From Zohran Mamdani's campaign to the US government's memes, fandom has become the defining language of US politics. Zohran Mamdani never auditioned for, but one of his campaign's final television ads placed him in the middle of the show's infamous Tribal Council. For roughly 30 seconds, a handful of former contestants addressed the camera while explaining their decisions to vote Mamdani's top opponent, Andrew Cuomo, off the "island" of Manhattan. "Didn't we already vote you out?" asks one former contestant. The spot is just one of a handful of fandom-influenced ads that Mamdani's campaign put out in the final weeks of the New York City mayoral race .

WIRED went to Brazil for YouTube's first live NFL broadcast. It was helmed by the platform's biggest influencers as the league expands its quest for global domination. Kay Adams, Deestroying, haleyybaylee, and Cam Newton at YouTube's first live NFL broadcast. The first international game of the National Football League season, a Friday-night tilt between the Kansas City Chiefs and Los Angeles Chargers in São Paulo, is celebrated on the ground by the usual pomp and circumstance. There are photo booths and merch tents catering to local fans, samba dancers in feathered head-pieces entertaining American die-hards traveling across the equator, and a press conference where Chiefs quarterback Patrick Mahomes has to backtrack after calling association football (that is, the kind that is still most popular in Brazil, and the rest of the word) "soccer."

By pretty much every objective measure, HBO's adaptation of the hit postapocalyptic video game The Last of Us has been a roaring success. Never before has a video game narrative been molded into Emmy nominations and such warm reception among respectable critics, industry darlings, and people who have no idea what the term "one-shotting" means. You'd think that the devotees who first fell in love with the game back when it was originally released in 2013 would be toasting the cultural ascendance of their favorite medium--and especially how the story's complicated morality has impacted those who've never picked up a controller. And yet, for as long as the show has been on television, its most dogmatic fans have been caught up in a controversy of much inferior consequence: Specifically, they're furious that Bella Ramsey doesn't look much like Ellie. On the most basic level, this observation is correct.

Sociolinguistic theories have highlighted how narratives are often retold, co-constructed and reconceptualized in collaborative settings. This working paper focuses on the re-interpretation of characters, an integral part of the narrative story-world, and attempts to study how this may be computationally compared between online communities. Using online fandom - a highly communal phenomenon that has been largely studied qualitatively - as data, computational methods were applied to explore shifts in character representations between two communities and the original text. Specifically, text from the Harry Potter novels, r/HarryPotter subreddit, and fanfiction on Archive of Our Own were analyzed for changes in character mentions, centrality measures from co-occurrence networks, and semantic associations. While fandom elevates secondary characters as found in past work, the two fan communities prioritize different subsets of characters. Word embedding tests reveal starkly different associations of the same characters between communities on the gendered concepts of femininity/masculinity, cruelty, and beauty. Furthermore, fanfiction descriptions of a male character analyzed between romance pairings scored higher for feminine-coded characteristics in male-male romance, matching past qualitative theorizing. The results high-light the potential for computational methods to assist in capturing the re-conceptualization of narrative elements across communities and in supporting qualitative research on fandom.

Entity linking (EL) in conversations faces notable challenges in practical applications, primarily due to the scarcity of entity-annotated conversational datasets and sparse knowledge bases (KB) containing domain-specific, long-tail entities. We designed targeted evaluation scenarios to measure the efficacy of EL models under resource constraints. Our evaluation employs two KBs: Fandom, exemplifying real-world EL complexities, and the widely used Wikipedia. First, we assess EL models' ability to generalize to a new unfamiliar KB using Fandom and a novel zero-shot conversational entity linking dataset that we curated based on Reddit discussions on Fandom entities. We then evaluate the adaptability of EL models to conversational settings without prior training. Our results indicate that current zero-shot EL models falter when introduced to new, domain-specific KBs without prior training, significantly dropping in performance. Our findings reveal that previous evaluation approaches fall short of capturing real-world complexities for zero-shot EL, highlighting the necessity for new approaches to design and assess conversational EL models to adapt to limited resources. The evaluation setup and the dataset proposed in this research are made publicly available.

The elevator landed on the suite level of Lower.com Field in Columbus, Ohio, and emptied out into a miniature CES full of soccer's latest and greatest technological achievements. On July 24 and 25, as a sort of VIPs-only sideshow to the Major League Soccer All-Star Game, the MLS held this expo to demonstrate what's next for the sport, a commercial for what the league says soccer will soon be. That future, it seems, is rich with data--more than you can appreciate. Every slight movement and every split-second reaction is converted into data and then informs some other system: on-field reviews, advanced statistics, generative A.I. chatbots, and virtual reality experiences. The promise is that it'll unlock plenty of new, custom experiences on the field and for viewers at home.

While machines learn from existing corpora, humans have the unique capability to establish and accept new language systems. This makes human form unique language systems within social groups. Aligning with this, we focus on a gap remaining in addressing translation challenges within social groups, where in-group members utilize unique terminologies. We propose KpopMT dataset, which aims to fill this gap by enabling precise terminology translation, choosing Kpop fandom as an initiative for social groups given its global popularity. Expert translators provide 1k English translations for Korean posts and comments, each annotated with specific terminology within social groups' language systems. We evaluate existing translation systems including GPT models on KpopMT to identify their failure cases. Results show overall low scores, underscoring the challenges of reflecting group-specific terminologies and styles in translation. We make KpopMT publicly available.

Manual data annotation is an important NLP task but one that takes considerable amount of resources and effort. In spite of the costs, labeling and categorizing entities is essential for NLP tasks such as semantic evaluation. Even though annotation can be done by non-experts in most cases, due to the fact that this requires human labor, the process is costly. Another major challenge encountered in data annotation is maintaining the annotation consistency. Annotation efforts are typically carried out by teams of multiple annotators. The annotations need to maintain the consistency in relation to both the domain truth and annotation format while reducing human errors. Annotating a specialized domain that deviates significantly from the general domain, such as fantasy literature, will see a lot of human error and annotator disagreement. So it is vital that proper guidelines and error reduction mechanisms are enforced. One such way to enforce these constraints is using a specialized application. Such an app can ensure that the notations are consistent, and the labels can be pre-defined or restricted reducing the room for errors. In this paper, we present SHADE, an annotation software that can be used to annotate entities in the high fantasy literature domain. Specifically in Dungeons and Dragons lore extracted from the Forgotten Realms Fandom Wiki.

Microsoft's big Xbox announcement last week turned out to be something of an anticlimax: just four games, none of them particularly earth-shattering, are making their way to PlayStation or Nintendo Switch in the near future. Microsoft is neither exiting the console market nor taking all its games multiplatform, as whipped-up rumour mongers had wildly speculated. And the (excellent value) Xbox Game Pass subscription service is remaining exclusive to Xbox and PC. Microsoft was already one of the biggest publishers on PlayStation, especially now that it owns both Bethesda and Activision-Blizzard: everything from Skyrim to Call of Duty to Minecraft is technically a Microsoft game. If Microsoft gaming chief Phil Spencer had announced that, say, last year's Starfield was headed to PlayStation 5, or Xbox head Sarah Bond had said that Microsoft was ditching the idea of Xbox-exclusive games altogether, well, that would have been a huge shift worthy of writing home about (or, in my case, writing to you about).

Cultural evolution is driven by how we choose what to consume and share with others. A common belief is that the cultural artifacts that succeed are ones that balance novelty and conventionality. This balance theory suggests that people prefer works that are familiar, but not so familiar as to be boring; novel, but not so novel as to violate the expectations of their genre. We test this idea using a large dataset of fanfiction. We apply a multiple regression model and a generalized additive model to examine how the recognition a work receives varies with its novelty, estimated through a Latent Dirichlet Allocation topic model, in the context of existing works. We find the opposite pattern of what the balance theory predicts$\unicode{x2014}$overall success decline almost monotonically with novelty and exhibits a U-shaped, instead of an inverse U-shaped, curve. This puzzle is resolved by teasing out two competing forces: sameness attracts the mass whereas novelty provides enjoyment. Taken together, even though the balance theory holds in terms of expressed enjoyment, the overall success can show the opposite pattern due to the dominant role of sameness to attract the audience. Under these two forces, cultural evolution may have to work against inertia$\unicode{x2014}$the appetite for consuming the familiar$\unicode{x2014}$and may resemble a punctuated equilibrium, marked by occasional leaps.