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Name: Algospeak
Rating: 4.4 (34 reviews)
Author: Adam Aleksic

Adam Aleksic

How Social Media Is Transforming the Future of Language

4.4 (34 ratings)

22 mins

Brief summary

Algospeak examines how algorithm-driven content moderation shapes online discourse, influencing communication, behavior, and society. Adam Aleksic provides insights into the impacts of these algorithms and suggests strategies for more informed digital engagement.

Topics

Lifestyle & Pop Culture Digital Transformation Social Media Culture

Table of Contents

Algospeak
Summary of 6 key ideas

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Key idea 1 of 6

What’s in it for me? Learn how social media is changing the way we speak

Have you ever noticed that different online content platforms sound different? With YouTube, TikTok, Instagram Live, and beyond, it’s almost as if different social media platforms have their own accents.YouTube creators speak with rapid-fire delivery and minimal pauses – since any silence is an opportunity for someone to click away. TikTok creators use exaggerated intonation and strategically stretch out words like “aaand” and “soooo,” to keep viewers watching just a few seconds longer.

It’s no coincidence. These are adaptations to each platform’s algorithm – the specific way each platform measures, prioritizes, and rewards user engagement.

In this Blink, we’ll look at how algorithms are quietly but powerfully reshaping the words and phrases we use, and our very ways of speaking. From content moderation systems to recommendation engines, we’ll see how social media technology is reshaping human expression in ways we’re only just starting to grasp.

When platform speak goes offline

In 2024, a museum placard sparked a viral controversy – a controversy that hints at the way digital algorithms are reshaping human language. The Seattle Museum of Pop Culture held an exhibition commemorating Kurt Cobain on the anniversary of his death. Beneath a moving photo of the iconic singer was a placard containing the jarring phrase, “Kurt Cobain un-alived himself at 27.” Backlash soon followed. Critics called the use of internet slang in such a solemn context deeply disrespectful and tone-deaf.

But the controversy reveals something deeper than poor curatorial judgment. The term “unalive” didn't emerge organically from youth culture or creative expression – it was born of an algorithm.

The term “unalive” emerged as an ironic synonym for “kill” or “die.” The phrase became widely adopted around 2019, following new censorship enforcement by the Chinese Communist Party. Since ByteDance, the creator of TikTok, is a Chinese company, they began enforcing keyword searches against violent words, prompting users to develop creative alternatives.

The linguistic innovation of “unalive” spread rapidly across platforms and cultures, with Spanish-speaking youth adopting “desvivir” – a construction playing on “vivir,” meaning “to live.”A similar pattern is “s-e-g-g-s” as an alternative for “sex,” where users replace letters to avoid automated detection. Both examples illustrate how content moderation algorithms are actively reshaping language, forcing users to develop coded alternatives.

These coded alternatives then enter mainstream usage. Young people now use these terms naturally, unaware of their origins, leading to awkward moments when platform-specific slang appears in formal settings like classrooms or museums. Algorithmic pressures are driving language change, even offline.

Hiding from the algorithm

Euphemism is one way in which content creators can avoid the axe of moderation. But there are many others.

People have been searching for creative ways of avoiding censorship from time immemorial. Norman Mailer replaced the f-word with “f-u-g” in The Naked and the Dead to navigate publishing restrictions. Going from the profane to the sacred, we have such ancient examples as the stylized fish symbol, often seen now on the bumper stickers that Christians use to signal their faith. This originated in the period of early Christianity when believers used it to identify each other in an era of Roman persecution.

The rise of machine learning

What’s the first internet-specific term you ever heard? If you’re in your thirties, it might have been “pwned” from gaming forums in the early 2000s, or perhaps “rickrolled” from that infamous YouTube prank involving Rick Astley’s “Never Gonna Give You Up.” If you’re a bit older, you might remember first encountering “LOL” in early chat rooms or hearing someone described as a “newbie” on bulletin board systems. These terms didn’t emerge from corporate marketing departments or viral campaigns; they grew organically from the communities that used them.

Before roughly 2004, the internet was fundamentally decentralized, scattered across message boards, forums, and early websites. The next wave of the internet introduced massive centralization through platforms like Facebook, Twitter, and YouTube. These social networks connecting millions of users within unified ecosystems allowed new terms to spread faster than ever before. Yet even these early social platforms operated on relatively simple, transparent systems that users could understand and navigate.

What it means to “go viral”

Most people know memes as humorous images with bold text – from the early “I Can Has Cheezburger?” cats to the so-called Wojaks, those simple black-outlined drawings of bald men expressing various emotions. But did you know the term “meme” has its roots in evolutionary biology?The term was coined by biologist Richard Dawkins in his 1976 book The Selfish Gene. Dawkins derived it from the Greek word mimema, meaning “something imitated,” and deliberately shortened it to echo “gene.” In doing so, he drew a fascinating parallel between how genetic information replicates through biological evolution and how cultural information spreads through human societies.

In Dawkins’ original conception, memes encompass any units of cultural transmission – ideas, behaviors, or vocabulary – that jump from person to person through imitation. This biological metaphor reveals something about how language evolves: words and concepts function like viral organisms, as if us humans are mere hosts among which they spread.

Made for the machine

TikTok’s algorithm represents a quantum leap in content recommendation. Users often report the uncanny experience of the app practically seeming to read their minds – anticipating their interests before users are even aware of them. The platform’s success depends on its ability to track user behavior with remarkable precision – monitoring not just what users watch, but how long they do so, what they skip and when, and many other behavioral signals. Using advanced machine learning (or else some kind of black magic), TikTok’s algorithm optimizes for maximum user engagement, keeping people scrolling for hours.This algorithmic sophistication creates powerful incentives for linguistic trends. When a new slang term emerges, users who engage with it trigger the algorithm to suggest more content featuring the same term. This only incentivizes creators to jump on trendy terms. TikTok’s audio-reuse feature amplifies this effect, allowing sounds and phrases to spread across thousands of videos with minimal friction.

The word “rizz” – short for charisma – exemplifies this phenomenon. Emerging in 2023, “rizz” spread rapidly through TikTok’s recommendation engine. The viral explosion reached its peak in October 2023 with the “Rizzler song,” which cannily crammed multiple trending Gen Alpha slang terms like “rizz,” “sigma,” and “skibidi” together into deliberately nonsensical lyrics.

Engagement and its hidden costs

Goofy new slang terms are all well and good, but there’s more at stake. While algorithms were designed to connect and entertain people, they have some serious unintended consequences. Let’s look at them.

In 2010, Wharton professors conducted an analysis of New York Times articles – specifically, how and when users shared them. They discovered a troubling pattern: the more emotionally charged a piece of content was, the more likely it would be shared. Notably, it didn’t matter whether the emotion was positive or negative; what mattered was its intensity. This finding has been replicated again and again, across multiple formats and platforms.

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What is Algospeak about?

Algospeak examines how digital platforms are transforming language. It shows how internet culture creates new ways of speaking and writing, shaping both online and offline communication. It also reveals how this technology-driven linguistic change reflects broader shifts in social norms, as well as our relationship to language itself.

Who should read Algospeak?

Linguistics enthusiasts interested in how technology is accelerating language change
Tech-savvy professionals looking to understand online platforms
Anyone interested in slang and popular culture

About the Author

Adam Aleksic is a linguistic and content creator known as the “Etymology Nerd,” with an audience of over two million followers. He founded the Harvard Undergraduate Linguistics Society, has lectured at prestigious universities like Stanford, Yale, and Georgetown, and delivered a TEDx talk at the University of Pennsylvania. His work has been featured in the Washington Post, New York Times, Economist, and Guardian.

Categories with Algospeak

Society & Culture Technology & the Future Communication Skills

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