By: Peter Conlin

Improbable: Hacking the Predictive System

We live in a time obsessed with forms of prediction, wherein prediction, despite its integration into computational technologies and its presumed rationality, is often indistinguishable from something like fate, especially if one is on the wrong side of the uncertainty circuits. It is fundamental to financial capitalism with its ‘modes of prediction’¹ wherein investment risk, directed by predictive abstractions, replaces labour as ‘the fount of value’,² and is central in the strategic decision-making of corporations and state agencies through a full suite of predictive technologies; however the focus of this commentary is on the predictive within the everyday and intimate levels of the self. In any of these dimensions we should not take prediction on its own terms (i.e. the domain of mathematics installed into digital systems, a fine-tuned instrument of data science, etc.). It is larger than this, and in a way, less than this, as it is bound to the mundane, as well as to mythical currents and powers of authority. The crux of the predictive lies in the zones where data-science meets the drawing of lots, and then disappears into the weird psyches of 21^st century lives. If there are points to hack, it is where and when a probabilistic system transmutes into an ambient condition. What if a trace of indeterminacy is injected into these points? And if no such transformation or sabotage is possible now, at least this could be a site to hone aleatory guile? These are the areas and ideas I want to explore in this speculative and generative text, which will delve into the web project Petit Tube (petittube.com) as a specific case.

For those in societies with an intensive integration into digital technologies, probabilistic calculations seep into how we think, act, and interact. In yet another layer of machinic living, underlying both production and social reproduction, prediction is produced through digital stochastic reasoning, that combines huge volumes of data with mathematical processes (e.g. Markov chains) to convert randomness into likely outcomes, and quantifies the degree and type of uncertainty. The data-quantification of our lives puts us into the probability circuit, with the naive ideal of better living (‘the more of social life that can be translated into metrics and measured over time, the better the decisions, products, or services provided by companies, political actors, and governments will be.’³) Work, health, security and cultural systems are increasingly driven by processes that govern through stochastic variables. The fact that many of these predictions fail or are unreliable doesn’t lessen the obsession and involvement of our energies into quantitative anticipation in any way—it’s not that kind of prediction. We aren’t disappointed or surprised if they ‘come true’ or not, but we might change platforms. These are predictions without a future as such, and shift experience from specific dread or promise into a ground force that manifests as a type of pressure and reconfigures motivation and expectation. 

As I will elaborate, prediction is perhaps one among many other figures leading into a social-temporal assembly or a source of social gravity. The crux of what I am calling prediction lies in our relation to contingency, which is especially heightened in turbulent times. As such, probabilistic rationality is one figuration (dominant, digital capitalist, supposedly technocratic) among a related set identified by Matthew Fuller and Olga Goriunova including: ‘luck, fate, fortune, providence, destiny, necessity, risk.’⁴ In this light, prediction is—despite the hyper-rational association—a technologised incorporation of the contingency-fate complex, connecting the corporal to abstract realms and altering the reality process. Perhaps a new being has emerged—‘homo probabilism’—a term coined by Ivan Ascher⁵ to describe the culmination of financial capital but which I see as extending more generally across dimensions and experiences of life.

A longer history of probability and a tentative call for reclaiming indeterminacy

Most histories of probability⁶ begin with games of chance—the literal rolling of bones, coin tosses and the attempts to observe patterns and manage chance, whether to improve odds and/or to develop the analysis of random probability distribution itself. If probabilistic calculation is central to social life now, as I believe it is, then we cannot downplay the significance of games of chance in the cultural and social logic of this conjuncture. Predictive living is indicative of a deep gamification whether we see ourselves as players or not. Maybe stochasticity, itself, was a hack long ago—a playful subversion of scholasticism and final causes, perhaps a form of early-Modern pranking. Wasn’t it strange to posit this speculative condition of foreknowledge with inescapable practical applications that could out-manoeuvre deterministic chains? But it quickly lost its smile and through the centuries became its own dire system. This appears to be a recurring pattern. We could see the hijinks of programmers and hardware engineers of the 60s and 70s as giving a crucial energy to the development of the surveillance society, preemptive policing, conspirituality, new realms of commodification and other Fred Turner⁷ nightmares. A cautionary note is to see hacking as having the tendency to contribute to the very entity it seeks to foil and unwittingly enhance new inaccessible systems. And to momentarily get way ahead of myself, what about the hacks of hacking, what of those projects? The self-negating way forward or a deathly serious play of fire leading into impossibly complex systems with sinister logics? Soon we will reach the end of hacking in the finite limits of life. But these are only figures of capture and betrayal of the true spirit of hacking? All of the above. Hack that.

In many ways this is the story of the ascent of the statistical into higher (or is it lower?) levels of reality—the full diffusion of stats into our lives in synergies between psychometrics, capitalism and digital technologies with the self as ground zero. ‘The system’ here is much larger than digital data and networks of the past few decades, and extends back to the 17^th century if not further. It has subsequently gone through several different phases of which ‘the algorithm’ and AI are but instants or a manifestation. However emergent or ‘digital’ all this might seem, this condition nevertheless arises from well known areas of contention within the social sciences around the representational nature of statistics and their constructive and normative functions. Following Alain Desrosières’⁸ terms, the predictive operates within the fraught relationship between the thing being measured and the measuring process, and how social entities may be, themselves, statistical constructs for the purposes of measure. Values, meaning and experience are reshaped in this quantification process. ‘Numbers create and can be compared with norms, which are among the gentlest and yet most pervasive forms of power in modern democracies.’⁹ At almost every moment when using digital devices we are within the deep web of this ‘gentle’ power. The complexities of these relations are so ubiquitous and far-reaching, it is easy to forget that data is an element of measure, and that a datafied society is a fully and obsessively measured one. Who or what is doing all of this? Why have we entered this vast realm of measured living? Can there or should there be a way out?

Prediction developed by probabilistic statistics seems to imply carefully produced knowledge, within a rigorous scientific method for gauging uncertainty and producing reliable information of future events. However, this is not our probability. There are post-probability pronouncements of moving from prediction to pre-emption, from classical frequentist statistics (probability in terms of the frequency of objective properties sampled) to Bayesian approaches (probability as a measure of believability that a statistician has about the occurrence of an event), and from a kind of knowledge to the production of correlations outside of human comprehension but certainly within the competitive advantage of businesses.¹⁰ In these shifts prediction might appear to be recast as self-interest with only an operational level of truth, and that we are no longer in the science of prediction but in the drama of pre-emptive action. But the goals and function of quantification have never been merely descriptive and are ‘part of a strategy of intervention.’¹¹ Classical statistical norms have always been creating qualities they are meant to dispassionately measure. As well, to think probability in the movement from reason to irrationality, from centralised public planning to ad hoc opportunism of private interests misses the fundamental double-nature of probability as Ian Hacking saw it.

The condition I am exploring is lodged within two theses by Ian Hacking, philosopher of probability and science, and are pivotal in coming to terms with probabilistic reason. These ideas long predate digital technologies, but I argue that they have been augmented in the politics of automated probability. The first is the dual nature of probability—it combines the search for stable frequencies to gauge uncertainty with assertions of belief on the occurrence of future events. ‘Probability…is Janus faced. On the one side it is statistical, concerning itself with the stochastic laws of chance processes. On the other side it is epistemological, dedicated to assessing the reasonable degrees of belief in propositions quite devoid of statistical background.’¹² The second thesis is that the rise of probability from the 19^th century onward (outlined in Hacking’s Taming of Chance) saw a departure from deep deterministic understandings, and an entry into an indeterminate view of the world. However, this did not result in a radical openness and social entropy, but on the contrary, in ceaseless surveying and calculating. In the development of statistical measures of control (in fact, measure as control), indeterminacy becomes increasingly synonymous with managerial quantification. Chance from this time on is taken seriously in social life as it is converted into statistical laws based on data and distribution curves, hence the titular ‘taming of chance’. From this, I am proposing—in a tentative and exploratory manner—a project of reclaiming indeterminacy in the 21^st century and a re-envisioning of predictive data as belief. I am not casting all probabilistic functions as pure belief or arbitrary plays within an anything-goes social entropy. But we cannot lose a feel for the indeterminant, nor can we be blind to all the epistemological investment in what is presented as data-driven likely occurrences.

Imagine we find ourselves wandering around the edges of a vast predictive system. Maybe we abhor it but we are nevertheless of it, either in spite of ourselves or through an all-out embrace. But in any account, from the right vantage point there is a fascination with the transactions and communion. After all these years there is still the imaginary of the cybernetic dream of an X (singularity entity, artificial superintelligence) that knows us beyond how we know ourselves, and not only knows the future but can pass into impending moments, distorting temporal boundaries like the poets sought. But instead of enriching vitality, we can feel it draining away into temporal homogeneity. So it would appear to be no easy hack—how to hack a dream of techno-omniscience and data-driven time machines? They’ve ruined randomness, so it must be reinvented. How to get lucky when contingency has become the exclusive domain of ‘data science’? How does one hack statistical fate in the 21^st century?

Artificial Futures (AI as AF)

I am working with the supposition that whenever we are in a situation modulated by digital technologies—specifically the array of machine learning techniques, algorithmic functions, extensive data processing, automated functions frequently labelled as ‘AI’—we are necessarily within a predictive realm, even if we don’t want to be. Almost all the machine learning techniques and functions associated with Artificial Intelligence are driven one way or another by a predictive logic, underscored by the dubious assumption that prediction and intelligence are somehow synonymous. As such, they are not only data processing and information technologies, but time technologies orientated to modelling future events (within the statistical sense of an ‘event’); given that prediction is a temporal function, these technologies are not only about data and processing but alter the experience and conceptualization of time. This is complicated by the way that such an engagement with the future (as a mathematical abstraction, from a system which is using prediction as a means of  control and to shape outcomes), usually has a de-futuring effect rendering what is to come as merely a projection based on existing patterns of the present and data sets without the alterity and sense of open possibility of The Future; and thus as stated, making predictions without a future.

Another way to say this is that the predictive condition I am exploring is a temporal expression of quantification. By framing data collection and analysis within the larger enterprise of prediction, I am foregrounding the temporality embedded in measure. Prediction is the time of measure, and its expansion into the time of everyday life. To be imbricated into digital networks is to enter this time, with our actions and expressions are variables of capacities and performance. This ubiquitous and quotidian aspect of digital media should be seen as a time technology altering to the future, whether it is cancelled or reassigned.

The opposite of prediction

So what would it mean to ‘hack’ this temporality of predictive living? My view is to not leave it at subsuming probability into the service of the collective (Red AI), or to democratise the modes of prediction, merging stochastic reason with social justice. These are fascinating projects, hopefully already well underway, however, for me a hack means opening these systems (and ourselves as we are enfolded in them) to the improbable. What does unpredictability or the improbably really mean at this point in time? This is what we must open ourselves to, this is part of the hack. We can ask, to begin with, why the obsession with prediction in such unpredictable times? The ostensible answer is that probabilistic relations to the future are an attempt to mitigate uncertainty and achieve a degree of control and governability. But the predictive modes I am addressing here never seem to stabilise and usually add to uncertainty. It’s time to investigate the opposite of predictability, whatever it might be.

As prediction is oriented to likely outcomes of what will happen, then the hack could mean that the great wheel of ‘what’s next’ breaks down—slows or jams shut. In life without systemic anticipation, what are we going to do indeed? How will anything function anymore, will the structures of temporality unravel? In this light, maybe the opposite of prediction lies in moments when time stands still. A bit of spiritual hacking would do us all some good. For such temporal hacking, the experience of randomness is one way to start.

Aleatory visions (Petit Tube)

If the prediction system began in games of chance, then we should return to its origins of play and indeterminacy. In a data environment where everything is incessantly trying to predict what we want, randomness can be political. So we need to relearn how to inhabit chance and develop aleatory sensibilities. From deep within the prediction economy (political and libidinal), ‘chance must be prepared.’¹³ If John Cage prepared pianos, then we need to prepare platforms. ‘The suggestion here is that these discussions offer the development of a sensual and political understanding of chance that establishes it as the grounding condition for modes of being and one that is perversely synthetic in its ethico-aesthetics.’¹⁴ In a time attempting to irradicate improbable actions through measure and probabilistic calculations, we can no longer take chance for granted. We must cultivate ourselves to chance because no one is born lucky in a universe proceeding under the sway of probability.

Hacking algorithmic recommendation systems can be one way of doing this, and the web project Petit Tube (petittube.com) is an example. It randomly filters YouTube videos that have very few views and presents itself as offering ‘the least interesting videos on YouTube.’ Click play and the aleatory and enigmatic viewing of content begins. In an algorithm vs algorithm scenario, it selects videos which have been ignored, forgotten or otherwise outside YouTube’s predictions. The experience for me is surprisingly freeing, generating both a receptive energy toward the multiplicity of images, and a space from which to reflect and rewire our experience of predictive systems. Launched in 2011, the project was developed by Yann van der Cruyssen, a polymath based in France, who works on the borders of art, video game creation, music and wallpaper invention.

What is it like? It’s both ordinary and subtly weird, maybe an infraordinary of media. More of a simple side-step than anything psychedelic or mind-bendingly entropic. On a typical viewing one might see a used car advertisement set in a North American dealership with dirty late-winter snow, an office party in a generic business environment, fingers slowly rotating an opal stone, a presentation from the Antelope High School career counselling team, a clip from a video game reposted from Twitch, a Birthday party in a South-East Asian home. So this is the great unwatched. In fact, only a small minority of videos on YouTube are actually watched in large numbers, and most of these are music videos (especially songs for children), followed by how-to videos and product demos, and then more distantly by videos from popular content creators. What about the rest? I have been exploring Petit Tube for several years, and there appears to be what I characterise as an ‘outlook’. After months of revisiting the site, it can feel like beachcombing—walking the same stretch of coast but each time finding something perplexing has washed in. It’s like some kind of media therapy or deprogramming process to work free from ultra-curated content, personalised algorithms, known pleasures, and the worn grooves of one’s own tastes reinforced by a predictive media system. It sometimes feels calming, even though the content is a haphazard assortment typical of so much of contemporary media culture, not particularly meditative onto itself. So we have not quite reached the end of the media universe—rather a cul de sac that at times seems like a worm-hole.

The overall feel is game-like, in a simple and slightly clunky interface that is both underwhelming, and in a way, mysterious—something of a Bermuda Triangle of online content. The videos have disappeared in the sense that they have almost never been viewed, but through this random selection, they still can be seen on the threshold of a media void. Perhaps even the person who uploaded the content is no longer interested or even aware of their own media. It is a site which, momentarily, makes lost content reappear. We watch them, and then we watch them disappear all over again. We watch the blinding insignificance.The larger context of this is, on the one hand, a media ecology seeking to eliminate chance through predictive systems that already know who we are and what we want to experience; and on the other hand, a larger psychosocial environment that is increasingly animated by uncertainty. This hack offers a media universe wherein video waste functions like a filtering process rather than degradation. Disinterest and abandonment function as a prism through which something presumed as trivial might reach a kind of plenitude in spite of its staggering banality. In these refractions, Petit Tube works as a distrusting agent of YouTube. It is only by way of an improbable filtering of content that no one is interested in that we might finally drop out of the supposed affirmative preemption of contemporary image culture. What we lose in expectation of the visual we might gain in hope. If this kind of randomness forms a mysterious triangle, then it is formed in the interdependence between what exceeds our grasp, the mundane and lucidity.

BIO

Peter Conlin is a writer and researcher based in Birmingham (UK) and works as a Teaching Associate at the Department of Cultural, Media and Visual Studies, University of Nottingham. He is the author of Temporal Politics and Banal Culture: Before the Future (Routledge).

REFERENCES

1. Ivan Ascher, Portfolio Society: On the Capitalist Mode of Prediction (New York: Zone/Near Futures, 2016). ︎
2. Ascher, Portfolio Society, 26. ︎
3. Andreas Jungherr, Gonzalo Rivero and Daniel Gayo-Avello, Retooling Politics: How Digital Media Are Shaping Democracy (Cambridge: Cambridge University Press, 2020), 165. ︎
4. Matthew Fuller and Olga Goriunova, Bleak Joys: Aesthetics of Ecology and Impossibility. (Minneapolis: University of Minnesota Press, 2019), 78. ︎
5. Ascher, Portfolio Society, 85. ︎
6. F. N. David, Games, Gods and Gambling (London: Charles Griffin, 1962); Rüdiger Campe, The Game of Probability: Literature and Calculation from Pascal and Kleist (Redwood City: Stanford University Press, 2012); Brian Everitt, Chance Rules (New York: Springer Science & Business Media, 2008); John Haigh, Probability: A very short introduction (Oxford: Oxford University Press, 2012). ︎
7. Fred Turner, From Counterculture to Cyberculture (London: University of Chicago Press, 2006). ︎
8. Alain Desrosières, The Politics of Large Numbers (Cambridge, Mass.: Harvard University Press, 1998). ︎
9. Theodore Porter, Trust in Numbers (Princeton, N.J.: Princeton University Press, 1995), 45. ︎
10. Mark Andrejevic, Infoglut (London: Taylor & Francis Group, 2013), 96-110. ︎
11. Porter, Trust in Numbers, 42. ︎
12. Ian Hacking, The Emergence of Probability (Cambridge: Cambridge University Press, 2006), 12. ︎
13. Fuller and Goriunova, Bleak Joys, 83. ︎
14. Fuller and Goriunova, Bleak Joys, 83. ︎

back to the ball

By: erynn young

@lgosp3@k: C0mmun!c@t!0n H@ck!ng on T1kT0k

Introduction

On TikTok, a platform notorious for algorithmic content moderation¹, there is an emerging phenomenon of strategic communication hacking, called algospeak for algorithm speak. Algospeak provides users with a seemingly limitless yield of communication hacks in response to content surveillance and policing. Algospeak is situated in a specific digital, interactive context (TikTok), where appropriate content is defined through mobilisations of politics of politeness (e.g. no hate speech), neoliberal sociopolitical ideals (e.g. no racist ideologies), and existing sociocultural hierarchies of marginalisation (e.g. moderation of sexual content).² Through the enforcement of its community guidelines, TikTok establishes a bounded system that targets inappropriate users/communication for censorship via algorithmic content moderation. Algospeak communication hacking enables TikTok users to subvert these interactive constraints; it disrupts this system.

Guidelines = Discourse = System

TikTok is a multimodal social media platform, providing users with audio, visual, and written communication channels, expanding opportunities for interaction and content generation. All platform interactions are moderated, predominantly through automated/algorithmic content moderation systems; TikTok remains opaque regarding specific operational details.³ Users are instead provided with a collection of community guidelines that circumscribe TikTok’s dos and don’ts (mostly don’ts): this is TikTok’s proclaimed attempt to foster an inclusive, welcoming, and safe social space. Do: treat fellow users respectfully. Don’t: incite violence. Don’t: depict sexually explicit activities. Do: express yourself. Don’t: discriminate based on religion, race, gender identity, etc. Don’t: harass others.

Users must comply with these guidelines or risk having their content removed and their accounts banned. The algorithmic content moderation systems that enable TikTok to constantly surveil its users contribute to users’ maintenance of the pervasive fear of censorship (e.g. studies investigating behaviour and communication chilling/silencing effects of surveillance).⁴ Despite the consequences of content violations, TikTok’s community guidelines are not explicit in how inappropriate content is made manifest in users’ communication practices. Many guidelines indicate that TikTok does not allow X, Y, or Z, but do not elaborate on which words are violative. Users must assess their own communicative content within TikTok’s systemic context and its definitions of inappropriate content, correcting when necessary, to avoid algorithmic scrutiny. Algorithmic content moderation thus coerces users into anticipatorily self-censoring.

TikTok defines its ‘safe’ space through a bricolage of sociocultural, political, and legal values, ideologies, and conventions. It establishes the rather vague boundaries of this system (or discourse, in the Foucauldian sense)⁵ through content guidelines and (re)definitions of what it means to be appropriate and eligible for TikTok audiences predominantly through language censorship.⁶ Survival within TikTok’s system translates to platform access (i.e. non-banned accounts) and visibility (i.e. content distribution/circulation). This survival is predicated on a confluence of factors. First, users must successfully internalise TikTok’s community guidelines; they must translate what these guidelines mean in terms of restricting particular language use and put these understandings into practice. Users must also develop (and reference) perceptions of how extensive they believe TikTok’s algorithmic surveillance capabilities to be – termed users’ algorithmic imaginaries.⁷ These imaginaries drive varying defensive digital practices, including severe self-censorship or speech chilling⁸ if algorithmic surveillance is perceived/believed to be comprehensive. Users mobilise these perceptions/beliefs through subsequent self-moderation⁹ and communication hacking. Algospeak, a medley of (written, video, audio) communication manipulation strategies, is one such defensive digital practice, protecting users within TikTok’s surveilled system by providing them with creative, innovative, and crucially, adaptive means of (communication) resistance.

Hacking Strategies

Developing research into algospeak on TikTok has illuminated numerous possibilities for communication subterfuge that algospeak users exploit in challenging TikTok surveillance, culminating in a rich toolkit of communication hacking strategies.¹⁰ Algospeak-as-hacking is a perpetually ongoing process of system disruption by which users take back some expressive agency. The informing research underlines algospeak strategies themselves as tools for successful and sustainable communication hacking rather than specific algospeak forms that might be considered as constitutive of a coded language. This is because the boundaries of TikTok’s system can and do shift – for example, their content moderation capabilities adapt – and users are better prepared to respond to the system’s fluidity with equally fluid hacking strategies rather than lasting (and therefore more moderate-able) words/forms. The algospeak strategies¹¹ below demonstrate a broad range of users’ approaches to communication hacking.

Censoring obscures content through strategic content substitution; text, sound, or image censors disrupt content filters that are triggered by linguistically meaningful words and recognisable imagery.

Users can also manipulate letters/characters within words or phrases. Visual coding (also l33tspeak¹², hackers’ language,¹³ rebus writing¹⁴) and typos disguise word forms while maintaining intelligibility through users’ abilities to treat non-alphabetic characters as if they were alphabetic. Users can convey and understand meaning through these hacked spellings by making meaning where it does not exist; automated word-detecting filters cannot.

Visual cues exploit recognisable cultural themes/artefacts for audience comprehension but can also encode meaning through the intertextuality or memetic culture of digital communication.¹⁵

Emojis exploit the fuzzy boundaries between visual and written channels. Emoji use can be representative (or literal); it can also integrate implied phonetic (linguistic) material, as in the case of the emoji homophone for ‘fuck.’ Users’ digital literacies regarding evolving emoji meanings also play a substantial role in subversive emoji communication.

Users use visual gestures to convey sensitive content without triggering text or sound filters.

Language gestures and performances skirt the boundaries between gesture/performance and speech by strategically removing or manipulating parts of conventional communication; this includes withholding audio content (e.g. mouthing), separating a word form (e.g. spelling), and using associative sounds to indicate particular words (e.g. onomatopoeia). 

Users sometimes change the phonetic makeup of their communication while maintaining intelligibility, mostly by relying on similar sounds to keep the intended meanings retrievable for audiences.

sects, a phonetic substitution of ‘sex,’ substitutes -X with -CTS, which achieves a similar pronunciation. Adding consonants -HM before the vowel – a variation of shm-reduplication from Yiddish¹⁶ – results in a minimal difference: /seks/ becomes /ʃmeks/. Heaux’s,¹⁷ a homophone, is identical in pronunciation to its target word ‘hoes’: /hoʊz/ remains /hoʊz/. This approach to communication hacking can be extended to unexpected word endings, which substitutes new word endings to potentially violative words and relies on context to convey the intended meaning.

Multilingual/-dialectal creativity reveals how TikTok’s system boundaries are conceptualised by users – what is and is not considered at risk of moderation – and how these boundaries are transgressed by users in their English interactions.

Users highlight how inappropriate content is socially/culturally/etc. specific. In other words, a word’s inappropriateness shifts depending on the language variety in which it is communicated and local social/cultural customs, norms, and conventions. Users can manipulate either one of these aspects by integrating multiple language varieties. Loanwords for (English) algospeak functions like typical loanword use; a word is borrowed from another language variety and integrated into English language content. The loanword’s non-Englishness becomes subversive, helping users evade filters meant to track locally (i.e. English) violative words. Users borrow sounds (phonetics) from other language varieties, using multilingual homophones like phoque (‘seal’ in French) instead of ‘fuck.’ Phonetic similarities facilitate audience comprehension, and borrowed phonetics exploit the lack of situated (English) inappropriateness; phoque is not violative in French.

Algospeak word creation has garnered considerable attention on and beyond TikTok,¹⁸ owing in part to the novelty of such neologisms as well as their seeming emblematic of algospeak as a coded language.

This strategy follows existing rules for word creation; for example, unalive is created by adding a negating prefix (e.g. UN-) to an adjective (e.g. ALIVE) to convey meanings regarding death or killing. The resulting word is unconventional but shares semantic qualities (i.e. literal meaning) with the word(s) it replaces. Unalive’s accordance with word creation conventions and its use of a binary opposition to convey a familiar concept facilitates audience comprehension while its unconventionality disrupts the system’s expectations.

Removing part(s) of users’ communication – from vowels to entire words – is also an effective form of algospeak.

These strategies rely on context for communicative success; audiences must fill in the blanks with contextual knowledge they gather throughout the interaction. Users’ heightened sensitivities to communication and content on TikTok – driven by the centrality of TikTok’s content guidelines and moderation – contribute to the success of such partial language as hacked communication by encouraging users to anticipate implicit messaging. 

Algospeak euphemisms and dysphemisms are alternative words/expressions for at-risk communication that also reduces or increases negative connotations, respectively. One subset of euphemisms frequently used for hacking is the innuendo – owing to TikTok’s conspicuous moderation of sexual content.¹⁹ 

Conventional euphemisms, innuendos, and dysphemisms rely on broad, cultural intelligibility while non-conventional alternatives require audiences to access context and cues to key into the intended meaning.

Algospeak metaphors involve mapping potentially inappropriate concepts onto new words/expressions that share some common threads of meaning or symbolism so audiences can figure out what is being communicated.

Metaphors, as well as allusions, retain one or more salient characteristic(s) of the original word or concept. In envelopes/envelopians for ‘White people,’ the salient characteristic is whiteness. This enables audiences to make inferences based on conceptual continuity between the intended meaning and the algospeak form. The eggplant emoji , used metaphorically on and off TikTok for ‘penis,’ illustrates the role of conceptual continuity in comprehending hacked communication. This continuity can rely on cultural artefacts (e.g. memes²⁰) to ensure audience comprehension, like the meme-ification of algospeak itself (e.g. le dollar bean’s alluding to le$bian/le$bean²¹).

Users’ contextualisation attempts show direct, dialogic engagement with TikTok at the system level. Users attempt to negotiate with (i.e. appeals, real contextualisation) or challenge (i.e. fake contextualisation) TikTok’s content evaluation and moderation practices. These strategies function in tandem with the potentially violative content, which remains uncensored.

These strategies represent a continuum of users’ willingness to be more or less subversive. On one end of the continuum (i.e. appeals, authentic disclaimers), users directly confront the potential inappropriateness of their content. On the other end (i.e. inauthentic disclaimers), users are deceptive about the nature of their content, following the internal logic that content that is not real cannot be a real violation.²² 

Discussion & Conclusion

Algospeak is a process of communication hacking that has developed in the face of TikTok’s algorithmic content moderation systems, which threaten users’ platform access and visibility, as well as their communicative agency. The manipulative strategies that comprise the algospeak toolkit for subversive communication enable users to hack their lines of code to achieve their goals: literally altering segments of (language) code to embed, disguise, imply, hint, and covertly convey their intended meanings. These strategies reach across communicative channels (i.e. written, audio, visual), media types (i.e. cultural allusions), geographies and languages varieties, cultures, and time (i.e. 90’s l33tspeak) to helps users keep their communication safe from moderation, remain selectively comprehensible among intended audiences, and embrace creativity and adaptability. Algospeak strategies provide users with rich and diverse ways to hack their communication and manage and restrict comprehensibility, allowing them to survive within TikTok’s algorithmic surveillance state and remain visible to fellow users. These seemingly paradoxical functions of algospeak – self-censorship or self-moderation to expand content reach – mirror the double-edged nature of TikTok’s algorithmic capabilities. TikTok’s algorithms moderate and remove undesirable content; they also promote desirable content to broader audiences, expanding content visibility (e.g. viral videos). With communication hacking, users can simultaneously keep their content safe from algorithmic scrutiny and eligible for mass, algorithmic distribution. Algospeak is simultaneously communication hacking to avoid algorithms and to benefit from algorithms. Thus, algospeak – or algorithm speak – is speaking for and against algorithms.

It is crucial, however, to reckon with the implications of algospeak as a communicative phenomenon, as well as algospeak research and heightened visibility more broadly. First, contending with algospeak in the digital hands of differentially motivated users is necessary. While it is a resourceful collection of language hacks that help protect users from having their communication unfairly moderated,²³ it is also accessible for strategic uses in covertly conveying harmful, discriminatory, violent communication. For example, it is possible to protect hate speech or threatening language – which are both unequivocally in violation of TikTok’s community guidelines – from content moderation through algospeak communication hacking (e.g. racist dog whistles as a kind of metaphor/allusion). Though no instances of such algospeak application were encountered during this research process, hacking is (inevitably) a tool accessible to a diverse range of users.

Additionally, it is also necessary to reconcile the risks associated with maintaining algospeak discourses, among its users online and in other domains including media journalism and academic research. Heightened algospeak visibility can backfire and unintentionally push content moderation systems like those operating on TikTok to adapt, improving moderation capabilities by integrating more complex forms and styles of communication. It is not implausible to anticipate that subversive, algospeak communication is someday soon explicitly targeted for moderation, at which point algospeak use by platform users will invite rather than avoid surveillance and (algorithmic) scrutiny. It is difficult to predict exactly how such evolutions to TikTok’s content moderation practices would come about, if at all. But perhaps some solace can be taken in the historical successes of subversive communication observed: for example, Lubunca, a repertoire of secret slang (an argot) for various queer and sex worker communities in Turkey.²⁴

TikTok surveils, moderates, and controls content and user behaviour through language. And it is through language that users disturb systemic expectations and norms. Users reassert their control, negotiating their communicative and expressive agency by pushing boundaries, manipulating language, and subverting community guidelines. They resist in the face of TikTok’s threats of censorship and deplatforming/banning. They exploit the weaknesses in TikTok’s algorithmic armour by playing across communicative channels: their human and creative communication skills outsmart and outpace current content moderation systems.²⁵ They layer manipulations, blend strategies, juxtapose and converge communication channels: innovate, innovate, innovate. Users are resilient against the imposition of content moderation through algospeak, creating new possibilities for communication, expression, connection, and ensuring digital survival. However, as algorithmic content moderation practices adapt in response – for instance, integrating popular or widely used algospeak communication into content filters – users will need to continue to generate and deploy innovative, increasingly complex and/or creative strategies for communication hacking to stay one step ahead.

Acknowledgements: The author declares no conflict of interest.

BIO

I was born and raised in the United States of America, where I completed the majority of my undergraduate studies (in French language and linguistics). I have recently completed a research master’s in Linguistics and Communication at the University of Amsterdam. I am currently located in and conducting independent research out of Amsterdam, Netherlands. Previous and ongoing research interests include critical (technocultural) discourse analysis, argumentation analysis, communicative and interactive phenomena on digital platforms, deliberate linguistic manipulation as user agency negotiation/resistance, and communication practices language users engage in to represent/perform/negotiate their positionalities/identities.

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1. Danielle Blunt, Ariel Wolf, Emily Coombes, & Shanelle Mullin, “Posting Into the Void: Studying the impact of shadowbanning on sex workers and activists,” Hacking//Hustling, 2020; Faithe J. Day, “Are Censorship Algorithms Changing TikTok’s Culture?,” OneZero, December 11, 2021; Taylor Lorenz, “Internet ‘algospeak’ is changing our language in real time, from ‘nip nops’ to ‘le dollar bean’,” The Washington Post, April 8, 2022; Kait Sanchez, “TikTok says the repeat removal of the intersex hashtag was a mistake,” The Verge, June 4, 2021. ︎
2. Blunt, Wolf, Coombes, & Mullin,  2020. ︎
3. “Community Guidelines Enforcement Report,” TikTok. https://www.tiktok.com/transparency/nl-nl/community-guidelines-enforcement-2022-4/ (accessed March 2023); “Community Guidelines,” TikTok. https://www.tiktok.com/community-guidelines/en/ (accessed March 2023). ︎
4. Elvin Ong, “Online Repression and Self-Censorship: Evidence from Southeast Asia,” Government and Opposition, 56 (2021): 141-162; Jonathon W. Penney, “Internet surveillance, regulation, and chilling effects online: a comparative case study,” Internet Policy Review, 6 (2017). ︎
5. Michel Foucault, The Archaeology of Knowledge and the Discourse on Language (New York: Pantheon Books, 1972). ︎
6. Tarleton Gillespie, “Do Not Recommend? Reduction as a Form of Content Moderation,” Social Media + Society, 8 (2022). ︎
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8. Ong, 2021; Penney, 2017. ︎
9. Nadia Karizat, Dan Delmonaco, Motahhare Eslami & Nazanin Andalibli, “Algorithmic Folk Theories and Identity: How TikTok Users Co-Produce Knowledge of Identity and Engage in Algorithmic Resistance,” Proceedings of the ACM on Human-Computer Interaction, 5 (2021): 1-44; Daniel Klug, Ella Steen, & Kathryn Yurechko, “How Algorithm Awareness Impacts Algospeak Use on TikTok,” WWW ’23: The ACM Web Conference 2023 (2023). ︎
10.  Kendra Calhoun & Alexia Fawcett, “’They Edited Out Her Nip Nops’: Linguistic Innovation as Textual Censorship Avoidance on TikTok,” Language@Internet, 21 (2023); Klug, Steen, & Yurechko, 2023; young, 2023. ︎
11. In this article, algospeak hacked communication is in italics, its glosses (non-hacked forms) are in ‘single quotes,’ and gestures/performances are underlined. ︎
12. Blake Sherblom-Woodward, “Hackers, Gamers and Lamers: The Use of l33t in the Computer Sub-Culture” (Master’s thesis), (University of Swarthmore, 2002). ︎
13. Brenda Danet, Cyberpl@y: Communicating Online (Routledge, 2001). ︎
14. David Crystal, Internet Linguistics (Routledge, 2011); Ana Deumert, Sociolinguistics and Mobile Communication (Edinburgh University Press: 2014). ︎
15. Calhoun & Fawcett, 2023. ︎
16. Andrew Nevins & Bert Vaux, “Metalinguistic, shmetalinguistic: the phonology of shm reduplication,” Proceedings of CLS 39, 2003 (2003). ︎
17. This form also uses multilingual borrowed phonetics, borrowing eau(x) and its pronunciation from French. ︎
18. Ellie Botoman, “UNALIVING THE ALGORITHM.” Cursor, 2022; Alexandra S. Levine, “From Camping To Cheese Pizza, ‘Algospeak’ Is Taking Over Social Media,” Forbes, September 19, 2022. ︎
19. Blunt, Wolf, Coombes, & Mullin, 2020; Mikayla E. Knight, “#SEGGSED: Sex, Safety, and Censorship on TikTok” (Master’s thesis), (San Diego State University, 2022). ︎
20. Calhoun & Fawcett, 2023. ︎
21. Ibid. ︎
22. Charissa Cheong, “The phrase ‘fake body’ is spreading on TikTok as users think it tricks the app into allowing semi-nude videos,” Insider, February, 8, 2022. ︎
23. Blunt, Wolf, Coombes, & Mullin, 2020; Calhoun & Fawcett, 2023. ︎
24. Nicholas Kontovas, “Lubunca: The Historical Development of Istanbul’s Queer Slang and a Social-Functional Approach to Diachronic Processes in Language” (Master’s thesis), (Indiana University, 2012). ︎
25. Day, 2021; Robert Gorwa, Reuben Binns, & Christian Katzenbach, “Algorithmic content moderation: Technical and political governance,“ Big Data & Society, 7 (2020). ︎

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