=Paper=
{{Paper
|id=Vol-3834/paper23
|storemode=property
|title=Catching Feelings: Aspect-Based Sentiment Analysis for Fanfiction Comments about Greek Myth
|pdfUrl=https://ceur-ws.org/Vol-3834/paper23.pdf
|volume=Vol-3834
|authors=Julia Neugarten,Tess Dejaeghere,Pranaydeep Singh,Amanda Robin Hemmons,Julie M. Birkholz
|dblpUrl=https://dblp.org/rec/conf/chr/NeugartenDSHB24
}}
==Catching Feelings: Aspect-Based Sentiment Analysis for Fanfiction Comments about Greek Myth==
https://ceur-ws.org/Vol-3834/paper23.pdf
Catching Feelings: Aspect-Based Sentiment Analysis
for Fanfiction Comments about Greek Myth
Julia Neugarten1,∗ , Tess Dejaeghere2,‡ , Pranaydeep Singh3,‡ ,
Amanda Robin Hemmons2 and Julie M. Birkholz2,4
1
Arts and Culture Studies, Radboud University, The Netherlands
2
GhentCDH-Ghent Center for Digital Humanities and Department of History, Ghent University, Belgium
3
LT3 -Department of Translation, Interpreting and Communication, Ghent University, Belgium
4
KBR- Royal Library of Belgium, Belgium
Abstract
The application of sentiment analysis in literary studies has been limited and often criticized, yet aspect-
based sentiment analysis (ABSA) offers interesting applications in this domain because it addresses some
limitations of traditional SA tools and provides a more detailed and context-sensitive analysis of sen-
timent. To investigate its usage in literary reception studies, we apply ABSA to a corpus of ±25,000
comments written by readers in response to fanfiction about Greek mythology on fanfiction website
Archive of Our Own (AO3), one of the largest platforms for fanfiction in English. Our ABSA pipeline
detects sentiment (positive/negative) associated with eight aspects of fanfiction stories (general eval-
uation, Greek mythology, character, character emotion, reading experience, writing style, events and
storyworld, and non-specific sentiment). We explain the process of data collection and annotation and
present a small inter-annotator agreement study (Pairwise Cohen’s 𝜅 0.86 for aspects and 0.88 for senti-
ments). We develop, evaluate, and fine-tune a machine-learning pipeline for ABSA, tackling the aspect
extraction and sentiment analysis tasks respectively. We obtain the best results using NuNER for as-
pect extraction (0.5 macro F1) and Twitter-roBERTa-sentiment for sentiment analysis (0.75 macro F1).
Finally, we outline some avenues for future research and reflect on the generalizability of our method
to other domains, especially to fanfiction from other fandoms and platforms but also other social media.
Keywords
aspect-based sentiment analysis (absa), online discourse, fanfiction, reader response, classical reception,
web data
1. Introduction
Fanfiction – stories inspired by existing works, written by and for an audience of fans, and
published for free online – is a burgeoning domain of online cultural activity. Archive of Our
Own (AO3), one of the largest English-language websites for publishing and reading fanfiction,
recently reported hosting over 13 million works [31]. Fan studies scholars emphasize the need
to understand fanfiction in relation to its community contexts, because fanfiction is usually
CHR 2024: Computational Humanities Research Conference, December 4–6, 2024, Aarhus, Denmark
∗
Corresponding author.
†
These authors contributed equally.
£ julia.neugarten@ru.nl (J. Neugarten); tess.dejaeghere@ugent.be (T. Dejaeghere); pranaydeep.singh@ugent.be
(P. Singh); amanda.hemmons@ugent.be (A. R. Hemmons); julie.birkholz@ugent.be (J. M. Birkholz)
ȉ 0000-0003-3314-9445 (J. Neugarten)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
217
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
written ”within and to the standards of a particular fannish community [9]” and ”is often so
deeply embedded within a specific community that it is practically incomprehensible to those
who don’t share exactly the same set of references [35].” As such, ”a fan fiction story cannot be
viewed as a wholly self-contained object, a text delimited by the first and last words that appear
on the screen, the way that readers of novels and other genres of print culture conventionally
read books as bounded by their covers [11].” Due to the deeply intertwined nature of fanfiction
texts and the shared practices and contexts of their online community – which fans call fandom
– fanfiction texts must be approached ”as both literary and digital objects [4].” Scholars need
methods to study the ways fanfiction is received, embedded and evaluated in online spaces like
AO3 that account for its hybrid literary/digital nature.
Additionally, fanfiction often revolves around emotional trajectories. Love, heartbreak, men-
tal illness and other emotional highs and lows are frequently central to fanfiction, which has
been described as ”emotional landscapes of reading [33]” both because of its intradiegetic em-
phasis on emotionality and because of its capacity to evoke strong emotions in readers.
The comment-section on AO3 lets readers share their affective responses to fanfiction read-
ing, which we conceptualize as sentiments in the current paper. Sentiments can develop over
the course of the reading and may extend or endure into the time after. Through comments,
readers express these sentiments in the social context of their fan community. Sentiments are
important for understanding which aspects of fanfiction are loved, enjoyed, and appreciated.
The hybrid digital/textual nature of fanfiction comment, the enormous scale of fanfiction and
comment production and exchange as born-digital objects, and the fanfiction community’s em-
phasis on both intradiegetic and extradiegetic emotion make fanfiction comments suitable for
computational approaches such as sentiment analysis (SA).
The application of Natural Language Processing (NLP) methodologies to date in literary
studies has been limited [5, 23]. Specifically, SA, which categorizes texts as positive, neutral, or
negative, is often criticized and considered inadequate for the detail-oriented research needs
of literary scholars [20, 34]. The sentiments present in literary or narrative text often cannot
be reduced to a negative/positive binary. To address some of the limitations of existing SA
tools, aspect-based sentiment analysis (ABSA) has gained attention. Unlike conventional SA
that labels the sentiment of entire documents, paragraphs, or sentences, ABSA operates at
the aspect level, extracting specific aspects and their corresponding sentiment polarities [3,
36]. Although ABSA offers a more granular approach to sentiment mining, its application has
largely been confined to commercial domains such as customer reviews [36] and applications
in (computational) literary studies still need to be explored.
To fill these research gaps – the application of ABSA to literary studies data and the use of
NLP to analyze textual/digital fanfiction comments – we employ ABSA to detect the aspects
of fanfiction that readers refer to when writing comments and the sentiments commenters
attach to these aspects, conceptualized in a binary fashion (positive/negative). We address a
methodologically-oriented research question about fanfiction reception on AO3:
• What are the affordances and limitations of aspect-based sentiment analysis (ABSA) for
analyzing fanfiction comments?
In other words, we investigate whether ABSA is an effective approach to analyzing the
aspect-sentiment combinations in a dataset of fanfiction comments. We limit this research
218
Table 1
Descriptive statistics of character- and wordcount in the comment-corpus
stat characters words
mean 210.11 37.91
std 388.78 68.25
min 1 1
25% 44 8
50% 101 18
75% 223 40
max 9,088 1,659
sum 5,456,580 984,400
to one platform (AO3) for reasons of scope. We focus on the Greek mythology fandom be-
cause Catching Feelings is part of the larger project Anchoring and Innovating Greek Myth in
Contemporary Online Fanfiction (2022-2026), which focuses on the way contemporary online
fanfiction transforms cultural material about Greek mythology and the way this cultural mate-
rial resonates with online fan communities.1
Section 2 outlines the processes of data collection and annotation. Section 3 describes our
ABSA pipeline. Section 4 reports on the inter-annotator-agreement of the annotated dataset,
and presents the results of various ABSA pipelines. Section 5 examines these results. Finally,
Sections 6 reflects on the reproducibility, generalizability and limitations of our approach to
ABSA for fanfiction comments and outlines some avenues for future research, and in Section
7 we answer our research question.
2. Data
2.1. Data Collection
We based our comment dataset on MythFic Metadata [29], an existing collection of metadata
for 5.155 works of fanfiction about Greek mythology that were published on AO3 between 2008
and 2022.2 We used the list of unique identifiers for the stories in MythFic Metadata as input for
the AO3-api [14] to collect comments on those stories. The resulting dataset contains 25.970
comments. Table 1 provides descriptive statistics of the comments in terms of character- and
word count.
We must address three limitations of this dataset. First, fanfiction comments, like any type of
textual review or comment, do not fully capture the complex mental and emotional processes
of reading experience and evaluation. Instead, these comments reflect only the elements of
the reading experience that commenters are consciously aware of and chose to comment on,
in the way they chose or were led by discursive norms to represent these elements textually.
Second, commenters are only part of the users of AO3, and may not be representative of the
reader reception happening all over the platform, let alone the fanfiction reception happening
1
To find out more about this project, visit the Anchoring Innovation website.
2
2008 is the year that AO3 went into open beta.
219
on other platforms. In a 2013 census of AO3, 43.6% of respondents reported commenting on
stories they had read (n = 4,358) [7]. When MythFic Metadata was collected, 1,584 stories in the
fandom Ancient Greek Religion and Lore, or 30.7%, of the fandom’s works, had not been com-
mented on at all. Additionally, commenters can be characterized as above-averagely engaged
or committed readers, since they invest the time and effort to comment. Finally, the content
of fanfiction comments is co-shaped by the culture and affordances of AO3. Like with online
book reviews, social factors like ”the interactive nature of the reviewing platform or reviewers’
desire to cultivate a persona or gain followers [22]” influence these texts. Additionally, fan
communities differ between platforms because ”the relationship that fans have to technology
and online platforms is integral to the culture of these communities [13].” The exact dynamics
of such relationships remain largely unknown and so the generalizability of our findings to
other platforms may be limited.
2.2. Annotation
Of the 25,970 comments, 25,240 contain text and 730 contain only emojis. The emoji-only
comments were discarded in our subsequent analysis. From the textual comments, we created
an annotated subset using the following criteria:
• Length: 100 - 4,000 characters.3
• Language: English as predicted by the Python package langid [25] with high confidence
(>0.9).
From the remaining 12,866 comments, we randomly sampled 1,000 to create the annotations.
Following existing research, we define sentiments as ”social constructs of emotions that
develop over time and are enduring [10].” Since we conceptualize aspects here as particular
features of a fanfiction story that elicit sentiments, it becomes possible to identify and com-
putationally extract positive and negative sentiments as they relate to particular aspects of
fanfiction reception.
To develop an annotation guide we conducted exploratory analysis of the comment data,
such as an analysis of the most-frequent noun chunks as detected by SpaCy [17] and a topic
model of longer comments (>300 characters) created with Top2Vec [1]. Based on these explo-
rations, we formulated a scheme of 8 aspects of fanfiction that were frequently commented on.
Table 2 provides examples of each aspect-category. Following the methodological principle of
ethical fabrication [19] these examples were fabricated to protect the privacy of commenters
while still mirroring as closely as possible the content of the data. Some aspects were chosen
bottom-up, i.e. based on the exploratory analysis, and others were chosen top-down, i.e. based
on aspects of comment-data that interest us.
The eight resulting aspect-categories are:
• Canon: reference to how fanfiction transforms, critiques, or engages with canonical ma-
terial, including references to Greek mythology but also source materials from popular
culture.
3
Shorter comments convey little to no evaluation, longer comments tend to go off-topic or quote extensively from
the fanfiction. The length-limitation for writing comments on AO3 is 10,000 characters.
220
Table 2
Aspect Example Sentences
aspect example sentence
NULL ‘OMG amazing!!’
canon ‘a great retelling of The Iliad.’
character ‘Achilles is my favorite.’
emotion ‘aawh it’s great to see Odysseus happy.’
events and storyworld ‘the Underworld is soooo spooky.’
general ‘Great story!’
reading experience ‘I couldn’t stop reading this.’
style ‘Your writing style is wonderful.’
• Character: reference to and assessment of character, characterization, character appear-
ance and relationships between characters from the story.
• Emotion: references to the emotions experienced by characters intradiegetically.
• Events and Storyworld: reference to plot events and settings, specific scenes, world
building, story content like tropes, and general plot elements like twists or endings.
• General: reference to the story as a whole or in general terms.
• Reading Experience: reference to reading experience, such as emotional engagement
(of the reader, not the characters), absorption or narrative tension.
• Style: reference to how the story was written down or rendered, including writing style,
word choices, metaphors, turns of phrase, voice, perspective.
• NULL: expressions of sentiment that do not refer to specific words or aspects.
One central aspect of fanfiction comments we exclude is expressions that foster a bond with
the author. Because fanfiction exchange relies on a gift economy [16], expressions of grati-
tude, appreciation and encouragement are an important part of commenting, and phrases like
”Thank you for writing this” or ”Please write more!” occur very frequently. An existing LDA
topic model of fanfiction comments showed that ”author encouragements” and ”requests for
story” were among the most prominent topics in comments on another fanfiction website, Fan-
fiction.net [26]. However, because these types of expressions do not explicitly evaluate aspects
of stories themselves, but rather relate to the status and position of authors and readers in the
community, we disregard them here.
We used INCepTION [21] to annotate (see Figure 1 for an example). The first author anno-
tated all 1,000 comments in the annotation-set. One co-author annotated an overlapping 100
comments to determine inter-annotator agreement (Section 4.1).
3. Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is a type of information extraction (IE) task consisting
of two subtasks: aspect extraction (AE) and sentiment analysis (SA) of the sentiments associ-
ated with these aspects. Approaches to these tasks in and beyond computational humanities
include rule-based models [22], machine learning models [3, 10, 36, 30] and, more recently,
221
Figure 1: Example of Annotation in INCepTION
generative large language models [12, 18, 8]. We selected a machine learning approach be-
cause (a) formulating domain-specific rules is time-consuming and would not generalize well
over other datasets, and (b) while approaches using generative Large Language Models (LLMs)
are currently in full development, the effects of their opaque nature, tendency to propagate
bias and hallucinate are not yet fully understood in IE-applications for the literary domain
[12]. However, exploring the use of LLM’s for ABSA presents an interesting avenue for future
research.
Given the evaluative nature of the comment section on AO3, ABSA could be a good fit for
assessing the sentiments of fanfiction readers on specific aspects of these texts. At the same
time, the strong influence of subcultural community norms on the description of aspects and
the expression of sentiments in these comments may make it challenging to apply an out-of-
the-box method.
4. Results
4.1. Evaluating the Annotations
Before training a model on our annotations, we conducted a small-scale inter-annotator agree-
ment study of the 100 comments annotated by both annotators. Our inter-annotator agree-
ment was exceptionally high: pairwise Cohen’s 𝜅 of 0.86 (aspects) and 0.88 (sentiments). This
is likely due to the intensive collaboration and discussion between the annotators on the an-
notation guidelines and because of our shared familiarity with the discursive and social norms
of fanfiction communities. The only categories showing notable confusion were Events and
Storyworld and Character, with 9 instances of confusion between annotators. This is some-
what to be expected since characters often participate in story-events and always exist in the
storyworld, so the distinction between these aspects can be blurry. Both annotators found a
clear prevalence of positive evaluations (n=206 agreed occurrences in the annotation-set) of
aspects over negative evaluations (n=4). This aligned with our expectations based on the ex-
ploratory data analysis and on a review of the existing literature, where research has shown
that fanfiction comments are often written in a ”positive style [28].”
222
Table 3
Annotation Frequencies for Aspects
aspects frequency inter-annotator set frequency full set
NULL 173 736
canon 87 328
character 419 1.061
emotion 60 143
events and storyworld 136 550
general 229 849
reading experience 97 521
style 87 406
Table 4
Annotation Frequencies for Sentiments
sentiment frequency inter-annotator set frequency full set
NULL 12 39
negative 39 146
positive 768 3,532
Table 5
Evaluation of Aspect-Extraction Models
model F-score (micro) F-score (macro) accuracy
roBERTa-base 0.30 0.25 0.18
Twitter-roBERTa-sentiment 0.35 0.31 0.22
NuNER 0.50 0.45 0.34
NuNER (split task) 0.50 0.50 0.34
4.2. Results of the Aspect Extraction
We tested three models for joint aspect extraction and categorization: roBERTa-base [24]
(Macro F1 0.25), Twitter-roBERT-sentiment [2] (Macro F1 0.31), and NuNER [6] (Macro F1 0.45).
Evaluation metrics for each of these models are listed in Table 5 and per aspect in Table 7. Since
NuNER performed best, we tried to improve its results further by splitting the tasks of aspect
extraction and categorization, to minimal effect (from 0.45 to 0.50 Macro F1).
Note that the numbers for support for split- and joint task approaches are different because
in the split task, aspect extraction is a separate first step where some aspects may not have
been detected. Additionally, the support-numbers in Table 7 do not perfectly match the num-
ber of annotations reported in Table 3, because in a handful of instances the aspect-term in an
annotated sentence proved difÏcult to locate due to variations in spelling, punctuation, capital-
ization or even spaces.
223
Table 6
Evaluation of Sentiment Analysis Model
model F-score (micro) F-score (macro) accuracy
Twitter-roBERTa-sentiment 0.95 0.75 0.96
4.3. Results of the Sentiment Analysis
We used the Twitter-roBERTa-sentiment model since it has already been fine-tuned for senti-
ment detection, so we were expecting it to perform well on a polarity detection task. These
results were decent (0.75 macro F1) (Table 6). This may be because sentiment polarity does not
deal with as many categories as the aspect-task (two instead of eight), and because the model
has been trained on Twitter-data, which is in some ways similar to our dataset of fanfiction-
comments. Both types of texts are relatively brief, use internet jargon and often convey strong
sentiments or opinions.
5. Analysis
Based on these results, the affordances and limitations of ABSA for analyzing fanfiction com-
ments seem strongly differentiated by aspect category. As expected, the models perform best
for those aspect categories that have high support in the annotations, especially General and
Style. Emotions are particularly problematic, perhaps due to their low support but perhaps
also because emotions are not commonly considered entities or aspects in NER-tasks.
Figure 2 is a confusion matrix between the best-performing aspect-extraction model
(NuNER) and our gold standard annotations. As you can see, the only significant source of
confusion was between Character and Event & Storyworld aspects, which makes sense as
characters usually participate in events and interact with story worlds. Entities that the model
failed to detect were left out of Figure 2 for the sake of interpretability. Manual inspection of
these left-out results reveal that many Events were not detected. Emotion was also difÏcult
for all models to detect, perhaps because words that refer to the Emotion-aspect also often
occur in descriptions of Reading Experience. Detection of Canon was often partial, but
detecting these partial matches (often revolving around words like ‘retelling,’ ‘original’ ‘allu-
sion’ or ‘portrayal’) is still useful in mapping on a large scale how Canon is discussed in the
comments. Characters that were very frequent in the data (such as Achilles) were more often
accurately detected than less prominent characters (such as Demeter). General aspects were
captured well in most models, with the exception of some relatively long and unique figures of
speech, such as ‘I take my hat off to you.’ Non-specific expressions of affect were also detected
relatively well, including the kinds of borderline-incoherent expressions that are specific to
fanfiction communities. Examples of these include ‘WAHHH’ and ‘HNNG’. NuNER even iden-
tified ‘skdjahskdjhsd’ as a NULL-aspect; correctly, in our opinion, as keyboardsmashes often
convey strong but non-specific sentiments.
Figure 3 is a confusion matrix between the two annotators, for whom the Character and
General aspects were easiest to agree on. With the General aspect, then, the skills of hu-
224
Table 7
Evaluation of Aspect-Extraction Models per Aspect
roBERTa-base precision recall f1-score support
character 0.18 0.33 0.23 110
general 0.64 0.72 0.68 112
event 0.04 0.08 0.05 66
NULL 0.24 0.31 0.27 71
reading 0.16 0.16 0.16 57
style 0.41 0.48 0.44 50
canon 0.16 0.21 0.18 39
emotion 0.00 0.00 0.00 11
micro avg 0.26 0.36 0.30 516
macro avg 0.23 0.28 0.25 516
weighted avg 0.29 0.36 0.32 516
Twitter-roBERTa-sentiment precision recall f1-score support
character 0.21 0.35 0.27 110
general 0.69 0.72 0.71 112
event 0.05 0.08 0.06 66
NULL 0.48 0.41 0.44 71
reading 0.31 0.19 0.24 57
style 0.42 0.46 0.44 50
canon 0.14 0.26 0.18 39
emotion 0.50 0.09 0.15 11
micro avg 0.32 0.39 0.35 516
macro avg 0.35 0.32 0.31 516
weighted avg 0.36 0.39 0.36 516
NuNER precision recall f1-score support
character 0.38 0.45 0.41 110
general 0.74 0.78 0.76 112
event 0.28 0.42 0.34 66
NULL 0.62 0.58 0.60 71
reading 0.29 0.32 0.30 57
style 0.63 0.74 0.68 50
canon 0.29 0.33 0.31 39
emotion 0.18 0.27 0.21 11
micro avg 0.46 0.53 0.50 516
macro avg 0.42 0.49 0.45 516
weighted avg 0.48 0.53 0.50 516
NuNER (split-task) precision recall f1-score support
micro avg 0.47 0.55 0.50 511
macro avg 0.47 0.55 0.50 511
weighted avg 0.47 0.55 0.50 511
225
man annotators overlap with those of NuNER. For Style, it seems that NuNER performed well
because Style is often described in the comments using a relatively specific and limited vocab-
ulary. In our test set, the Style tokens contained only 30 unique words, compared to 53 for
Emotion, 93 for Canon and 287 for Events & Storyworld. The most common words for Style
are also quite consistent and content-related, such as ’written’, ’writing’, ’describe’, ’language’,
and ’words. In comparison, the most common words detected in other aspect-categories are
often stop-words. This may make Style relatively easy to detect computationally.
With NuNER’s help, we also found some small errors in our annotations, such as character-
names that were overlooked in the part of the annotation-set that was only annotated by one
author. In future work, an annotation effort where each text is annotated by multiple people
may provide better results.
Figure 2: Confusion Matrix Gold Standard Annotation vs. best model (NuNER)
6. Discussion
The machine-learning approach to ABSA is more generalizable to other domains, especially
other fandoms, than a rule-based model would be. Particularly in the relatively successful
categories of General and Style, we expect no significant differences between fandoms in how
commenters refer to these aspects of fanfiction. We do expect, however, that our model will be
difÏcult to generalize across different platforms for fanfiction exchange, as research has shown
that ”our lack of knowledge about platform influence on norms of commenting makes it difÏcult
to generalize about the dynamics of fanfiction commenting beyond AO3 [28].” The importance
of subcultural norms in expressing the sentiments of members of fanfiction communities like
226
Figure 3: Confusion Matrix Between Two Annotators
AO3 may also make it difÏcult to apply our ABSA pipeline to other types of comments, such
as book reviews on Goodreads or comments on Youtube.
We want to highlight five potential directions for future research. Firstly, it may improve
results to annotate more data in a targeted way, supplementing those aspects for which support
was low. Secondly, it may be of interest to analyze the 730 comments containing only emojis,
using, for example, the Multidimensional Lexicon of Emojis [15]. Third, it may be interesting
to tackle the task of ABSA with a generative LLM, since these models are gaining ground as
information extractors. GoLLie [32] looks especially promising, as this prompting framework
would allow us to feed our existing detailed guidelines for annotation directly to a generative
model. Fourth, a hybrid approach to ABSA, particularly adding some rule-based elements
to our existing ML pipeline, may be a productive way to improve outcomes for some of the
aspects that were difÏcult to detect using our current setup. For example, we could use an
emotion lexicon like EmoLex [27] to detect the Emotion aspect and create lists of characters
and story settings based on existing metadata [29] to detect the Character aspect and the
storyworld-dimension of the Events and Storyworld aspect. Finally, a fruitful next step in
the field of fandom studies would be to examine the outcomes of our current best model in more
detail to generate domain-specific insight into the ways sentiment is expressed and attached
to particular aspects of fanfiction in the comment data.
227
7. Conclusion
NuNER delivered the best performance at our aspect-extraction task. Although results were
poor for some aspects (especially Emotion but also Events & Storyworld), other aspects (es-
pecially General and Style) were more accurately detected. Furthermore, since the model
detected many partial matches in the aspect extraction for aspects such as Canon, results are
still very useful to researchers wanting to know which aspects are mentioned in connection
to positive and negative sentiments in the fanfiction comment data. Our approach of using
Twitter-roBERTa-sentiment proved sufÏciently suitable to detecting positive and negative sen-
timent. Overall, fanfiction comments seem suited to ABSA because many of the texts explicitly
express sentiments regarding specifically named aspects of fanfiction stories.
8. Data Access Statement
To protect the privacy of the fanfiction community, the dataset of fanfiction comments will not
be made available for reuse. Derived data and code are available on Github.
9. Acknowledgments
This research was conducted as part of a Transnational Access Fellowship at GhentCDH & Lt3
at Ghent University in the project Computational Literary Studies Infrastructure (CLSINFRA),
which has received funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement No. 101004984.
The project Anchoring and Innovating Greek Myth in Contemporary Online Fanfiction is sup-
ported by the Dutch ministry of Education, Culture and Science (OCW) through the Dutch Re-
search Council (NWO), as part of the Anchoring Innovation Gravitation Grant research agenda
of OIKOS, the National Research School in Classical Studies, the Netherlands (project number
024.003.012).
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