annotation work – with four annotators working in parallel for a total of 18 months, starting in December 2018. Our goal will be that of producing a ground truth corpus as training data for machine learning algorithms, towards a fully-automated matching of Goodreads reviews with the different aspects of absorbed reading.
Figure 3. Network graph based on the absorption scores for the four SWAS categories.
Figure 4. Zoom of Fig. 3.
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