Hi Sheena! Pat (@aheartfullofjolllly) sent u this love note: Sheena! You are so funny (meme queen) and always so game to roll with our bs! I love that we can always just joke around with each other and have a good laugh. You are always so kind and generous. But remember, cleanliness is next to godliness. Sharing is caring but not everything. HAHAHA. You are seriously so talented and I love your photo manips especially this! The matching robes! 💗💗💗
We would also like to thank u for creating content for the fandom and for being a part of our net! you are incredible!💞
💌 Love, the mdzsnet lovebot! 💌
@aheartfullofjolllly thank you! ❤❤❤
I try my best to keep up with the meme shengshou, Pat 😘
also since we're talking about sharing let me share this gif with you. I trust no explanations are needed ^_^
/ a very drarry special ||| pt.1 of ?
DRARRY ON AO3: TROPES THROUGH TAGS
I think many of us have heard of AO3′s data dump by now! I was really excited like everyone else to be able to play with that dataset - especially because it's an entry point into analysing my first and still most-loved ship: Drarry.
Drarry - like many other ships in many other fandoms - has its unique set of tropes. After getting into a new ship and reading it for a while, we kind of intuitively know about its common tropes. Within Harry Potter, there are so many that have accumulated across the history of the fandom – Veela blood inheritance, Hogwarts 8th year, Harry in Slytherin AUs, etc. And we know that when we see certain tags – e.g., creature inheritance – we can expect to see certain other tags (e.g. veela Draco Malfoy, magical bonds/soulmates).
That brings us to the question: are we able to capture some of these tropes or ideas automatically given a huge dataset of tags? That’s what I attempted (attempted is a huge keyword here) to do, looking at each rating (Gen/Teen/Mature/Explicit) separately.
The networks shown are samples of some groups of tags which were pulled out with the help of some preprocessing and a community detection algorithm. You can view these interactive plots and the whole write-up here on my Github page.
//
As with my work on using tags to characterise characters in the D:BH fandom, I want to reiterate that this work is very coarse, with tags capturing only a small part of the picture. This is not an attempt to replace close qualitative reading and understanding of fic text. Feedback, etc is always welcome too, always looking to improve my handling of data!
The only reason I'll accept for you not sitting yo ass down to finish Tea and Tourniquets is if... you're working on something for our network project 😏
We love an encouraging friend 💖
ALSO
There is a really cool, totally amazing, spectacular project on @bangtansmutcentral so, you guys should totally check it out and spread the word 🎉
→ @camphalfbloodnetwork: quest 3: fav brotp/friendship {nico and reyna}
get to know the members: flora
“We had one home,’ she said. ‘Now we have two.’ She gave Nico a big hug and the crowd roared with approval. For once, Nico didn’t feel like pulling away. He buried his face in Reyna’s shoulder and blinked the tears out of his eyes.” (x) (x)
Hi Sheena! Teresa (@manhasetardis) sent u this love note: Hi Sheena! Thank you for making such gorgeous gifsets and edits! My favorites are probably lwj with wild geese and the lwj gusu lan sect rules series! I also love your funny edits, they’re brilliant! I hope you’re having a wonderful day 💖💖💖
We would also like to thank u for creating content for the fandom and for being a part of our net! you are incredible!💞
💌 Love, the mdzsnet lovebot! 💌
Thank you so much @manhasetardis! You are so sweet and such a delight to know ❤
So more than a month ago I started looking at the kudos information I scraped from AO3 D:BH fics, very basic descriptive stuff.
Assuming (1) choosing to write a fic about a certain ship(s), and/or (2) choosing to read a fic with a certain ship(s) and leaving a kudos on it can be taken to be some proxy measure of interest in a ship, I decided to make a simple plot summarising the amount of interested users that each ship has and that each ship shares with another ship in the fandom.
Resulting plot:
x Interactive version of the plot can be found on my Github (link in description/Tumblr heading). My Github page also has links to tables with the raw counts shown when hovering over the nodes/links, for easier reference.
Again, note that this scrape was done in June 2020. I removed any fics that were non-English, were crossovers, or had less than 10 words. 16211 fics remain for analysis.
Details of the process under the cut.
1. Preprocessing relationship tags
Ship tags can get pretty unstandardised! I took only tags that had a ‘/’ between the characters (e.g. Connor/RK900). I then split each tag at the ‘/’ character and standardised the resulting two (or more) names. For example, Connor (one-sided), RK 800, Connie, Connor - they all fall under ‘Connor’.
As ships may involve more than two characters, for the sake of visualisation, I converted those into a list of every possible permutation of pairs within the listed ship; so Connor/RK900/Hank into [Connor/RK900, Connor/Hank, RK900/Hank].
2. Extracting user interest in ship(s)
Again, I took user interest in a ship to be signalled by:
(1) being an author of a fic with the ship tagged, or
(2) leaving a kudos on a fic with the ship tagged
(1) was extracted very simply given that each fic has an author(s) tied to it. (2) was taken from the kudos list shown at the end of every fic. Given that there’s no way to disambiguate guests and tie them across different fics on my end, I discarded that information and focused only on registered site users.
At the end of this process, I had a long list of user-ship interest pairs. 354382 entries, to be exact.
3. Preparing the plot
Given the information I have (user-ship interest pairs), I started with a bipartite plot. All that means is that there’s one set of nodes that’s all users, and another set of nodes that’s all the D:BH ships ever written about on AO3. Between these two sets of nodes are links that connect a user to a particular ship if they’ve expressed interest in it. The set of user nodes has no links within it, same for the set of ship nodes.
The bipartite graph can then be flattened/projected into a regular graph with just one type of nodes (in this case, ships, since I’m interested to see how many interested users each pair of ships shares). So we keep only the ship nodes, and a link exists between two ship nodes now if they share at least one common interested user. We can weight the links by the number of common interested users they share.
This graph has 311 ship nodes and 34429 links. It’s not huge, but it’s very unwieldy to visualise. Likely, many nodes also share very weak links (e.g. just a couple of common users). Since my end-goal is really just visualisation, I decided to prune the graph.
4. Filtering the graph
I reuse the same filter from Serrano et al., (2009) that I applied on my character co-occurence graph. I set it at a relatively strict level of α=.001. This filtered the graph down to 185 ship nodes and 1251 links.
5. Visualising the graph
I made this one less springy than the previous ones, since I realised how annoying it was to explore when the nodes keep bouncing back to place when you tug them out.
Ship node size is determined by number of users that have indicated interest in the ship (bigger=more). Link size is determined by the number of common interested users the pair of ships shares (thicker=more).
I also realised it’s still a little tough to really pick apart the links to get a good look, so I’ve uploaded the tables with the raw counts on my Github page. Unfortunately I cannot add a search bar since I don’t think I can deploy Dash on Github pages, so it may be a bit tedious looking through the link table.
