Algospeak

When was the last time you said or typed a word because you knew an app would treat it better—writing “unalive,” “seggs,” or even 🍆 instead of the literal term? In Algospeak, linguist and creator Adam Aleksic argues that this is not a side story of the internet; it is the story of modern English. Algorithms don’t just moderate a few taboo words. They shape which words are born, who hears them, how fast they spread, what tone we adopt, and even the way our voices sound on camera. Aleksic contends that we’ve crossed a new inflection point in the history of communication (after writing, print, and the early web): the era of personalized, short‑form video—and it’s transforming language in real time.

Across TikTok, Reels, Shorts, and their lookalikes, algorithms privilege content that maximizes attention and engagement. That incentive structure triggers a cascade of linguistic consequences: euphemistic work‑arounds (“unalive”), meme‑accelerated slang (rizz, sigma, skibidi), creator “influencer accents” optimized for retention, and niche fanilects that flourish inside filter bubbles (Swifties, K‑pop). Meanwhile, commerce colonizes vocabulary through microlabels and SEO‑ish tags (cottagecore, goblincore, preppy), and controversial ideas hitchhike into the mainstream through irony (incel jargon like mewing and looksmaxxing).

What This Book Asks You to Notice

Aleksic invites you to zoom out: rather than blaming “kids these days,” look at the machinery. Why does a museum placard for Kurt Cobain saying “unalived himself” feel jarring? Because a term minted to evade algorithmic flags slipped into a sacred offline space. Why does “no because—” grab your ear? Because it’s a discourse marker that creators learned keeps you from scrolling. Why do so many creators sound the same? Because uptalk, emphatic prosody, and zero‑silence openings (“Gen Z shake”) reliably hold the floor.

The Core Argument in One Line

Algorithms no longer just sort language; they co‑produce it. Words, memes, and metadata have merged into a single pipeline where human needs (to euphemize, belong, joke, sell) meet machine priorities (to predict, personalize, optimize). The result is what Aleksic calls an emergent system: neither purely human nor purely technical, but a feedback loop that births new vocabularies, spreads them at viral speed, and engrains them through habit, performance, and commerce.

What You’ll Learn in This Summary

You’ll see how censorship games create linguistic Whac‑A‑Mole (chapter 1), how memes and metadata propel slang from joke to norm (chapter 2), and how the attention economy scripts your hooks, emotions, and even your sentence structure (chapter 3). You’ll hear why “influencer accents” are real—lifestyle, entertainment (MrBeast), and educational—and how they became prestige dialects online (chapter 4). You’ll step inside filter bubbles where fanilects bloom (Swifties, K‑pop), then spill out through context collapse, engagement‑maximization, and misinformation dynamics (chapter 5). You’ll track how incel language traveled from 4chan to mainstream satire to subtle normalization (chapter 6), and how AAE and ballroom slang (“slay,” “tea,” “mother”) got mass‑adopted, often without credit, through hood‑irony memes and corporate trendbait (chapter 7).

You’ll also watch commerce lace itself into vocabulary via aesthetics and microlabels (“-core” culture), Spotify‑style microgenres, and platform shops that turn identities into shoppable tags (chapter 8). You’ll see how “generations” (Gen Z vs. millennials) became a meme‑driven identity with its own canon, where language is simplified by Flanderization and lived life becomes “plot,” “eras,” and “POV” (chapter 9). Finally, you’ll look beyond English: Spanish teens calque “unalive” as desvivir; French borrows African slang via rap and social media; ASL compresses for the vertical video frame; and protesters in China invent homophones to outwit censors (chapter 10).

Why This Matters—for You

If you teach, parent, market, moderate, or simply communicate online, these patterns explain the words your students bring to class, why your reels perform (or flop), and how subcultures coalesce or polarize. They also reveal opportunities: euphemisms can open safer mental‑health talk; fanilects can build community; attention‑savvy storytelling can spread civic journalism. And they surface risks: context collapse can pejorize in‑group terms (“acoustic”), irony can mainstream harms (“Oxford study” troll), and engagement optimization can push rage and doom across feeds.

Bottom line

Algospeak isn’t kids breaking language. It’s humans doing what we’ve always done—euphemize death, badge belonging, sell style, joke at the edge—inside a new medium that speeds, amplifies, and commercializes each move. Once you see the loop—human motives ↔ platform incentives—you can work with it instead of just worrying about it.

Aleksic opens with a word you’ve probably heard but maybe never said out loud: “unalive.” It began as a cheeky Spider‑Man meme in 2013, languished online, then exploded when creators needed a way to discuss self‑harm and suicide without getting videos removed or down‑ranked. The proximate cause? China’s 2019 crackdown on short‑video platforms like Douyin (TikTok’s Chinese twin), which systematized the use of sensitive‑word libraries. When TikTok’s international algorithm and moderation norms followed, so did euphemisms. People learned that saying “suicide” suppressed reach; “unalive” didn’t (at first). A decade later, seventh graders write “Hamlet contemplates unaliving himself.” The euphemism now serves both an algorithmic and a social purpose—kids can actually talk about death in class.

From Leetspeak to Algospeak

The dynamic is old; only the machinery is new. 1980s bulletin boards birthed leetspeak—“5U1C1D3” for suicide, “pr0n” for porn—to dodge crude text filters. Today’s system uses shadowbans and search suppression you can’t see, so you experiment with spellings, emojis, and sounds and then infer what “works” from analytics. That’s Whac‑A‑Mole: as soon as a substitution gains traction, the mallet falls, and you move again. Aleksic notes creators look for telltale signs (sudden drops in “For You” traffic) and err on the side of euphemism when stakes are high (sex ed, mental health).

Bowdlerization—Now with Emojis

We’ve always “bleeped” taboo language—double em dashes in Victorian print (——), cartoon grawlixes ($#*!). Online you see vowel‑stripped variants (“f*ck,” “btch”) and playful misspells (“fucc,” “bicht”). Emojis add a pictographic twist: 🍆 for penis, 🍑 for butt or vagina, 🍒 for breasts, and 🔥 (spicy) for sex. Some swaps are rhymes (🌽 for porn, 🍇 for rape, 🥷 for the n‑word), echoing Cockney rhyming slang (raspberry tart → fart). Others shrink intensity with cuteness—“seggs,” “nip nops,” “peen”—classic minced oaths (heck for hell). Norman Mailer once published The Naked and the Dead with “fug”; TikTok doctors post “p3nis.” Same move, different medium.

Voldemorting, SA, and Metonymy

When even euphemisms get flagged, creators use indirection. They “Voldemort” taboo topics (“the top guy of the Germans”), abbreviate (“SA” for sexual assault), or metonymize (the college “Red Zone” for assault risk). During the 2023 Israel–Gaza war, creators used diacritics (“Ğaza”), swapped 🇵🇸 for watermelon 🍉 when flags got punished, and tiptoed around “highly controversial” rules that auto‑suppressed videos—including Aleksic’s neutral linguistic explainer of “from the river to the sea.”

Who Gets Silenced?

The catch‑22: hate‑speech rules remove slurs—even pedagogically bleeped—while the communities most targeted by slurs (Black, queer, disabled creators) get suppressed when they discuss the discrimination they face. Trolls weaponize this by mass‑reporting videos with “politically charged” language. LGBTQ+ terms were even regionally censored (e.g., #gay/#trans blocked in parts of Russia and the Middle East). That birthed in‑jokey end‑runs like “le$bian” (which TikTok’s TTS hilariously pronounced “le dollar bean”), “wlw,” “alphabet mafia,” and “zesty.”

From Euphemisms to a Sociolect

Why do words persist after the mallet falls? Because algospeak becomes a sociolect—a shared variety people use to signal “I’m one of us.” Saying “seggs” or holding up a palm to denote “white” reminds your audience that the platform is listening and you know how to play. You also code‑switch by domain: the same teen who says “unalive” on TikTok may say “die” at dinner—or may not, if they blur online and offline. Aleksic’s conclusion: the euphemism treadmill (Steven Pinker) now runs on platform electricity. It won’t stop until the underlying taboos or incentive structures change.

(Context: Gretchen McCulloch Because Internet chronicled how the early web normalized informal writing. Aleksic extends that lens to the short‑video age, showing how algorithmic rules—not just social ones—steer which “informal” you adopt.)

If you felt flooded by new slang in 2023—rizz, sigma, skibidi, gyat—you weren’t imagining it. Aleksic traces the pipeline from Vine to Musical.ly to TikTok and shows why words now bloom and die faster. Short‑form video makes repetition irresistible; a good audio or format invites participation. TikTok’s key upgrade over Vine, though, is personalization: instead of a single global feed, your For You page learns your tastes (Nicki Minaj + linguistics? Here’s “Super Freaky Girl” over fiber‑optic explainers) and feeds you the version of a meme you’re likeliest to love. That makes the first encounter stickier—and stickiness is fuel for word adoption.

The Rizzler Moment

Aleksic dissects one emblematic case: the Rizzler song (“Sticking out your gyat for the rizzler / you’re so skibidi / you’re so fanum tax / I just wanna be your sigma / …give me your ohio”). Every noun was already trending in Gen Alpha comedy: rizz (charisma), gyat (butt), sigma (lone‑wolf “alpha”), skibidi (nonsense), fanum tax (inside‑joke “theft”), ohio (absurd place meme). Package them in an earworm and you supercharge their reach. Aleksic himself rode the wave, posting etymology explainers that racked up millions of views—because the metadata (“rizz,” “sigma,” “skibidi”) made the algorithm send his videos to primed audiences.

Templates Keep Words Alive

Words survive when they slot into many frames. That’s always been true (David Crystal calls it “language play”); TikTok just multiplies the frames. “Sigma” morphed into “what the sigma,” an oath template that previously hosted “what the fuck.” “On skibidi” riffed on “on God.” The evergreen “she X on my Y till I Z” accepted anything (“she rizz on my gyat till I ohio”). This Mad Libs elasticity lets memes outlive their original video and pull new users in through fresh jokes.

Obtrusiveness vs. Endurance

Why do some terms die with the trend while others stick? Aleksic borrows Everett Rogers’s diffusion curve (innovators → early adopters → early/late majority → laggards) and adds two crucial filters: obtrusiveness (does it feel forced?) and endurance (does it fill a semantic gap?). The chair emoji briefly replaced 😂 after a prank, but it screamed “joke” and disappeared. By contrast, 💀 as “I’m dead (from laughing)” felt natural and helpfully differentiated younger from older cohorts—so it stayed. Similarly, “function” (for party) grew through meme captions (“when the function has…”) but persisted because it offered a playful, slightly different shade than “party.”

The Engagement Treadmill

Creators learn to serve the algorithm’s cravings. If “rizz” content performs, more creators make “rizz” content; the audience engages more; the algorithm serves more “rizz.” Aleksic calls this the engagement treadmill. It also drives spinoffs (Gregorian‑chant Rizzler; piano ballad Rizzler) and educational tie‑ins (his etymology videos) because everyone knows the keyword will unlock discovery. The treadmill helps a word conquer middle schools fast: in Aleksic’s survey, 80% of parents/teachers heard “rizz” within months; “gyat” quickly followed.

Memes as Cultural Vectors

Memes are ideas built to travel (Richard Dawkins’s point), and in the short‑video age their fitness depends on watch time, shares, and comments—not just likes. Slang is now inseparable from meme templates and algorithmic incentives. That’s why Aleksic argues that “language and memes and metadata are one and the same.”

(Comparison: Allan Metcalf’s Predicting New Words asks whether new coinages fill a need. Aleksic updates the question: do they fill a need and thrive in the engagement ecology—low obtrusiveness, high template‑fit, and metadata momentum?)

Aleksic’s creator eye is sharpest when he turns to attention. He used to game Reddit’s public ranking formula (post early; craft a curiosity‑gap title like “Snowfall in Sequoia National Park, California”). On TikTok/YouTube/Instagram, the formulas are hidden, but the pressure is the same: win the first second or you lose the viewer. That pressure rewrites your language.

Hooks that Retain

Creators learn that extreme language (“my least favorite thing about…,” “it should be illegal to…”) and second‑person framing (“you need to…,” “if you do X, here’s how…”) hold attention. Studies back it up: high‑valence emotion spreads more (Wharton research); second‑person pronouns increase song popularity (NYU). Aleksic’s own analytics show audience drop‑offs correlate with weak openings; punchier hooks keep people watching.

Ragebait, Clickbait, Trendbait

Because engagement is a goal, creators sometimes provoke. Aleksic profiles actress Louisa Melcher, who posts outlandish “performance art” lies (she fell off the Super Bowl halftime stage; she’s on a one‑way space mission) precisely to evoke intense reactions. Viewers who spot the fib still “hate‑watch”; others argue; everyone comments—feeding the algorithm. Elsewhere, “eighth greatest fact”‑style clickbait promises a payoff it never delivers, exploiting your curiosity to pump comments of frustration. And then there’s trendbait: coining phrases that can become participatory memes (“girl dinner,” “boomer ellipses,” “Roman Empire”) because you know reaction stitches will snowball reach.

“No Because—”: A Micro‑Hook

Listen closely and you’ll hear a new interjection everywhere: “no because—.” Linguistically, it’s a discourse marker—like “wait” or “hold on”—that claims the conversational floor. Psychologically, it primes you to expect something interesting and buys the creator half a beat to think, just as uptalk drags you into the next clause. Over thousands of exposures, platforms train you: “no because” usually precedes an engaging bit; keep watching. The phrase then migrates offline, a telltale sign that platform‑optimized speech is crossing domains.

The Matthew Effect

On social platforms, a small retention edge compounds into massive reach (the Matthew effect). If your first 30 seconds retain 20% more viewers, the system bets on you, shows your video to more people, and social proof kicks in (views beget views). This dynamic also self‑perpetuates linguistic tics: once the system learns that videos with a certain phrasing (“no because,” second‑person hooks) keep viewers, it serves more of them—teaching creators to imitate the moves.

Ethics and Intention

Aleksic doesn’t excuse manipulation; he names it and urges conscientious use. He scripts “microhooks” into educational videos to sustain attention while delivering value. But he’s frank: if you take a week off, miss a shift (e.g., when TikTok pivoted to favor >60‑second videos), or ignore a trend, your reach can crater. You’re not simply speaking to people; you’re speaking to a triad: audience, algorithm, and norms the algorithm rewards.

(Related lens: Herbert Simon’s “attention economy” predicted this scramble decades ago. Aleksic adds the linguistic layer—how the scramble standardizes hooks, emotions, and discourse markers—and shows how creator analytics turn language into an A/B‑tested craft.)

Once you hear it, you can’t unhear it: the influencer accent. Aleksic distinguishes three flavors—lifestyle, entertainment, and educational—but they share a toolkit: sentence‑final uptalk, emphatic prosody (stressing more words than a “normal” sentence needs), lengthened vowels, ultra‑light pauses (or none), and a faster‑than‑conversation tempo. These are not random quirks; they are floor‑holding strategies optimized for retention and parasocial closeness. Uptalk makes every clause feel like a question—don’t scroll, the payoff is coming. Lengthening and rhoticity (exaggerated “r”s) cover thinking time without silence (which loses viewers). Even starting to speak while setting the phone down—the “Gen Z shake”—functions as a visual hook.

Prestige Dialect—Online

In broadcast news, Received Pronunciation or “General American” became prestige accents; on TikTok/YouTube, the influencer accent takes that role. Aleksic’s poll shows two‑thirds of creators consciously modulate speech for metrics; many others subconsciously assimilate by imitation (think frat “fraccent”). MrBeast is a case study: early videos clocked ~170 words/minute with big variance; today he speaks just under 200 wpm with tight variance—data‑driven cadence honed for maximal excitement. Off‑camera, he doesn’t talk like that. It’s code‑switching.

Where Did It Come From?

Aleksic points to a linguistic founder effect. Early YouTubers borrowed from Valley‑girl cadences popularized by celebrity culture (Kardashians, Paris Hilton). TikTok’s first wave took cues from YouTube. Over time, analytics nudged everyone toward the same prosody because it “performed.” Journalist‑creator Sophia Smith Galer describes aiming for a voice “between broadcaster and creator,” never sounding like she’s finishing a sentence—because that’s what keeps viewers. Even Aleksic’s friends tease him when his educational accent slips out offline.

Global Homogenization, Local Variation

Across the world, creators “soften” regional accents toward American or RP norms to hit the biggest markets (Americanization online mirrors Hollywood’s century‑long influence). British kids were documented adopting “YouTube accents” during lockdown. Yet there’s also branching: lifestyle vs. entertainment vs. educational styles diverge because each niche optimizes for a different vibe. Meanwhile, code‑switching scrambles identity politics—Indian‑born creator Sara Deshmukh faced backlash for switching to her Indian accent after years of British‑accent content, revealing community pressures and internalized prestige hierarchies.

What It Signals to You

When you hear the accent, your brain infers “this will be engaging,” much as good lighting or crisp thumbnails signal quality. That framing boosts retention in the crucial first seconds. In effect, language has become a badge: “I speak in the house style of successful creators.” That may flatten diversity (regional dialects lose ground) but also spawns platform‑specific dialects you can now recognize instantly.

(Related comparison: Julie Beck’s “YouTube voice” essay mapped early traits in 2015. Aleksic shows how analytics matured those traits into a prestige dialect, much like the broadcaster voice emerged under different media incentives.)

Why do Swifties and K‑pop stans seem to speak their own languages? Because algorithms are exceptionally good at building in‑groups around niche passions, then feeding those groups ever more specialized content. Inside those bubbles, people need new words to name shared references and feelings—fanilects. For Taylor Swift fans that means portmanteaus like “Haylor,” theories like “Gaylor,” and lyrical shorthands (red scarf, invisible string). For K‑pop, Korean loanwords (aegyo, sasaeng) mix with repurposed English (bias, anti). The more you watch, the more fluent you become—and the more “you” the algorithm thinks you are.

Filter Bubbles vs. Echo Chambers

Aleksic borrows from Arvind Narayanan to argue that human + algorithm is a complex system with emergent effects. “Gaylor,” for example, is created by fans (human), then used by the platform as metadata to target fans (algorithm), which accelerates its spread within and beyond the group (system). The same dynamics can veer harmful: during the autism self‑diagnosis boom on TikTok, in‑group jokes like “acoustic” (for autistic) leaked outward through context collapse. Outsiders adopted them as pejoratives, and misinformation about diagnostic traits went viral because divisive, “whoa‑really?” content earns engagement.

Engagement Optimizes for “Fun,” Not Truth

Platforms optimize for comments, shares, and watch time, not accuracy or community care. Aleksic calls this “digital rubbernecking”: we react most to what aggravates, amazes, or offends. That drives ragebait politics (more incivility online), but also linguistic side effects—words that are “fun” proliferate (“in my X era,” “delulu is the solulu”), while those that don’t fit meme culture don’t travel as far.

Leakage to the Mainstream

Sometimes a fanilect term fills a general gap and escapes. “In my X era” left SwiftTok to label everyone’s life phases; “delulu” broadened from “delusional idol romance” to “playfully unrealistic” in dating memes. Anime community words like uwu and waifu took a similar path a decade earlier. The pattern is durable: niche → meme‑friendly crossover → general slang, accelerated by algorithmic discovery.

What You Can Do

If you moderate or create in sensitive communities, assume context collapse. Add captions that define in‑group terms, model respectful usage, and be ready with alternates when a term starts getting pejorized. As a consumer, notice when your “For You” starts to feel like your “From Me.” Your feed partially reflects who you were yesterday and partially nudges who you’ll be tomorrow.

(Context: Eli Pariser coined “filter bubble” for search; Aleksic reframes it for short‑video, where bubbles aren’t static walls but permeable membranes where language and identity are co‑manufactured.)

One of Aleksic’s most sobering chapters follows incel language from fringe forums to the feeds of teens who’d never set foot on 4chan. The journey matters because it shows how irony can be a Trojan horse. On 4chan’s /r9k/, users without identities “earned” credibility by wielding insider slang—mogging, cucked, looksmaxxing, blackpilled. Reddit subreddits (including r/Incels before bans) and “rate me” boards laundered lookism via pseudoscientific face metrics (canthal tilt, “hunter eyes”), nudging everyday users toward “improvement” talk (mewing, jaw surgery). When TikTok’s meme engines discovered how goofy this looked, creators began to satirize it—“walkpilled cardiomaxxer,” “Mogwarts,” sigma parodies.

Poe’s Law in Action

Online, extreme views wrapped in humor are easily mistaken for sincerity and vice‑versa (Poe’s law). That ambiguity helps incel concepts travel. After enough jokes about canthal tilt, beauty creators began using it straight; Google searches for jaw surgery ticked up. The “Oxford study” troll (falsely claiming research about white male/Asian female pairings) metastasized into a real‑seeming insult used to harass Asian women. “Doomslang” (it’s over, LDAR/bedrotting, doomer) mirrors incel fatalism and spreads because it fits a pessimistic zeitgeist and earns engagement.

Memetic Pipelines

Aleksic shows the broader meme pipeline, too: anime‑community terms like “sussy baka” left their niche as self‑parody and were embraced by outsiders precisely because they were fun to say. Years earlier, 4chan birthed “seggs” the same way. The lesson is not that all irony is bad; it’s that irony is an accelerant without brakes. Algorithms amplify what’s catchy, not what’s careful, so satire often becomes substrate for normalization.

A Practical Read

If you teach or parent, Aleksic’s timeline helps you decode the in‑jokes you’re hearing (“sigma face tutorial,” “Chad vs. virgin”). If you create, you can weigh the costs of piggybacking on edgy metadata: it may juice reach while smuggling frames you don’t endorse. If you moderate, expect that expelled communities will reappear through looks‑oriented self‑improvement and satire. Build guidance for how to distinguish parody from promotion in policy and enforcement.

(Parallel: Whitney Phillips and Ryan Milner’s work on antagonistic media ecosystems tracks similar flows; Aleksic’s contribution is the linguistic lifecycle inside short‑video mechanics.)

If you hear “slay,” “tea,” “ate,” “serve,” or “mother,” you’re hearing the afterlife of ballroom culture—Black and Latino queer houses in 1980s–90s New York that built language for joy and resistance (voguing, shade, house “mothers”). Through Madonna’s “Vogue,” Paris Is Burning, and later RuPaul’s Drag Race, those words leapt to gay culture broadly, then to straight teen slang—often stripped of their roots. Aleksic argues that social media’s fuzzy group boundaries accelerate this cycle: words intended for an in‑group get picked up by audiences who feel adjacent (e.g., straight girls close to gay male friends), then multiply through memes and corporate marketing until the origin is invisible.

Hood Irony and the ️ Emoji

A parallel pipeline runs through “hood irony.” The Bloods’ practice of replacing “c” with ️ (and “ck” with “cc”) became a memetic joke for white teens; words like “thicc,” “succ,” and “opp” (from Chicago gangs) turned into Gen Z slang. As with ballroom, the process makes life‑and‑death realities into playful tropes, often without acknowledgment. Add in digital blackface (non‑Black users over‑indexing on Black reaction GIFs) and you get a steady diet of exaggerated “Blaccent” parodies—exposed in controversies like creator Tray Soe’s stylized “cone bread” pronunciation.

Who Gets Paid?

Appropriation also has economic stakes. In 2014, Kayla Newman coined “on fleek” on Vine; celebrities and brands (Ariana Grande, IHOP, Forever 21) profited, but Kayla didn’t. TikTok’s “Renegade” dance made Charli D’Amelio a star before credit circled back to the teenage choreographer Jalaiah Harmon. Aleksic notes why: attribution adds friction (bad for the curiosity gap) and trend remixes obliterate provenance. By the time a brand prints “fleek” tees, language has been gentrified.

How to Do Better

Creators can learn and credit origins in captions and voiceover, especially when teaching or profiting from styles rooted in marginalized cultures. Brands can partner with originators, not just trendriders. Teachers can frame AAE as a legitimate dialect (not “improper English”) and show how “cool” traveled from West African itutu to jazz to mass coolness—then got replaced by new in‑group terms as old ones were mainstreamed.

(Context: Linguists like John Rickford and Geneva Smitherman have long argued AAE’s systematicity. Aleksic adds the platform economics that speed up appropriation and muddy credit.)

Remember SEO? On social video, keywords don’t just help search; they help recommendation. That’s why aesthetic microlabels—cottagecore, goblincore, fairycore, grandmacore—proliferated after 2020. They double as identity tags and metadata signals: once you interact with “goblincore,” the app knows which community to feed you. Spotify does the same with microgenres (hyperpop, bedroom pop): human‑friendly names for machine‑detected clusters. Names then shape taste; playlist labels validate genres; aesthetics validate shops.

Identity-as-Commerce

Aleksic connects dots between “-core” tags and platform shops. TikTok literally tells advertisers “subcultures are the new demographics”: build a brand that becomes part of users’ identities. Long‑tail economics (Chris Anderson) make this lucrative: at scale you can sell a little to a lot of niches. Hence cottagecore hauls, Shein microtrends, and “what are we wearing this summer?” refrains that let products latch onto seasonal memes (Barbie summer, brat summer). Even “preppy” got algorithmically redefined—from prep school polish to bright girly maximalism—because boutique owners like cutandcropped learned the #preppy hashtag indexed demand and optimized their content and inventory around it.

The Cost: Flattened Vocabularies

Labels help us find each other, but Aleksic cautions that they can compress language. You might stop describing your taste in rich, varied words and simply adopt a ready‑made “-core.” Creators learn to use popular tags rather than precise ones (#preppy instead of #maximalist) because niche tags underperform. Over time, “optimal distinctiveness” is achieved by choosing one of many preset boxes—unique enough to feel special, standardized enough to be monetizable.

Enshittification

Cory Doctorow’s term fits: platforms first optimize for users, then for business customers, then for their own profits—degrading the experience as ads crowd feeds and pay‑to‑play rises (Spotify boosts, X Premium). In recommendation systems, that means more content designed to hook and sell, less serendipity. The linguistic symptom is trendbait: language increasingly crafted for algorithmic affordances.

Use Labels, Don’t Let Them Use You

Aleksic’s pragmatic take: labels can be helpful on‑ramps to community and style, but keep your descriptive range alive. If you’re a creator, pair popular tags with specific descriptors and storytelling that exceeds a label’s boundaries. If you’re a consumer, notice when the “-core” is deciding for you.

(Parallel: Kendall Walton’s “Categories of Art” argues categories frame how we perceive artwork. Aleksic shows platform labels frame how we perceive—and purchase—ourselves.)

Do “Gen Z” and “millennials” really speak differently—or have we memed generations into being? Aleksic notes that social science finds weak evidence for hard generational divides, but online culture has turned generations into living categories with shared canons and inside jokes. Emojis shift meaning by cohort (💀 over 😂). Micro‑behaviors caricature age: “millennial pause” versus “Gen Z shake,” the “Gen Z intern” as editing wizard. Media about generations begets more media about generations, a self‑fulfilling loop.

Brainrot and Flanderization

The label “brainrot” illustrates how a generational frame can pejorize language. “Skibidi” is no more nonsensical than Scooby‑Doo (both scats), yet “brainrot” casts Gen Alpha slang as cognitively corrosive. Aleksic warns that content dynamics Flanderize identities (exaggerate a trait until it becomes the whole). Creators Flanderize themselves for retention; platforms Flanderize subcultures (e.g., tradwives as hyper‑cottagecore). In language, life becomes “plot,” “sidequests,” “eras,” and the viewer is always the “main character”—narrative metaphors honed for short‑video storytelling bleed into everyday talk.

Folklore on Fast-Forward

Each platform now has its folklore: salt transitions, Bella Poarch head‑bops, and stitched catchphrases form a canon that new users learn to be “from here.” Comments even hyperlink the canon (a viral plastic‑surgeon parody caption listed “gen x breath / millennial pause / gen z shake,” each clickable to its search page). The more fluent you are in these references, the more you belong—and the more your speech reflects platform‑born patterns.

A Saner Middle

Aleksic doesn’t dismiss generational labels; he treats them as stories we tell about ourselves. Stories can build community and insight—or shrink us. Use the frame, but don’t let it flatten the person in front of you (or yourself).

(Note: This complements danah boyd’s “imagined audiences” work—our speech adapts to who we think we’re addressing. Generational lore narrows that imagined audience unless we keep it porous.)

Finally, Aleksic looks past English. The same forces play out in other languages. Spanish‑speaking teens borrow directly (“delulu”) or calque (“desvivir” for “unalive,” despite the RAE’s older definition). In French, African slang (cheh, wesh) flows into mainstream via rap and social media—chafing at the Académie Française’s prescriptive grip. In ASL, vertical video and one‑handed filming compress signs (DOG becomes snapped D‑G), puzzling elders and proving the medium is the message.

Whac‑A‑Mole, Global Edition

Chinese users evade censorship with homophones (héxié “harmonious” → héxiè “river crab” → shuǐchǎn “aquatic product”) and playful Kongish blends during the Hong Kong protests (“wai yi yau ghost” for undercover cop). A Georgia Tech study estimated that flagging every plausible homophone would nuke ~20% of posts—proof that language outsmarts lists. The pattern is constant: people will keep talking; tools will keep adjusting; creativity will find a path.

Diversity: Converging and Diverging

Yes, English dominates online (and some smaller languages face faster erosion), and regional accents show convergence (e.g., Texas vowel shifts toward “General American”). But dialectal diversity also migrates to digital: Swiftie, furry, K‑pop, and incel dialects replace some geographic variation with communal ones. The rule of thumb—“the longer a place is settled, the more dialects form”—now applies to platforms, not just towns.

Are We Cooked?

Aleksic closes with Chaucer: forms of speech change, yet people “succeed as well in love” as ever. That’s the stance here. Algorithms tilting language isn’t apocalypse; it’s a new medium pressing on old human tendencies: to euphemize death, coin in‑jokes, badge belonging, hawk style, and tell stories. If you see the loops—human motives ↔ platform incentives—you can choose when to play along, when to push back, and how to protect communities while letting language do what it has always done: adapt.

(Echo: David Crystal’s Language and the Internet documented an earlier inflection point. Aleksic updates the map for algorithmic feeds—and hands you a compass.)