Academic Journal of Modern Philology

doi: 10.34616/ajmp.2025.27.3

ISSN 2299–7164

Vol. 27 (2025)

Open Access. Published by Committee for Philology of the Polish Academy of Sciences, Wrocław Branch and College for Interdisciplinary Studies, University of Wrocław. This work is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License.

Beata Bury

University of Bielsko-Biala, Foreign Language Centre of the University of Bielsko-Biala

bbury@ubb.edu.pl

ORCID: 0000-0002-5147-5394

Decoding Algospeak: Unveiling the Patterns of Linguistic Evolution in the Digital Age

Abstract

The subject matter of this study is algospeak, a form of coded language used by social media users to evade automated content moderation systems. This study investigates the intricate linguistic strategies employed in algospeak, encompassing emojis, acronyms, euphemisms, number, and symbol substitutions. The research perspective employed in this study is grounded theory, which involves systematic data analysis. The research material comprises an algospeak corpus sourced from platforms such as Know Your Meme, dictionary.com, and Urban Dictionary, which was meticulously analyzed to discern patterns and motivations underlying these linguistic adaptations. The study unveils that algospeak not only facilitates circumvention of censorship mechanisms but also serves to cultivate communal identity and provide support to marginalized communities. This study underscores the complex interplay between technology and human expression, highlighting the need for balanced content moderation that respects free speech and enhances digital literacy.

Keywords: algospeak, grounded theory, social media, content moderation

Introduction

In an era where social media algorithms determine the visibility and virality of content, the way people communicate online is increasingly shaped by the need to circumvent automated moderation systems. Terms and phrases are strategically altered to avoid censorship, evade bans, or ensure that messages are prominently featured in users’ feeds. This phenomenon, known as algospeak, represents a fascinating intersection of technology, language, and culture. It demonstrates how users develop and employ new linguistic strategies to adapt to the ever-changing landscape of digital communication (McCulloch 2019: 154–176; Roberts 2019: 112–130).

From substituting sensitive keywords with phonetically similar words or creative misspellings to using coded language that only in-group members can decipher, algospeak highlights the ingenuity of Internet users in their quest to express themselves freely. However, this evolution is not without its challenges. The need to constantly adapt language to outsmart algorithms can lead to miscommunication, exclusion of those unfamiliar with the latest terms, and the perpetuation of echo chambers where only those versed in the specific vernacular can participate (Wardle, Derakhshan 2017: 67–74).

Moreover, the rise of algospeak has broader implications for digital literacy and the preservation of free speech. It raises questions about the balance between moderating harmful content and fostering open, authentic communication. The constant evolution of these linguistic strategies reflects broader societal trends, where power dynamics between platform users and the platforms themselves are continually negotiated and contested (Gillespie 2018).

The implications extend to various spheres, including politics, where activists and dissidents may use algospeak to organize and spread messages under the radar of oppressive regimes (Cervi et al. 2021: 267–287). Similarly, marginalized communities might adopt algospeak to create safe spaces for discourse that might otherwise be suppressed or misunderstood. This dynamic also influences marketing and branding strategies, as businesses must remain agile, adapting their language to ensure that their content reaches the desired audience without falling foul of algorithmic filters (Donovan et al. 2022).

The aim of this article is to systematically decode the phenomenon of algospeak, uncovering the intricate patterns of linguistic adaptation in response to algorithmic oversight. By analyzing how digital communication evolves to navigate algorithmic constraints, this article offers a comprehensive exploration of the interplay between technology and language. Its originality lies in its dual focus: not only examining the creative strategies employed by users but also addressing the broader implications for digital discourse and societal dynamics. Understanding these dynamics is crucial for navigating and shaping the future of online discourse. Researchers, technologists, and policymakers must engage with these evolving linguistic patterns to foster a digital environment that respects both the need for moderation and the fundamental right to free expression. This balance will be essential in promoting a more inclusive and communicative digital society.

History of Coded Language

The history of coded language spans centuries, reflecting the evolving need for secrecy, identity, and subversion across various cultures. In ancient times, coded language was used for political and military purposes, such as the Spartans’ use of the scytale, a tool for encrypting messages. In the 19th century, Cockney rhyming slang emerged in East London as a playful yet practical linguistic code. It involved substituting phrases with rhyming counterparts, often shortened to the first word, like “apples and pears” (stairs) becoming just “apples” (Hellekson and Busse 2014). This form of slang allowed the working-class Cockneys to communicate covertly, often to confuse eavesdroppers or outsiders, and remains a vibrant part of London culture today (Tancer 2008).

Similarly, Polari was used in the mid-20th century primarily by gay men in Britain as a secretive form of communication during times of intense social stigma and legal persecution. Drawing from Italian, Romani, and other sources, Polari allowed users to speak openly about their experiences and identities without fear of exposure. Terms like “naff” (bad) and “zhoosh” (to spruce up) provided a layer of safety and solidarity within the LGBTQ+ community. Linguist Paul Baker has extensively studied Polari, highlighting its significance in providing a linguistic refuge and fostering a sense of community among its users (Baker 2019; Baker 2002).

In the digital age, leetspeak (or “1337speak”) developed among hackers and online communities in the 1980s and 1990s. Originating from the word “elite,” leetspeak uses character substitutions to replace letters with numbers or other symbols: “leet” becomes “1337,” “hacker” becomes “h4x0r.” This language not only served as a badge of identity among early computer enthusiasts but also functioned to bypass simple text filters and censorship. Its playful complexity and subversive nature reflect the hacker ethic of creativity and defiance. Researchers such as Tim Jordan have explored how leetspeak embodies the hacker culture’s values and its impact on digital communication (Jordan 1999).

Recently, algospeak has emerged in response to social media algorithms, with users intentionally altering words to avoid content moderation—examples include “seggs” instead of “sex” (Curtis 2022) or “clock app” for “TikTok” (Levine 2022) to evade bans on certain sensitive topics. These various forms of coded language highlight the continuous human endeavor to adapt language for secrecy, solidarity, and survival in changing social landscapes.

The reasons for developing and using coded languages are multifaceted. They often arise from a need to protect the speaker from persecution or censorship, to create a sense of community and identity among marginalized groups, and to subvert existing power structures. Whether in the form of Cockney rhyming slang, Polari, leetspeak, or algospeak, coded languages provide a way to communicate securely and expressively in environments where open communication may be dangerous or restricted (Fairman 2009).

Research Questions

This study is centred on the following research questions:

1. What linguistic strategies and modifications do users employ in algospeak to circumvent algorithmic detection and moderation on digital platforms?

This question seeks to explore the specific linguistic techniques used, such as acronyms, creative misspellings, and symbolic representations, to evade automated content moderation systems.

2. To what extent do algorithmic changes and updates influence the evolution of linguistic patterns in algospeak?

This question investigates how updates to content moderation algorithms and platform policies drive shifts in the strategies and effectiveness of algospeak techniques over time.

3. What role does user intent play in the adoption and adaptation of algospeak linguistic patterns?

This question delves into the motivations behind users’ decisions to modify their language, exploring whether they seek to evade detection, express identity, or achieve other communicative goals.

4. What are the implications of algospeak for language evolution and digital literacy skills among users?

This question explores the broader impacts of algospeak on language use and understanding, including its potential influence on language learning, literacy levels, and the development of critical thinking skills in digital environments.

These research questions aim to deepen our understanding of algospeak as a complex linguistic phenomenon shaped by technological, social, and cultural factors. These factors highlight the multifaceted nature of algospeak and its implications for digital communication, governance, and societal norms. The research provides a detailed understanding of how language adapts to technological constraints and reflects broader societal dynamics. By discussing the intricacies of algospeak, the research expands our understanding of how language evolves in response to technological advancements and societal shifts in the digital age.

Research Method

The study employs grounded theory as its primary research method to investigate the linguistic patterns of algospeak. Grounded theory, pioneered by Glaser and Strauss (1967), is a qualitative research approach that emphasizes theory generation from systematically collected and analyzed data rather than testing pre-existing hypotheses. This method is particularly suitable for exploring dynamic and evolving phenomena like algospeak, where the focus is on understanding the processes and meanings embedded in social interactions and language use (Charmaz 2006).

In grounded theory, data collection and analysis proceed iteratively, allowing researchers to continuously refine their understanding of the topic under study. The process typically involves open coding (initial categorization of data), axial coding (connecting categories to explore relationships), and selective coding (integration of categories into a cohesive theory). This structured yet flexible approach enables researchers to capture the complexity and nuances of algospeak linguistic adaptations across different digital platforms (Strauss and Corbin 1990).

By utilizing grounded theory, this study aims to develop a comprehensive understanding of how users modify their language to navigate and circumvent algorithmic detection, exploring the socio-cultural contexts and motivations driving these linguistic adaptations. This methodological choice ensures that the findings are grounded in empirical data and provide insights that are deeply rooted in the lived experiences and practices of digital communication.

Mode of Data Extraction and Presentation

To extract data for the study on algospeak using grounded theory, researchers leverage platforms like Know Your Meme, dictionary.com and Urban Dictionary, which are rich repositories of user-generated content and slang. The process begins by systematically searching these platforms for terms and phrases associated with algospeak. This involves identifying relevant entries on Know Your Meme, where users document and explain the origins and usage of Internet memes, often including instances of modified language. Similarly, Urban Dictionary provides user-submitted definitions and examples of slang, including terms that have evolved to bypass algorithmic detection.

Researchers start by compiling a comprehensive list of potential algospeak terms from these sources, paying attention to entries that explicitly mention evasion of content moderation or demonetization. Each identified term is then scrutinized for its context, usage, and variations. For example, entries might provide insights into the phonetic changes (like “seggs” instead of “sex”), the creative use of symbols (such as “” for buttocks), and the cultural or social contexts in which these terms are used.

In this study, the corpus consists of 96 algospeak words, sourced from platforms like Know Your Meme, dictionary.com, and Urban Dictionary. It is important to note that not all extracted words are discussed in the article. This selective discussion is due to the need to maintain a focused analysis on the most relevant and impactful examples of algospeak. Once a substantial dataset is gathered, the researchers proceed with open coding, breaking down the data into categories and subcategories based on the types of algospeak identified. They then move to axial coding, analyzing the relationships between these categories and the specific contexts of their use. This involves linking particular terms to the themes or topics they are associated with, such as political discourse or health misinformation.

The results of the study will be presented in a table format. By structuring data into columns, the table enhances readability and ensures that each piece of information is clearly delineated. This organization aids in quickly grasping the breadth and depth of the data collected. Moreover, tables provide a concise summary of data that is accessible at a glance. This format is particularly useful for conveying complex information succinctly, making it easier for readers to comprehend and interpret the findings of the study.

By methodically extracting and analyzing data from Know Your Meme, dictionary.com and Urban Dictionary, researchers build a robust foundation for understanding the patterns and dynamics of algospeak. This structured approach ensures a thorough and contextually rich dataset, enabling the development of a grounded theory that accurately reflects the strategies users employ to navigate and subvert algorithmic controls in digital communication.

Discussion of the Findings

The analysis identified a few regular patterns in algospeak, which are used creatively by social media users to bypass algorithmic moderation. These patterns include the use of emojis, acronyms, euphmemisms as well as symbol and number substitutions. Each pattern showcases the ingenuity and adaptability of users in maintaining communication despite the presence of content moderation systems. Below, we discuss each linguistic pattern and present the results in a table.

Emojis in Algospeak

Emojis function as visual metaphors and symbolic stand-ins, substituting for sensitive lexical items that may be flagged by moderation systems. Below is a table of emojis commonly used in algospeak, along with their typical meanings and contexts. The analyzed corpus revealed a total of 25 emojis.

Table 1. Emojis used in algospeak. Source: Author’s own work

No.

Emoji

Meaning

Context Use

1.

(eggplant)

male genitalia

used in place of explicit terms

2.

(peach)

buttocks

common in sexual content discussions

3.

(corn)

porn

common in sexual content discussions

4.

(droplets)

ejaculation

common in sexual content discussions

5.

(cat)

female genitals

common in sexual content discussions

6.

(watermelon)

solidarity with Palestine

used in discussions about sensitive political issues

7.

(sunflower)

solidarity with Ukraine

used in discussions about sensitive political issues

Based on the table above, it can be observed that (eggplant) and (peach) operate as metonymic representations of male genitalia and buttocks, respectively. These choices are not arbitrary but rather embedded in widely recognized visual-linguistic associations. This mirrors the semiotic process of iconicity, where the form resembles or suggests its referent (Chandler 2007). Such emoji use exemplifies metonymic and metaphorical extensions common in digital discourse (Danesi 2017; Skovholt et al., 2014), where users rely on shared visual-linguistic cues to convey implicit meaning. The figurative nature of emojis highlights their polysemy and dependence on contextual pragmatics for interpretation (Ge, Herring 2018; Dresner and Herring 2010). Users must rely on shared cultural or subcultural knowledge to decode meaning, positioning emojis as a form of coded in-group language (Zappavigna 2012). For instance, the emoji has emerged as a symbolic allusion to Palestinian resistance, drawing on the fruit’s visual resemblance to the colours of the Palestinian flag. Similarly, serves as a cultural synecdoche for Ukraine, frequently appearing in digital discourse as a visual shorthand for solidarity during the ongoing conflict. These uses demonstrate that emojis, though visually simple, are rhetorically complex and deeply embedded in collective semiotics (Danesi 2017; Tagg 2015).

The meanings attributed to emojis in algospeak reflect a high degree of semantic flexibility. Emojis like (droplets) and (cat) have undergone semantic shift, evolving to represent concepts like ejaculation and female genitals, respectively, in specific contexts. This flexibility indicates that the meanings of symbols can shift dramatically based on usage patterns and community conventions, highlighting the fluidity and context-dependence of digital language. As inherently ambiguous signs, emojis rely heavily on pragmatic inference and indexicality; their meaning is inferred based on who uses them, how, and in what context. The nuances in their interpretation, influenced by contextual factors, make emojis a complex and sometimes cryptic element of digital communication. As such, deciphering their true intent requires a deep familiarity with the specific context in which they are used (Ge, Herring 2018).

The rise of algospeak, with its reliance on emojis, signifies a broader trend in language evolution influenced by technological advancements and digital culture. Just as spoken and written languages have evolved over centuries, digital communication is rapidly developing its own set of rules and conventions. Emojis, initially designed as simple visual representations, have transformed into powerful linguistic tools that shape online discourse.

Acronyms in Algospeak

Another linguistic pattern observed in the analyzed corpus of algospeak is the use of acronyms. The corpus analysis identified 17 acronyms. The table below presents examples of acronyms that are popular among algospeak users:

Table 2. Acronyms in algospeak. Source: Author’s own work

No.

Acronym

Meaning

1.

SH

self-harm

2.

ED

eating disorder

3.

SA

sexual assault

4.

SS

super straight

5.

SW

sex worker

6.

yt

white (person)

It can be noticed that the use of acronyms such as SH (self-harm), ED (eating disorder), SA (sexual assault), SS (super straight), and SW (sex worker) serves a critical function in navigating the complex landscape of content moderation on social media platforms. These acronyms are often used to refer to sensitive or controversial topics that might otherwise trigger content filters or lead to posts being flagged or removed. By coding these terms, users can discuss critical issues while circumventing automated moderation systems designed to detect and limit the spread of certain types of content (Chancellor et al. 2016: 1201–1213). The acronym “yt” exemplifies a phonetic abbreviation, retaining its auditory resemblance to “white” (/waɪt/), thus maintaining perlocutionary effect despite orthographic alteration. These acronyms can also serve euphemistic functions by muting the emotive force of their full forms, an approach consistent with strategies of dysphemism avoidance and euphemistic substitution (Allan and Burridge, 1991). Beyond that, they carry a sociolinguistic function, acting as boundary markers that signify membership in specific online communities and allow discussions of sensitive topics such as racism, trauma or marginalization in semi-private codes.

Linguistically, the use of acronyms aligns with the concept of lexical economy, as described by scholars like David Crystal (2008: 156–159) or Jean Aitchison (2001: 78–82). Lexical economy refers to the tendency of language users to create shorter, more efficient forms of communication, especially in digital contexts where brevity is essential (Crystal 2008). This phenomenon is particularly evident in the use of acronyms or initialisms such as ED (eating disorder), SA (sexual assault), or SS (super straight), which exemplify how users condense complex, socially loaded concepts into abbreviated forms. From a linguistic perspective, these abbreviations serve multiple functions. On the one hand, they enhance processing efficiency and facilitate rapid communication in environments like social media, where space and attention are limited. On the other hand, they can act as euphemistic shields; by depersonalizing or distancing the speaker from sensitive content, these forms may reduce emotional impact or soften taboo expressions, aligning with what pragmatics scholars refer to as face-saving strategies (Brown, Levinson 1987).

The adaptation of language to circumvent moderation can be seen as a form of digital resistance, where users push back against the constraints imposed by technology and platform policies. This resistance highlights the resilience and creativity of online communities in preserving their voices and narratives despite restrictive environments. It also underscores the importance of understanding and analyzing these linguistic innovations to ensure that the platforms designed to connect us do not inadvertently silence critical conversations.

Euphemisms in Algospeak

Euphemisms, as extracted from the analyzed corpus (see Table 3), serve as an insightful lens for understanding algospeak and its users. These linguistic constructs not only reveal strategies to circumvent content moderation but also highlight the social, cultural, and psychological dynamics within online communities. It needs to be noted that a total of 35 euphemisms were identified in the analyzed corpus.

Table 3. Euphemisms in algospeak. Source: Author’s own work

No.

Euphemism

Meaning

1.

accountant

sex worker

2.

Backstreet Boys reunion tour

COVID-19 pandemic

3.

kermit sewer slide

Commit suicide

4.

Le Dollar Bean

lesbian

5.

leg booty community

LGBT community

6.

mascara

male genitals

7.

nip nops

nipples

8.

oui’d

weed

9.

panini

COVID-19 pandemic

10.

saltine

white person

11.

seggs

sex

12.

tism

autism

13.

unalive

dead

On the basis of the data collected in Table 3, it can be noticed that humour plays a significant role in the creation of algospeak euphemisms. According to linguist Victor Raskin’s script-based semantic theory of humour, jokes often rely on a dual interpretation of words or phrases (Raskin 1985). Algospeak euphemisms, such as “oui’d” for weed or “panini” for the COVID-19 pandemic, frequently employ humour, making serious or taboo topics more approachable and less threatening through semantic play or pragmatic mitigation. Phonologically, the euphemism “oui’d” operates through homophony and phonemic manipulation. The French word “oui” is pronounced /wi/, which is phonetically identical to the English word “wee,” the first syllable in “weed” /wiːd/. This phonetic similarity allows “oui” to function as a covert stand-in for “weed.” The addition of the voiced alveolar stop /d/ completes the phonological construction of the target word “weed,” resulting in “oui’d.” The transformation thus relies on segmental overlap and the strategic replacement of familiar phonemes in order to maintain auditory resemblance while achieving a euphemistic disguise. This type of manipulation exemplifies phonological approximation (Crystal, 2003), a common strategy in algospeak for evading platform moderation systems that detect explicit keywords. Regarding “panini,” it functions as a metaphorical euphemism for “pandemic,” relying on semantic incongruity and humorous dissonance. A panini is a harmless, even comforting, object (a toasted sandwich), in stark contrast to the severity of a global health crisis. This dissonance underpins the joke and offers a form of semantic softening, turning an anxiety-laden topic into something trivial. The shift is also an example of lexical metaphor (Lakoff and Johnson 1980), where a word from one semantic field (food) is mapped onto another (public health crisis) for expressive or humorous effect.

The ability to maintain communication on sensitive topics is critical, especially in communities that rely on social media for support. For example, discussions about mental health issues, such as suicide or depression, are often flagged by algorithms designed to prevent harmful content. “Kermit sewer slide” employs a metaphorical and cultural reference, invoking the image of Kermit the Frog sliding down a drain to indirectly denote suicide, thereby evoking emotional distance and humor. This phrase relies on semantic incongruity, using humor and absurdity to soften the serious and taboo nature of suicide. Pragmatically, it serves as a face-saving strategy that helps speakers discuss a difficult topic while avoiding emotional discomfort and evading content moderation algorithms (Brown and Levinson 1987; Crawford and Gillespie 2016). Meanwhile, “tism” is a clipped form derived from “autism,” which condenses the term into a more casual, possibly less stigmatizing variant. It can be noticed that “tism” retains the distinctive final consonant cluster, which helps maintain its recognizability despite the truncation.

Euphemisms in algospeak often reflect the cultural and social identities of specific online communities. The term “leg booty community” for the LGBT community, for example, not only avoids triggering content filters but also fosters a sense of belonging among users who are familiar with the term. This use of in-group language strengthens community bonds, reinforces shared identities and serves as a marker of social identity (Eckert 2000: 71). Additionally, the playful and metaphorical nature of “leg booty community” also functions as a politeness strategy, mitigating the potential social or emotional risks associated with discussing marginalized identities in a monitored environment (Brown, Levinson 1987).

Automated content moderation systems use algorithms to scan text for keywords and patterns that might indicate rule violations. These systems are a crucial tool for managing the vast amount of content on social media platforms. However, their reliance on fixed keywords can lead to over-censorship or misinterpretation of context. As a result, users have developed innovative linguistic strategies to preserve their communicative freedom in the face of surveillance. Euphemisms like “seggs” or “unalive” are not merely avoidance tactics, but they demonstrate users’ linguistic creativity and adaptability. These modified forms exhibit phonological resemblance to the censored terms while altering spelling just enough to avoid detection, exemplifying what linguists refer to as orthographic variation (Sebba 2007). This adaptive use of language reflects the broader human tendency to find ways around constraints, similar to how slang evolves to circumvent social taboos (Gillespie 2018: 50).

Symbol and Number Substitutions in Algospeak

The following table presents the findings from the analyzed corpus of algospeak, showcasing various substitutions used to evade censorship. These substitutions reveal the creative strategies users employ to navigate automated content moderation systems. The corpus analysis identified 19 symbol and number substitutions.

Table 4. Symbol and number substitutions in algospeak. Source: Author’s own work

No.

Type of substitution

Example substitution

Algospeak word

Meaning

1.

number substitution

<00> → <oo>

b00bs

breasts

2.

symbol substitution

<$> → <s>

le$bean

homosexual woman

3.

symbol substitution

<!> → <i>

sh!t

a censored word for “shit”

4.

number substitution

<8> → <aight>

str8

straight

5.

number substitution

<1> → <i>

k1ll

kill

Words like “b00bs” (breasts), “le$bean” (lesbian), “sh!t” (shit), and “str8” (straight) form a coded language that insiders understand while remaining obscure to outsiders, including automated censorship systems. This practice aligns with the principles of anti-language, where linguistic creativity serves both communicative and identity-affirming purposes. Anti-language, as defined by linguist Michael Halliday (1976), is used by subcultures to resist control by dominant social groups. In the case of algospeak, users employ inventive substitutions to maintain their freedom of expression and foster a sense of identity among those who share their linguistic code. Algospeak stands out as a tool for maintaining freedom of expression in environments with strict content controls, reflecting the ingenuity and resilience of online communities.

On the basis of the analysed corpus, it can be noticed that algospeak substitutions are not always consistent. For example, the letter “i” might be replaced with an exclamation mark “!” or the number “1”. This inconsistency functions as a form of adaptive variability, akin to what Labov (1972) described in spoken language variation, where certain linguistic forms fluctuate based on social and contextual pressures. In the case of algospeak, such pressures are algorithmic rather than social, but the user’s response is similar: develop a shifting code to maintain communicative goals while avoiding penalties.

Conclusion

This study underscores algospeak as an emergent, adaptive register that exemplifies the complex interplay between language, technology, and power. Rooted in the necessity to evade algorithmic detection, algospeak draws on diverse linguistic resources from orthographic manipulation (“seggs” for “sex”), numerical substitution (“k1ll” for “kill”), euphemisms (“unalive” for “dead”) to the strategic deployment of emojis (e.g. , ) and multimodal cues. These innovations reflect broader sociolinguistic processes of lexical creativity, pragmatic adaptation, and semiotic recontextualization (Crystal 2008; McCulloch 2019).

Based on the analysis of the corpus, it is evident that algospeak operates at the intersection of several established forms of language variation. Like slang, it is marked by rapid innovation, in-group identity signaling, and resistance to normative language forms (Eckert 2000). Like anti-languages, as theorized by Halliday (1976), algospeak is used by marginalized or surveilled communities to maintain communicative secrecy and solidarity under external pressures. However, unlike traditional anti-languages, which often arise in closed or subcultural settings (e.g., criminal groups or prisoners), algospeak is both mainstreamed and platform-specific, developing in response to algorithmic regimes rather than societal or legal oppression per se. It also shares features with cryptolects, such as Polari (Baker 2002, 2019), in that it obscures meaning from outsiders while fostering group cohesion, but differs in being decentralized, ephemeral, and shaped largely by machine surveillance rather than human authority.

The analysis reveals that the most frequently employed communicative tools, euphemisms and emojis, are not only semantically rich and emotionally expressive, but also play a crucial role in shaping digital identity. They often carry layered meanings that rely on context and mutual understanding, reinforcing group dynamics and fostering a sense of in-group communication among users navigating moderated online spaces (Ge, Herring 2018). This points to a broader trend in digital communication: the increasing reliance on multimodal discourse, where meaning is constructed through a blend of visual, textual, and contextual cues (Cervi et al. 2021; Darvin 2022).

Moreover, the study supports existing research on the sociolinguistic motivations for language change in digital environments. Algospeak is not solely instrumental, but it also fulfills social and affective functions, allowing users to construct identity, express dissent, and build community within algorithmically constrained spaces (Chancellor et al. 2016; Gillespie 2018). For marginalized groups, in particular, it becomes a linguistic strategy of both survival and resistance.

Yet, this online language comes at a cost. The use of opaque or rapidly shifting terms can result in communicative asymmetry, where only those “in the know” can decode messages, potentially exacerbating exclusion and reducing cross-community understanding (Wardle and Derakhshan 2017). The very features that make algospeak subversive and creative also complicate accessibility and accountability, raising ethical and pedagogical concerns for digital literacy and platform governance.

In conclusion, algospeak exemplifies the fluidity and resilience of human language in the face of technological constraints. It represents a new kind of digital sociolect, one shaped by machine surveillance, community needs, and the affordances of platform-specific design. As such, it offers rich terrain for future linguistic research. Longitudinal corpus analyses could track its diachronic changes, while cross-cultural comparisons may reveal how similar pressures produce divergent linguistic outcomes in different digital ecologies. Drawing from sociolinguistics, digital media studies, and discourse analysis, such research can contribute to a more comprehensive understanding of how language continues to evolve in algorithmically governed spaces.

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Internet references

Table 1

cat emoji. Retrieved from https://www.urbandictionary.com/define.php?term=%F0%9F%98%BA on 15 July 2024.

corn emoji. Retrieved from https://www.dictionary.com/e/slang/algospeak/ on 15 July 2024.

eggplant emoji. Retrieved from https://www.urbandictionary.com/define.php?term=Eggplant on 15 July 2024.

peach emoji. Retrieved from https://www.urbandictionary.com/define.php?term=peach%20emoji on 15 July 2024.

sunflower emoji. Retrieved from https://www.urbandictionary.com/define.php?term=Pushing%20Sunflowers on 15 July 2024.

water droplets emoji. Retrieved from https://www.urbandictionary.com/define.php?term=%F0%9F%8D%86%F0%9F%92%A6 on 15 July 2024.

watermelon emoji. Retrieved from https://www.dictionary.com/e/emoji/watermelon-emoji/ on 15 July 2024.

Table 2

ED. Retrieved from https://www.urbandictionary.com/define.php?term=EDtwt on 16 July 2024.

SA. Retrieved from https://www.urbandictionary.com/define.php?term=SA%27s on 16 July 2024.

SH. Retrieved from https://www.urbandictionary.com/define.php?term=SH on 16 July 2024.

SS. Retrieved from https://www.urbandictionary.com/define.php?term=Super%20Straight on 16 July 2024.

SW. Retrieved from https://www.urbandictionary.com/define.php?term=SW on 16 July 2024.

yt. Retrieved from https://www.urbandictionary.com/define.php?term=yt on 16 July 2024.

Table 3

accountant. Retrieved from https://www.urbandictionary.com/define.php?term=Accountant on 17 July 2024.

Backstreet Boys reunion tour. Retrieved from https://www.urbandictionary.com/define.php?term=Backstreet%20Boys%20Reunion%20Tour on 17 July 2024.

kermit sewer slide. Retrieved from https://www.urbandictionary.com/define.php?term=kermit%20sewer%20slide on 17 July 2024.

Le Dollar Bean. Retrieved from https://www.urbandictionary.com/define.php?term=Le%20Dollar%20Bean on 17 July 2024.

leg booty community. Retrieved from https://www.urbandictionary.com/define.php?term=leg%20booty on 17 July 2024.

mascara. Retrieved from https://www.urbandictionary.com/define.php?term=mascara&page=6 on 17 July 2024.

nip nops. Retrieved from https://www.urbandictionary.com/define.php?term=nip%20nop on 17 July 2024.

oui’d. Retrieved from https://www.urbandictionary.com/define.php?term=oui%27d on 17 July 2024.

panini. Retrieved from https://www.urbandictionary.com/define.php?term=global%20panini on 17 July 2024.

saltine. Retrieved from https://www.urbandictionary.com/define.php?term=Saltine on 17 July 2024.

seggs. Retrieved from https://www.urbandictionary.com/define.php?term=Seggs on 17 July 2024.

tism. Retrieved from https://www.urbandictionary.com/define.php?term=Tism on 17 July 2024.

unalive. Retrieved from https://www.urbandictionary.com/define.php?term=Unalive on 17 July 2024.

Table 4

b00bs. Retrieved from https://www.urbandictionary.com/define.php?term=b00b on 17 July 2024.

k1ll. Retrieved from https://www.urbandictionary.com/define.php?term=K1LL on 17 July 2024.

le$bean. Retrieved from https://www.urbandictionary.com/define.php?term=Le%24bean on 17 July 2024.

sh!t. Retrieved from https://www.urbandictionary.com/define.php?term=sh%21t on 17 July 2024.

str8. Retrieved from https://www.urbandictionary.com/define.php?term=str8 on 17 July 2024.

Received:

30.07.2024

Reviewed:

20.10.2024

Accepted:

16.03.2025