WEBVTT

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So in case you haven't heard about fanfiction yet, fanfiction is literature that is created

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by fans for fans without a commercial interest in mind. And so usually when we talk about

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fanfiction, two of these aspects come at the forefront, intertextuality and community.

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You can define these types of intertextuality with different levels of aggression, I would

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say, because Henry Jenkins famously called fanfiction writers textual poters. And then

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you can also say, well, they just use copyrighted material or they develop already existing

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root texts. And Abigail de Reco, for example, says that they are writing archontic literature,

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which describes this intertextual relationship more softly and without being so hierarchical,

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like if you use the words derivative or appropriated literature. And of course, then again, we immediately

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come to the community. So fan texts are created within a community and often for the community.

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But then in the next sentence, usually you will read about the quantity. There is a lot

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of fanfiction out there. And this is also the reason, according to some scholars, for

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example, Francesca Coppa, that why fanfiction hasn't been picked up by literary studies

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yet, because there's just too much of it and it's hard to focus on one thing in particular.

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She actually composed a fanfiction reader with sample fanfictions to provide some baseline

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for research. And she writes about it. It's there in the corner about this book. She writes,

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the most perverse thing about this book is that I'm giving you one Pringle, one Dorito

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and one Oreo, a single nacho, one pretzel nugget, a tiny little test a teaspoon of ice

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cream, one pita triangle worth of hummus. Then she of course says that you're not supposed

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to eat just one Dorito and goes on to say that the experience of fandom, especially

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in the age of the internet, is one of binge reading. Most new fans upon discovering it

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gobble it down. And even in the 1990s, so this is digital fandom, but even in the 1990s,

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Camille Bacon Smith wrote that first experiences of fandom are measured in inches or stacks

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rather than in titles. And this is when you had to acquire fandoms via zines that are

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somehow redistributed to you. So we have these three aspects, intertextuality, community

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and quantity. And why I find this important in the context of the social system of literature

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and digital methods is because we all know that there is this triad of author, reader

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and text. And of course we also know that there are other key forces like archival institutions

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or publishers or libraries who all deal with canonization. And Heidebrandt and Winkow describe

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this entire social subsystem with its actions and institution, the social system of literature.

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And some even say that literary history focuses on either the author or the reader or the

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text in different periods of its existence. And I think that now when we are talking about

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digital social reading so much, we can say that today literary history would do well

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while focusing on the social system of literature altogether. And what better way to do this

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than with digital methods? So we have different processes in place, the role of the author

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changes, we have different financing models and traditional publishing has to ease the

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process up because authors can now distribute their texts differently, maybe through social

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media. Then we see that there are different models of authorship. We are used to the single

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author, because this is something that has been established through copyright since the

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19th century, but now other models of authorship are resurfacing within digital spaces for

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example. At the same time for fan fiction authors, it is quite hard to distance themselves

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from these older authorship models and this is why many fan fictions start with the paratext

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of how people are not going to use the characters for any kind of financial gain as a safeguard

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against copyright claims. And so we see that fan fiction is actually neatly fits into this

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history of authorship and inter literary history as well. So in order to look at the

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social system of literature, what I'm coming to you with is a relational corpus from the

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website fanfiction.de. fanfiction.de is a German speaking fan fiction website which

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was, which exists since 2004 and it hosts both fan fictions and what is known as free

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works like original works. Today it covers over 1.4 thousand fandoms and over 400 thousand

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texts. It also has around 60, the corpus I'm working with has around 67 thousand texts

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by over 22 thousand authors. Initially the idea was developed at the University of Darmstadt

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where we thought it would be great to capture fan fiction production during COVID and we

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were thinking of course we will gather the data over a few months and then the pandemic

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will be over and so we can go on with our analysis. Well the data collection continued

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until 2024 and now the corpus is accessible on request and there is also a range of meta

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data available on the authors and on the texts. So what do we know from this meta data? We

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know that fan fiction.de attracts significantly more female users than male ones. In fact

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women make up 64 percent of the authors while men account for only about 6.7. Almost 27

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percent of users don't specify their gender at all and over the past four years the number

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of authors that identify as diverse has grown by a third which has to do with legislation.

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As you can see from the graphic most authors are between 18 and 40 years old. These findings

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aren't particularly surprising because we know that women have been dominating the fanzine

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subculture since the 1960s beginning with Star Trek and even in the 90s it was approximated

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that those fans who write fan fiction are probably around 90 percent of them are women.

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Whatever male fans were doing they were not writing texts and scholarly literature on

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fan fiction connects this gender distribution to historical ideas of identificatory reading

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or to the higher, the more complicated ways for women to enter the literary field. So

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what we also have within this corpus is relational data. So there are reviews to more than 51,000

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texts and the meta data on the stories and their protagonists. So this review data can

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be used in various ways. We can either look at how user A has commented on this or that

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chapter of user B but we also could build a network out of it. We can connect the users

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via their comments to stories. So the corpus includes texts, data on the authors, information

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about the texts such as genre and main characters and relational data in the form of reviews

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providing all the components of a digital social system of literature. So when we take

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a look at all the fandoms on Fan Fiction de l'Eye you can probably guess what is going

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to be the most popular fandom there. It is Harry Potter. It is followed by several anime

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fandoms such as Naruto and One Piece and My Hero Academia and across various platforms

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JK Rowling's books have captivated amateur authors and at first glance it seems that

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no other fandom can reach this. But let's keep in mind although the dominance is really

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unmet it is still only 18% of the overall content. In an initial paper together with

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a former Downstead team we focused on the Harry Potter fandom due to its dominance

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and made some interesting discoveries. For example there were slightly more male authors

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within the Harry Potter fandom and they received slightly more reviews to their texts. But

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if we introduce network analysis into this we can map and navigate our network of Fan

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Fictions and maybe filter out some of the texts a little bit better. So network analysis

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is a widely used approach based on the idea that real world networks somehow share similarities.

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Brandes and Erlebach formulated it as follows empirical networks representing diverse relations

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such as linkages among web pages, gene regulation in primitive organisms, sexual contacts among

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Swedes or the power grid of the western United States appear to have maybe surprisingly some

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statistical properties in common. So the idea is that the architecture of complex networks

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somehow is similar and we can use statistical tools to analyze and explore these different

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structures. So either we can be looking at Fan Fiction communities or collections of

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texts or characters and novels we can still using network analysis identify key actors

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and groups of entities that share certain similarities. So a network usually consists

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of nodes and edges and usually we can see that for example user A or A has written some

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letters to B and we may be interested in the content but we may be also interested in all

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the neighbors of A and B or the relationships among the neighbors and the neighbors of their

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neighbors and while the content of the letters stays the same there's a significant difference

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whether these letters are exchanged in a group where everybody talks to everybody or whether

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they are connecting two groups of people that otherwise do not communicate and we can also

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encode different information into these networks. We can say oh well C wrote 10 letters to D

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but D only replied once and then we can assess the different network positions who is more

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central who has more connections or who is connecting groups that otherwise would be

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not connected. And so you can also look at different clusters of actors and consider

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that something will be in common and in a previous project I examined interactions on

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an online forum of World War II enthusiasts and military historians and I could identify

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that when there were some sort of dense subgroups of communication on that forum then these

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different subgroups were representing different practices of memory. Some were interested

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in artifacts, other were interested in visiting bunkers and so on. And on the right you can

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see the texts also group according to the narrative according to first person narrative.

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So much for networks. This is how a network of our corpus looks like. So this is Fanfic

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2 and E and we can see that two users are connected by an edge if user A has written

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a review for a text by user B. And of course we also have metadata on the networks and

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this network is colored according to dense subgroups within the network. It consists

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of 47 communities ranging in size from 13 members to almost 4,000 and we have five largest

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clusters in the middle. These are approximately 40% of all users. So we can also map where

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do they interact within these subgroups using their metadata and then these are for examples

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all the subcommunities that deal with anime. And these are subcommunities that are somehow

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exchanging stories on Harry Potter. And here is Marvel and Star Trek and probably if you

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already did something on fan fiction you won't be surprised that Twilight and Fifty Shades

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of Grey are grouped together because one used to be a fan fiction of the other. And this

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is all quite not really surprising but this data driven approach enables us to see communities

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that we wouldn't have seen otherwise. So we all expect that there will be some exchange

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on Harry Potter but would we expect that the German series in Aller Freundschaft und Bergretter

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also would have a very tight knit and active community? I did not. Neither did I expect

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that German pseudo documentary criminal series from the early 2000s were also constituting

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a quite active community on fan fiction. And so I could go on here because there is also

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the medieval rock bands and fan fictions on Joko and Klaas and all sorts of things. But

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the next step here would be to look at how the interpretations of different texts within

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these sub communities differ. And what I could see and this is here we are coming to the

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changes that are happening within different stories. We have for example these two, the

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green cluster and the pink cluster which are mostly dealing with Harry Potter and what

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I noticed about them was that they have very very different distributions of gender. So

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we remember there are only 6% of male authors on the website in general but within the green

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cluster there are suddenly 16% of male authors. And the pink cluster only has less than 1%

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of male authors. So probably there is something more to it when we are looking at these differences.

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And so we were talking yesterday during coffee about how when you engage in a certain method

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becomes a hammer with which you can then go to the next step and create a next network

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because the data on the main characters within these two groups of people who exchange their

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views on the stories is also available. And so what do we see here? We see the protagonists

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of the pink cluster where mostly women are writing and of the green cluster that has

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quite a lot of quite a lot of male authors in comparison to the whole website. So we

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see that the green cluster features Harry Potter as the main protagonist and the pink

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cluster features Hermione Granger as the most central protagonist. However, their closest

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connections in both cases are to Severus Snape and Draco Malfoy. For in the pink cluster

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also quite interestingly is McGonagall who is connecting important characters from the

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canon but that do not have any connection for Hermione or any relevance Dumbledore or

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Voldemort are not very particularly interesting for Hermione but they are connected by McGonagall.

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And so we could either argue that these are two different ways of continuing to write

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Harry Potter. Either we take in a feminist perspective and place Hermione in the center

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of the narrative and some fan fictions do that, some do not. Then the other perspective

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is that Harry Potter is at the center and has more encounters with ambivalent characters.

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But it could also be that they are both used to tell more stories about Snape and Malfoy.

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And if we compare this data to data from other fan fiction portals then we can see that Draco

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and Harry and Draco and Hermione are very very common pairings. But Hermione and Snape

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and wait a second, Hermione and Snape and Harry and Snape are not that common. And I

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was wondering, this is data from Archive of Our Own which is the largest, I don't need

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to explain I think, is the largest fan fiction portal there is. And I was thinking maybe

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these are regional differences. Maybe this is something that German fans do and others

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do not. After all the Wattpet paper by Pianzola and Rebora show that there are regional differences

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in naming, conventions and so on. And then, and here I come to Middle Earth, I stumbled

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upon work on Vera Kunstlein and she famously demonstrated that in fan fiction fans tend

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to prefer younger characters in comparison to older ones. And this is not necessarily

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a regional question but I decided to check her data on Middle Earth to the data from

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fan fiction. Because of course we also have a Middle Earth cluster. And the Middle Earth

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cluster has around 1000 cells and it's a very large cluster. And I was thinking that

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there's around 1600 readers and authors. Middle Earth, here I combine The Hobbit and

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The Lord of the Rings, there are no fan fictions on Silmarillion for some reason. And it's

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approximately half of it. What I'm also interested in is how fandoms are combined because there

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is this idea by Henry Jenkins that we are all digital nomads and it's only natural to

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like different books and not just one. So here next to Middle Earth we have Merlin which

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kind of is self-explanatory, it's also fantasy, it's also set somewhere in the past probably.

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But also interestingly Kalmais Vinnitu is also quite present there. And we can look

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at it in a little bit more detail. Here you see a model of this cluster in the form of

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a bi-model network. So we can browse and explore the community using network characteristics.

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We can take a look at what is the most central text and then just navigate our reading and

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we don't have the problem that there is too much fan fiction to even start. The triangles

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are colored, the darker ones are Lord of the Rings and the brighter ones are The Hobbit

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and then Kalmais and Merlin and they are located at the center because it's so tightly mid

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and then Kalmais and Merlin are up there. Yeah and so what you see in Middle Earth fan

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fiction as well is that Hobbit lends itself more to classical fan fiction production.

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So this is a very very common way, very common distribution of genres. It's a love story

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and then a little bit of drama but also family and romance and romance again because there

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are two labels of romance in German and in English. You still haven't understood how

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they are differentiated and there is also the classical fan fiction genre, the hurt

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and foot genre which means that this is a type of story where one protagonist gets

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injured and is nurtured back into health and is a very common trope in fan fiction. But

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in Lord of the Rings you have a very different situation. You have adventure and drama and

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fantasy and no romance at all. So it sounds to me as if the Hobbit writers would stick

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more to fan-ish kinds of productions whereas Lord of the Rings fans are more stuck to the

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original. And I did not annotate the occurrences by the books and this is based on the German

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original because the fan fictions are of course also in German. And we can see that users

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on Fan Fiction DE much prefer to use the story world of Tolkien rather than the original

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characters. So on the left you can see the top ten protagonists which is basically the

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fellowship and the middle table demonstrates the same metric but shows the occurrences

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in the fan fiction corpus. While the right table demonstrates the number of main characters

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within the corpus. So people occur in fan fiction but then also they use tags to navigate

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the reader experience. And what we see is that Hobbits are on decline and more and more

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of elves and humans are also used. And the other thing that we see is that Hobbits are

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more popular. When we compare this to AO3 data we see a very different preferences.

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Aragorn is more popular than Legolas and Gandalf disappears from the list completely which

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supports the argument that older characters are excluded in favor of more conventionally

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attractive ones. Although older in the context of Lord of the Rings is a whole different

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story. As is the case for a lot of fan fiction the main tendency in the genre on AO3 is erotic

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in nature. So in the case of Fan Fiction DE it seems that the character tags sort of work

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as mechanism of expectation management and perhaps stories with the fan favorites like

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Legolas and Thranduil attract more attention from users. And the strong difference between

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these two platforms is an argument against this theory of a common fan fiction narrative

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that fans sort of retell the story into one direction. And whether this is language, culture

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or platform specific remains very much open ended. And finally when we look at the text

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of Lord of the Rings compared to the original in a very simple contrastive analysis I'll

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say a few words about that you don't have to read it all. We can see that there are

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some differences between the two that demonstrate how despite writing in the same genre still

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means something quite different. In the list on the right we have quite a few landscape

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descriptions which Tolkien is quite known for. One or two character names Frodo who

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is not mentioned at all in the fan fictions for some reason occurs within the original.

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And then we see and this is why I love digital analysis we see many many many references

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to light and darkness. And this is something that the list on the left which is the list

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of preferred words of the fan fiction doesn't pick up on at all. So on the list on the left

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we have plenty of interactional verbs more informal style references to the body lips

00:26:40.840 --> 00:26:47.640
hair shoulders and so on. And then lots of narrative time references which I'm also not

00:26:47.640 --> 00:26:54.640
sure what to do with. Somehow this may be due to the fact that amateur writers need

00:26:56.720 --> 00:27:03.720
more telling than showing which comes when or maybe this is because the narrative in

00:27:04.440 --> 00:27:11.440
Lord of the Rings is a little bit more straightforward than here. But overall it raises the question

00:27:11.800 --> 00:27:17.980
whether fan fiction has a style of its own. And in conclusion we see that we have different

00:27:17.980 --> 00:27:24.760
interpretive communities and they have different dynamics across platforms. They can be subversive

00:27:24.760 --> 00:27:30.040
but they don't have to be. And then we have the digital paratext that serve as a guiding

00:27:30.040 --> 00:27:37.040
mechanism for readers and shape their experiences. And finally and my most important takeaway

00:27:39.400 --> 00:27:45.480
message is that with network models you can definitely provide some structure to the reading

00:27:45.480 --> 00:27:52.480
experience and navigate the corpus much much better. So that's all.

