How Is Social Media Secretly Transforming Human Language in Ways We Never Imagined?

What Happens When Algorithms Start Controlling How We Speak and Write Every Day? How Social Media Is Transforming the Future of Language

Discover how Adam Aleksic’s Algospeak explores the hidden ways social media algorithms are reshaping language, creating new words, and changing communication patterns both online and offline. Learn about the surprising impact of TikTok slang, content moderation, and digital platforms on modern linguistics.

Ready to discover how your daily social media scrolling is literally rewiring your brain’s language centers? Keep reading to learn about the fascinating linguistic evolution happening right under your nose – and how it might be changing the way you think, speak, and connect with others in ways you never realized.

Genres

Technology and the Future, Communication Skills, Society, Culture

Learn how social media is changing the way we speak

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.

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 summary, 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.

What makes the current moment distinct is not the existence of coded language, but its speed and scale. Where the Christian fish symbol took centuries to evolve and spread, modern digital euphemisms can emerge and proliferate across millions of users within weeks.Content creators across social media platforms face the challenge of crafting posts that will reach their audiences without triggering algorithmic penalties. A single misstep can mean the difference between viral success and digital obscurity.

Explicit content can result in creators being hidden from discovery feeds, dramatically limiting their reach. On platforms like YouTube, violations trigger demonetization, cutting off creators’ primary revenue stream of advertising shares.

In response, creators have developed an elaborate system of linguistic workarounds, creative respellings, and novel acronyms. Content creators discuss “SA” instead of sexual assault, “ED” for eating disorders, and refer to someone’s “wife” when they mean “life” to avoid triggering content warnings on mental health topics.

Visual codes have emerged alongside these textual innovations. The corn emoji serves as a substitute for “porn” based on phonetic similarity, while the chili pepper emoji suggests sexual content. Even spacing and punctuation become tactical – creators insert periods between letters or use asterisks to break up flagged terms.

Yet the platforms then adapt, creating a continuous cycle of innovation and enforcement. As social media companies update their moderation algorithms to recognize new terms, creators must constantly innovate, developing fresh alternatives to stay ahead. It’s a perpetual game of cat and mouse, with each side adapting to the other’s latest moves.

We’ve examined how language is influenced by the enforcement of content rules, but social media algorithms do much more than moderate content. Next, let’s discuss the algorithms that drive so much of online content today.

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.

Early Reddit exemplified this simplicity with its straightforward ranking system – posts were ordered by the number of upvotes minus downvotes, adjusted for age of post. The author, an early Reddit user, became systematic about his content promotion strategy. He experimented with timing his posts and choosing content that would resonate with the platform’s mechanics. Through careful observation and skill at working the algorithm, he managed to become one of the top 100 Redditors out of several hundred million users.

But something changed around 2017. Reddit abandoned its simple ranking system in favor of machine learning-driven personalized content recommendations. Rather than global rankings that all users could see, each person now received individually tailored feeds. This shift reflected broader industry trends, as platforms across the internet moved toward personalized algorithms designed to maximize user engagement and time spent on site. Each platform – YouTube, Instagram, X – now guards secret proprietary algorithms that determine what content users see, leaving content creators guessing at how to reach their audiences. But if there’s one algorithm that leads the pack when it comes to user retention, it’s that of TikTok. Let’s talk about it next.

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.

When someone encounters a new word, it’s like they’ve been exposed to a pathogen. The individual either rejects the word, or they incorporate it into their vocabulary, becoming a host who transmits it to others. This viral model helps explain the seemingly random success or failure of new words and phrases. Some achieve widespread adoption while others vanish without trace, following patterns that mirror biological evolution.

It’s a bit of a challenging idea, to be sure. But if we think of words and ideas as akin to viruses, something must dictate which ones survive and thrive. So what are the evolutionary pressures that shape linguistic evolution?

Social cachet is one driver. People embrace new language because it feels cool or trendy, or signals membership in a particular group. Terms become generational markers and tools for social bonding, conferring status on early adopters. Young people might say someone is “serving” (meaning they look amazing) or describe something as “low-key fire” (subtly excellent), using these terms to signal their cultural awareness and group membership.However, this coolness factor proves ephemeral. What’s hip today becomes outdated tomorrow, creating a constant cycle of linguistic turnover. Consider the term “bae” – a word for one’s romantic partner or loved one – which exploded in 2013–’14 but quickly became overused by advertisers.

Attention-grabbing terms often achieve rapid initial spread but burn out quickly. Two prominent Gen Alpha examples illustrate this pattern: “gyat” (referring to someone’s behind) and “skibidi” (a versatile but deliberately nonsensical term). These words generated immediate notice through their novelty, but lacked staying power.

Long-term survival requires what linguists call an endurance factor – a compelling reason for the term to stick around. For example, terms can become indispensable by describing previously unnamed phenomena, filling a semantic gap in the language. The expression “side-eye,” describing a suspicious or judgmental glance, is a good candidate, as it provides a precise term for a common but previously unlabeled behavior.

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.

The song’s huge success created a new feedback loop: packing trending keywords helped the video go viral. But the video’s virality then further entrenched those terms in popular usage. The author sensed a qualitative change had taken place – a new high-water mark in the trend of content designed as much for the algorithm as for any human audience.

The reach of these algorithmic trends extends far beyond TikTok itself. Surveys reveal that eighty percent of US parents have heard the word “rizz,” demonstrating how platform-specific slang now penetrates mainstream culture with unprecedented speed and scope. Increasingly, algorithms don’t just spread language change – they actively shape which linguistic innovations succeed, and how they do so.

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.

This emotional amplification is a fundamental bias that damages the quality of public discourse. News outlets and content creators, recognizing that extreme reactions drive engagement, craft messaging to provoke rather than inform. For example, terms like “slammed” or “destroyed” replace neutral verbs like “criticized” or “responded to.” Nuance gives way to sensationalism, and thoughtful voices are drowned out by those willing to take the most polarizing positions.

Another troubling dynamic is the effects of these algorithms on the brain. Social media today is an extremely competitive environment where algorithms pit every creator against every other in a relentless struggle for attention. The result is an escalating arms race toward increasingly hyper-stimulating content, as creators must constantly outdo one another to break through the noise and capture fleeting user engagement.

This constant barrage of extreme content is fragmenting our collective attention span, making it harder for all of us to focus on sustained tasks. People are reading less; students are struggling to remain on task. Many believe we’re witnessing a full-scale epidemic of social media addiction as platforms optimizing for maximum engagement succeed all too well.

Finally, a growing concern is the threat to language diversity itself. Current projections suggest that nearly half of the world’s approximately 7,000 languages will disappear within this century, with one language dying every other week. While the extinction of languages isn’t itself new, digital platforms accelerate the process by supporting only a tiny fraction of human linguistic diversity. Google’s services support merely 240 languages, while YouTube accommodates just 80.

These technological limitations force speakers of less-common languages into a difficult choice: participate in global digital communication or maintain their traditional linguistic practices. The fate of human linguistic diversity depends on whether communities can successfully balance the practical demands of digital participation with the preservation of their traditions, enhancing rather than diminishing the rich tapestry of human expression.

Conclusion

The main takeaway of this summary to Algospeak by Adam Aleksic is that algorithms are quietly reshaping human language in ways we’re just beginning to understand.

Content moderation has evolved into a complex system in which platforms actively shape which words, phrases, and ideas survive and thrive. Creators are increasingly creating content designed for algorithmic consumption as much as human preferences – cramming trending keywords into videos, using coded language to avoid detection, and optimizing every element from timing to hashtags to game recommendation systems. From TikTok’s “unalive” to the viral spread of “rizz,” we’re witnessing language evolution at unprecedented speed. Algorithmic forces are fragmenting our attention, privileging emotionally extreme content, and threatening linguistic diversity worldwide. The future of human expression depends on recognizing and responding to these powerful forces.