Digital rhythmanalysis: Studying memetic and affective rhythms on the post-viral Web

Abstract

How to do justice to the pulsing, ephemeral and multisensory dimensions of today's digital lifeworlds? Even as platforms increasingly push audio-visual streams through algorithmic feeds optimized for repetitive scrolling and swiping, existing research paradigms still tend to approach the Web spatially and topologically as a network made of static nodes. Relatedly, memetic or viral approaches require critical revision in an increasingly artificial intelligence-driven and ‘post-viral’ Web where users are algorithmically segregated into niche communities and fuzzy ‘vibes’. In response to these structural transformations, we propose ‘digital rhythmanalysis’ as an alternative approach to study the cultural-material temporalities of platforms. We draw from Henri Lefebvre to propose a quali-quantitative methodology that ‘listens’ to platforms and the affective formulas that resonate through them. Digital rhythmanalysis emphasizes time before space and can be attuned to the study moments of (viral) intensity as well as the rhythms of everyday digital life. We bring our approach in dialogue with recent affective and aural turns in media and cultural studies and explore new methods to help us with ‘algorhythmic’ listening and transcribing the rhythms of memes and affects.

Keywords

Rhythm rhythmanalysis memes vibes affect digital methods

Introduction

Free from the human cycles of sleeping and waking, digital platforms have become vast global infrastructures that constantly listen to the everyday murmurs, moods and habits of users (Carmi, 2020; Yuill and Skeggs, 2020; Zuboff, 2019). Ever more intricate deep learning systems allow platforms like TikTok to constantly recalibrate their atmospheric media environments (Kemper and Jankowski, 2024). Streaming services like Spotify have become veritable ‘mood machines’, feeding back aggregate listening habits through fine-tuned playlists (Pelly, 2025). Platform infrastructures are hyper-optimized to sense minute behavioural changes, respond to them at the right time (Bucher, 2020), or anticipate them through predictive analytics. Beyond pushing content through lists and popularity metrics, they now also vectorize audio-visual data in unintelligibly complex manners to construct never-ending streams of similar yet different content optimized for repetitive scrolling and swiping (Gerbaudo, 2024; Rieder, 2020; Zulli & Zulli, 2020).

How do Internet researchers respond to these changing platform logics? How do they update their concepts and methods to align with this shift? In this paper, we engage with these questions by proposing a methodological outlook that is attuned to the rhythmic dimensions of digital platforms. We turn to the influential French-Marxist philosopher Henri Lefebvre, who in the 1990s envisioned rhythmanalysis as a ‘new field of knowledge [for] the analysis of rhythms; with practical consequences’ (1992/2004: 3), a methodology urging researchers to ‘listen’ to everyday murmurs through a range of sensory surfaces, whether it be their own body or scientific tools. We argue that this temporally sensitive and interdisciplinary outlook serves as a fruitful basis for the study of cultural-material rhythms online. We dub this new methodology ‘digital rhythmanalysis’.

Digital rhythmanalysis, we contend, grows more pertinent as platforms increasingly prioritize temporality, whether by relying on audio-visual streams, ephemeral ‘stories’, or fluctuating ‘for you pages’, while moving away from permanent profile posts, textual documents, and chronological feeds. Online actors are less and less ‘networked’ through friends, followers, or hashtags and increasingly brought together by opaque and volatile similarities generated through complex deep learning infrastructures. Social networks and interest-based forums are making way for loose and distributed ‘clustered publics’ (Gerbaudo, 2024) or ‘imitation publics’ (Zulli & Zulli, 2020) that hinge on fluctuating and evanescent predictions. As such, these techno-cultural changes challenge several academic paradigms. They notably cause friction with the idea of the ‘network’, which has been entrenched in Internet research with topological ideas, network analyses and other digital methods (Moats and Borra, 2018; Rogers, 2013). Likewise, viral understandings of ‘spreadable media’ (Jenkins et al., 2013) aptly describe how content could engulf centralized feeds and ‘trending’ content only a decade ago, but they are arguably less suited to study today's more saturated online ecosystems. Hyper-personalized feeds have caused a shift away from systems where the majority of attention is concentrated on the same ‘viral’ contents and towards a long tail of clusters that are still enormous but also innumerable, scattered among a ‘dark forest’ of ephemeral groupings (Strickler, 2019). This oversaturated yet algorithmically insulated state has been called ‘post-viral’ (Bogost, 2022; Broderick, 2023; Warzel, 2023)¹ and raises the question of whether the notions of ‘virality’ and the ‘meme’ still sufficiently capture the diffuse imitative trends of platforms like TikTok.

As a corrective to these paradigms, we situate digital rhythmanalysis within theories that have recently emerged as part of related ‘affective’ and ‘aural’ turns in media studies. These turns have come along with temporal, acoustic and atmospheric concepts like ‘flow’ (Castells, 2004; Helmond, 2015), ‘resonance’ (Paasonen, 2020), ‘reverberation’ (Cho, 2015; Kuntsman, 2012), ‘mood’ (Pelly, 2025), ‘vibes’ (James, 2024) and ‘ambiance’ (Roquet, 2016, 2021). Taking inspiration from this, we formulate digital rhythmanalysis as a temporally sensitive approach that is able to capture ‘algorhythmic’ processes (Miyazaki, 2012, 2013) and listen to the ‘affective resonances’ of cultural objects on the Web (Garcia, 2020). We find the specific benefit of rhythm as a lens onto digital culture in how it covers those temporal, embodied, affective and aesthetic flows that make up modern digital life. It also pushes researchers to think not just about moments of viral intensity or controversy but also about the more mundane rituals of everyday digital processes. Digital rhythmanalysis as such aims to be empirically attuned to cultural-material changes within platforms by drawing from the theoretical implications of rhythm (cyclicality, alinearity, recursivity and other types of repetition-with-variation).

In the first section, we discuss rhythm and Lefebvre's rhythmanalysis to review how these concepts have been applied across the social sciences. In the second section, we connect rhythmanalysis to broader affective and aural turns in media studies. We then synthesize these observations as digital rhythmanalysis in the last section. Here we showcase concrete examples and prototypes of what digital rhythmanalyses may look like, exploring new opportunities but also new challenges; for instance, those dealing with the complexities of ‘listening’ to affective and memetic rhythms.

Rhythm and rhythmanalysis

Even without rhythmanalysis, the concept of ‘rhythm’ already offers a fruitful alternative lens onto cultural-material movements online. Rhythm derives from the Greek rhythmos (ρυθμός), which in turn was often associated with flow (ῥέω; Benveniste, 1971: 281). Later, through Socrates, rhythmos was linked to regular intervals and activity that can be broken up into a cadence (Benveniste, 1971: 286–287). From here, the connotation of ‘rhythm’ as a repetitive interval emerged, leading it to be conflated with static and measurable meter. Benveniste discovered that this understanding tended to overstress repetition, effacing earlier Greek notions of rhythmos as a dynamic and fluctuating ‘form’, as ‘“configurations” without fixity or natural necessity and arising from an arrangement which is always subject to change’ (1971: 286). As Benveniste writes, rhythmos was originally related to

the form in the instant that it is assumed by what is moving, mobile and fluid, the form of that which does not have organic consistency; it fits the pattern of a fluid element, of a letter arbitrarily shaped, of a robe which one arranges at one's will, of a particular state of character or mood. It is the form as improvised, momentary, changeable. (Benveniste, 1971: 285–6)

In the 19th and 20th centuries, rhythm made its appearance in the works of Durkheim, Nietzsche, Derrida, Heidegger and many other philosophers (Brighenti and Kärrholm, 2018). In A Thousand Plateaus, Deleuze and Guattari theorize rhythm as a process of becoming, stressing variation over repetition: ‘it is the difference that is rhythmic, not the repetition, which nevertheless produces it’ (1987: 364). Discussing rhythm in Heidegger, Yuk Hui similarly contends that rhythm is not a static form, but rather a process of informing, as rhythm ‘hid[es] and express[es] itself in the guise of forms’ (2017: 68). It is this position in between ‘regularity and unpunctuated flow; symmetry and motion’ (Ikoniadou, 2014: 148) that makes rhythm so analytically useful to us. Rather than evoking ideas on ‘structures’ and rigid organizations, it offers a ‘method of inquiry’ (Ikoniadou, 2014: 149) to investigate ‘meaningful and recognizable combination[s] of ups and downs, motion and rest, tension and release’ (Lynch, 2020: 276; see also Michon, 2020). While a term like ‘flow’ also partially captures this, it tends to suggest uninterruptedness and fluidity. Rhythm, instead, both encapsulates the slow entrenchment of habits and procedures as well as change and ecstatic moments of eruption and release.

This analytical use of rhythm also motivated Lefebvre's Rhythmanalysis: Space, Time, and Everyday Life (posthumously published in 1992, translated to English in 2004).² In it, he recognizes how rhythm encapsulates both stability and instability, both repetition and difference. While there can be ‘no rhythm without repetition’, Lefebvre notes, ‘there is always something new and unforeseen that introduces itself into the repetitive’ (1992/2004: 6). The rhythmanalysist is consequently tasked with identifying these heterogeneous movements through an exercise of radical listening to different rhythms – from our bodily cycles to cosmic movements – and by detecting whether they are in concordance (‘polyrhythm’), in sync (‘eurhythmics’) or at odds (‘arrhythmia’). In one interesting passage, he asks: ‘Will the (future) rhythmanalyst have to professionalise himself? Will he have to set up and direct a lab where one compares documents: graphs, frequencies and various curves?’ (Lefebvre, 1992/2004: 22). To Lefebvre, the question is rhetorical. The rhythmanalyst has to ‘educate himself (to break himself in or accept training), to work very hard therefore, to modify his perception and conception of the world, of time and of the environment’ (Lefebvre, 1992/2004: 22). They have to turn their own body into a sensing surface while also tuning it and plugging it into the abstract apparatuses of the social and even natural sciences:

Just as he borrows and receives from his whole body and all his senses, so he receives data [données] from all the sciences: psychology, sociology, ethnology, biology; and even physics and mathematics. He must recognise representations by their curves, phases, periods and recurrences. […] he pursues an interdisciplinary approach. Without omitting the spatial and places, of course, he makes himself more sensitive to times than to spaces. He will come to ‘listen’ to a house, a street, a town, as an audience listens to a symphony. (Lefebvre, 1992/2004: 22)

As becomes clear in this quote, Lefebvre did not call for a departure from the spatial. In his better-known work The Production of Space (1974/1999/1999), Lefebvre theorizes how space is always socially produced and as such subject to historical and material power relations. In Rhythmanalysis, he connects this to an equally materialist understanding of time, theorizing how ‘everywhere where there is interaction between a place, a time and an expenditure of energy, there is rhythm’ (1992/2004: 15). Rhythm, in other words, was Lefebvre's way of linking space to time and in seeing both as connected to historical power relations and forces of production.

Despite his interdisciplinary ambition striking a chord with us, Lefebvre's rhythmanalysis does not offer a fleshed-out methodology and should be seen more as an ‘orientation’ or ‘investigative disposition’ (Lyon, 2019: 4). His rhythmanalytical demonstrations consist of brief phenomenological accounts about wandering through Paris and gazing through the apartment window. In the chapter The Media Day, he does reflect on how information technologies compose an ‘uninterrupted flow of words’ (1992/2004: 46), but the analysis does not go beyond an observation on how mass media reify and commodify everyday rhythms. Perhaps because of this lack of practical application, Lefebvre's ideas on rhythm were described as having found ‘little purchase since their publication’ (Elden, 2004: xiii).

At the end of the 2010s, however, rhythmanalysis saw a revival motivated by a growing wish for ‘nuanced understanding[s] of the articulations of tempo, movement, flow, stasis and repetition’ in socio-cultural life (Lyon, 2019: 3–4; see also Brighenti and Kärrholm, 2018; Henriques et al., 2014).³ Dawn Lyon's What Is Rhythmanalysis (2019) notably covers the question of how to operationalize Lefebvre's framework, providing welcome handles on how to study and convey everyday rhythms. Other efforts to give empirical substance to Lefebvre's ideas have resulted in rhythmanalyses of financial markets (Borch et al., 2015), ‘sonic events’ (Ikoniadou, 2014), migrant farmworkers (Reid-Musson, 2018) and even bus tours in Ireland (Edensor and Holloway, 2008).⁴ These efforts are foremost related to urban studies, but analyses of rhythms have also found their way into scholarship on new media as part of a larger ‘temporal turn’,⁵ dedicated to the question of how time and temporality are mediated by digital technologies (see, e.g. Coleman and Paasonen, 2020; Kaun and Stiernstedt, 2014; Lohmeier et al., 2020; Velkova and Plantin, 2023). This writing often comments on the quantification and commodification of time through datafication and the ubiquitous daily presence of devices and interfaces (e.g. Wajcman, 2014), aligning with how Lefebvre observed a disciplinary ‘training’ (dressage) in the rhythms of everyday life.

Yet rhythm here is often mentioned in passing, as a specific type of temporality to be taken for granted. Only a smaller subset of new media literature deploys the concept as a genuine analytical tool. Miyazaki, for instance, outlines how ‘listening’ to technological rhythms has long been present in the study of computational systems, with ‘passive listening to the computer-operations [being] very common [as] an active exploration of the machine, listening to its rhythms’ (2012). He proposes algorhythmics as a materialist method to study the rhythms of computation, where the notion of rhythm serves as ‘as an inter-modal tool and model for analysing cultural objects and their hidden relations with current techno and media-cultural situations’ (2013: 135; see also Henriques, 2020). In more contemporary applications, Yuill and Skeggs (2015, 2020) build on Lefebvre to interrogate how Facebook extracts value from the rhythms of user interactions. Similarly, Carmi (2020) draws from rhythmanalysis to coin the term rhythmedia, referring to the algorithmic ordering of user activity into predictable commodities. Lupinacci (2024) stayed close to Lefebvre by adopting a phenomenological approach to the study of ‘algorhythms’ in everyday life, letting participants fill out a diary on their daily engagements with social media platforms. Here she uses the notion of rhythm ‘to operationalize the idea of temporality as both subjectively experienced and orchestrated in certain ways by the platforms themselves’ (Lupinacci, 2024: 4080). These studies make clear how rhythm urges us to think in more nuanced and complex ways about the material-cultural processes of digital media while also showcasing practical implementations of rhythm as a method.

The affective and aural turns in media studies

The above research on mediated rhythms sits alongside a growing body of work on affect in media studies, which often employ aural notions as a way to convey the pulsing, ambient and multisensory experience of digital platforms, like ‘reverberation’ (Kuntsman, 2012) and ‘resonance’ (Paasonen, 2020). In this section, we briefly survey work related to these ‘affective’ and ‘aural’ turns in media studies and connect it to similar concerns that our take on digital rhythmanalysis addresses.

The affective turn names a growing academic focus in the humanities on non-representational, non-cognitive and non-conscious dimensions of culture and society (Clough and Halley, 2007), building upon pre-existing post-structuralist theories of affect (Gibbs, 2020: 118; see Brennan, 2004; Massumi, 2002; Thrift, 2007). What these approaches share is a granular and materialist understanding of the fuzzy and relational properties of contemporary forms of life, one that is less focused on content and representation than traditional approaches in media studies. Nigel Thrift's non-representational approach, for example, focuses on ‘how particular hybrid compositions attain and keep coherence, become bodies of influence’ (2007: 222). Zizi Papacharissi's work on ‘affective publics’ (2014) has been key in shifting attention from cultural representations towards the ‘affective affordances’ (Hautea et al., 2021) of digital media environments. Here, the point is that media involve not just an immaterial exchange of messages but also mediate the body in an open-ended relation to the world and others.⁶

While affect theory has also been criticized for overstating the ‘autonomous’ status of affect in contrast to the realm of cultural ‘representation’, Garcia has noted how affect always resonates through culture and materiality and that connecting these helps to understand ‘how subjects come into attunement with shared energies, how vibration comes to matter meaningfully’ (2020: 33). Studying electronic music and club scenes, Garcia identifies a strong affinity between the domain of affect and that of sound due to how both occupy the same intangible spatio-temporal medium of an ‘atmosphere’ or ‘ambiance’ (2020; see also Roquet, 2016). Both affect and sound are relational forces moving between and through bodies (Anderson, 2009) rather than individually localizable or personally possessed emotional or mental states. These rhythms do not obey any overarching logic (like ‘virality’) but possess their own spatio-temporal and affective character. Similarly, Paasonen has developed the notion of ‘resonant media’ to describe ‘the appeal, stickiness, and force that some media content holds’ (2020: 51). For Paasonen, ‘resonance’ in online environments offers ‘a means of addressing the oscillating registers of affect that alter in their qualities, rhythms, speeds, and intensities’ (2020: 51). As one more concrete example of this in new media scholarship, Cho (2015) theorizes how the circulation of images on Tumblr evoke a ‘queer reverb’, a temporal sensation of posts disappearing and reappearing years later at the whims of Tumblr's architecture and users.

Such affective-aural notions are abstract yet crucial to understand diverse sides of today's media and society. They, for instance, help understand how political ideas decreasingly spread through persuasion and argumentation but rather how they ‘resonate’ affectively through online platforms (Topinka, 2025). The spread of content through such affective resonance is related to another aural notion, ‘amplification’, which designates how diverse actors (platforms, journalists and influencers) may accelerate the proliferation of taboos and extremist imagery from fringe sources (Peck, 2020; Phillips, 2018). Renée DiResta (2021) even coined a portmanteau for this kind of propaganda: ‘ampliganda’. Similarly, Noordenbos and Tuters (forthcoming) propose the concept of ‘ambient propaganda’ to grasp the resonance of pro-Russian content on TikTok. What they refer to as ‘mimetic entrainment’ ‘involves an active, ongoing imitative process of calibrating and synchronizing one's body with the rhythms of musical and performative memes’, where ‘the notion of rhythm constitutes a mode of relating to oneself and one's environment’ (Noordenbos & Tuters forthcoming: 6).

In their research on the ‘reverberation’ of Russian propaganda through TikTok videos, Geboers and Pilipets (2024) describe the affective bonding created by memetic soundscapes as performative rather than persuasive. Reverberation, as Kuntsman notes, ‘makes us attentive to the simultaneous presence of speed and stillness in online sites; to distortions and resonance, intensification and dissolution in the process of moving through various digital terrains’ (2012: 2). As such it deflects from the rhetorical content of the message to focus on its movement across media environments in a way that is unpredictable, as it could ‘stick to’ or instead ‘bounce off’ specific spaces. Such movements become rhythmic in how they congeal into a repeatable, metastable form, consolidating and ultimately becoming part of the background conditions themselves. In his work on the ‘social media sleepwalker’, Tony D. Sampson exemplifies this by building on Deleuze and Guattari's notion of the refrain to describe ‘dark refrains’ as moments when affective rhythms become so ‘harmonized’ that reactionary actors ‘repeat themselves over and over again, without difference’ (2020: 5; see also Tuters, 2023).

New vocabularies from the affective and aural turns often better approximate the new temporal and multisensory logics of digital platforms than prior paradigms. Notions like affect and resonance, for instance, do a much better job at conveying the sensations related to fuzzy TikTok trends or Spotify moods than representational, object-focused terms like the meme or the network. Especially terms like ‘vibes’, ‘moods’ and ‘energies’ have already become commonplace on social media to articulate not only what is being shared, read or seen, but also ‘what being online presently feels like’ (Tanni, 2024: 44, 50, 63). We indeed seem to have entered the era of ‘mood-regulating media’ (Roquet, 2021) as exemplified by ‘ambient’ playlists on YouTube, Spotify's shift from genres to moods and aesthetic mood boards on Pinterest.⁷ In a New Yorker essay on the popularity of ‘vibes’, Kyle Chayka (2021) writes how from the late 2010s onwards, vibes became ‘a medium for feeling, the kind of abstract understanding that comes before words put a name to experience’ and whose ‘pre-linguistic quality makes them well suited to a social-media landscape that is increasingly prioritizing audio, video, and images over text’. Robin James argues that this proliferation of vibes marks a ‘vernacularization’ of the artificial intelligence (AI)-driven recommendation techniques used by platforms like TikTok, writing how ‘vibes discourse adopts an epistemic and ontological framework that is analogous to the one contemporary computational algorithms use to perceive and model our reality’ (2024). ‘Vibes’, in other words, signals yet another attempt to linguistically convey the feeling of today's digital lifewords and (especially with its connection to ‘vibration’) can thus be seen as part of the larger affective-aural shift outlined above.

Doing digital rhythmanalysis

Taking note of these affective-aural shifts, with digital rhythmanalysis, we aim to connect Lefebvre's intuitions with new temporally oriented media research and new methodologies for ‘listening’ to online rhythms. Taking inspiration from the etymology of rhythm, the goal of digital rhythmanalysis is to interrogate ‘dynamic shapes’ and ‘patterned variations’ established by digital media and their users. It takes inspiration from digital methods research that use longitudinal methods to materialize temporality online⁸ while also aiming to do justice to non-representational and affective processes. It prioritizes time and the non-representational as a corrective to the dominance of the spatial, without fully ‘omitting the spatial and places’ (Lefebvre, 1992/2004: 22) or overestimating the autonomy of affect (Garcia, 2020). As we will show, digital rhythmanalysis may ‘spatialize time’ by repurposing visual units to analyse online rhythms, and other authors have demonstrated similar moves.⁹ In this section we elaborate on our framing before discussing practical challenges through several examples.

Studying the evanescence of audio-visual data and the rhythmic acts of swiping, scrolling and streaming requires methods that are sensitive to time, and in this way urge for a rebalancing of the emphasis on space that has dominated the study of digital media. This spatial focus is notably embedded in topological epistemologies and network analyses, which have long been the go-to approach in digital methods research (Boullier, 2018; Moats and Borra, 2018). Striving to ‘repurpose’ the ‘methods of the medium’, digital methods have long followed the logics of links and networks inherent to the architecture of the early Web and, as a corollary, adopted topological perspectives. These analyses are still relevant as even ‘post-viral’ platforms like TikTok remain highly networked through sounds, stitches and hashtags and because network visualizations are capable of incorporating temporal dimensions (Borge-Holthoefer and Gonzalez-Bailon, 2017; Snijders, 2005). Still, to remain committed to ‘medium-specificity’, digital methods should follow the increasingly temporal, affective and multisensory logics of online ‘algorhythms’ and ‘vibey’ feeds. Spatial metaphors representing the Web as networks of more-or-less static documents feel increasingly amiss when dealing with audio-visual streams, evanescent feeds or stories and algorithmically curated vibes or ‘cores’.¹⁰ Despite their many advantages, network analyses tend to flatten active relational processes and diffuse movements (Munster, 2013) which increasingly define the post-viral Web.

The alternative methodological repertoire for digital rhythmanalysis will vary and require experimentation. How, for example, can researchers not only come to feel but also to convey rhythms? How to translate the affective sensation of participating in audio-visual feeds? This process will involve some phenomenological, bodily engagement. Researchers could opt for qualitative descriptions and non-representational accounts, akin to Lefebvre's phenomenological approaches and as inspired by affect theory (see, e.g. McCormack, 2002). Long-term participation may be paramount to witness and communicate the slow, subtle churn of both repetition and variation. Qualitative accounts of ‘felt’ rhythms can then be juxtaposed with data-driven, digital methods-inspired approaches, which will invariably refract but can nonetheless translate diverse rhythmic movements. Below, we, for instance, use flow graphs that map the ‘combination[s] of ups and downs, motion and rest, tension and release’ (Lynch, 2020: 276) over time in discussion threads on Reddit. The audio-visual density of modern platforms calls for methods that are capable of conveying ephemerality, possibly through modalities that are themselves ephemeral, like videos or soundscapes (see Ikoniadou, 2023; Lyon, 2019, 72–75; Rogers, 2023). Timelapse videos are commonly used in rhythmanalyses (Lyon, 2019; Simpson, 2012) but one could also think of more experimental and multi-modal techniques.¹¹ We have for instance experimented with video walls of ‘Italian brainrot’ on TikTok, merging a hundred videos into a single pane so they each play simultaneously, conveying an affectively intense view on their shared aesthetics.¹² Pilipets and Chao (2025) outline the practice of ‘metadating’, where researchers spend time with digital data not just as one-dimensional units but as complex and multi-faceted audio-visual objects, using a range of participatory and computational techniques to study the complexities of such ‘sonic social media’. In conjunction with these innovative methods, rhythms on both micro and macro scales may warrant quantitative statistics. Slow shifts in online contents, for instance, can be hard to put into words let alone memorize, but may be rendered concrete through computational time series analyses (Wells et al., 2019).

The analysis of digital rhythms will thus require a range of methodological registers. This is conveniently in line with Lefebvre's idea of rhythmanalysis as a quali-quantitative approach:

Rhythm reunites quantitative aspects and elements, which mark time and distinguish moments in it–and qualitative aspects and elements, which link them together […] Rhythm appears as regulated time, governed by rational laws, but in contact with what is least rational in the human being: the lived, the carnal, the body. (Lefebvre, 1992/2004: 8–9)

This ‘carnal’ aspect means that the rhythmanalyst should not measure but also ‘feel’ rhythms: ‘to grasp a rhythm it is necessary to have been grasped by it; one must let oneself go, give oneself over, abandon oneself to its duration’ (Lefebvre, 1992/2004: 27). With the body as an antenna, the rhythmanalyst ‘will listen to the world, and above all to what are disdainfully called noises, which are said without meaning, and to murmurs [rumeurs], full of meaning–and finally he will listen to silences’ (Lefebvre, 1992/2004: 19). Lefebvre recognizes that this intention made rhythmanalysis akin to phenomenology, but he also stresses how certain rhythms have to be rendered legible by scientific devices (1992/2004: 8–9, 18). Digital rhythmanalysis takes note of this call to listen from the ‘inside and outside’ (1992/2004: 27) and aims at studying the rhythmic whole and its parts (see also Latour et al., 2012; Venturini, 2024) – to ‘feel’ digital rhythms next to quantifying them. Quantitative analyses of rhythms on their own have little resonance with the affective experience of being submerged in the flows of online media. Conversely, qualitative rhythmanalyses may not be equipped to ‘listen to’ the datafied rhythms of platforms on both more granular and abstract levels. That said, digital rhythmanalysis should neither take shape as a dichotomous ‘mixed methods’ where the same rhythms are analysed through ethnographic and statistical means; as Lefebvre notes, different rhythms warrant different methods, and dichotomies such as the qualitative/quantitative may simplify their complexity and distinctiveness (Brighenti and Kärrholm, 2018: 10).¹³ This flexible and situational attentiveness to the quantitative as well as the qualitative, to datafied processes as well non-representational movements, sets digital rhythmanalysis apart from methodologies that focus on (visual) objects in temporally flattened big data corpora, like cultural analytics (Manovich, 2020) and network analyses.

From topos to rhythmos in digital methods

To illustrate what digital rhythmanalysis may look like and to address some of its practical challenges, we now turn to examples and prototypes. We first show more conventional ways to repurpose the rhythms of online platforms before speculating on a rhythmanalysis of memetic and affective movements.

One of the obvious hurdles in digital rhythmanalysis is how to operationalize an open-ended notion like ‘rhythm’ and how to define the rhythms of a specific platform. In Lefebvre's words, how can we ‘perceive distinct rhythms distinctly, without disrupting them, without dislocating time’ (1992/2004: 19–20)? What are the temporal characteristics of a medium? Are they cyclical or linear? Do we rely on timestamps to capture these? And if so, do we use them as a continuous variable, or do we group them by intervals? Such choices are crucial for detecting ‘meaningful shapes’. Before defaulting to static intervals, it is important to first think of whether we can let a digital rhythm define itself, resonant with the ‘fly on the wall’ approach of Lefebvre's phenomenology. For instance, the rhythm of a data feed can be ‘listened to’ by tracking user interactions as well as HTTP events that deliver new content (for an interesting example, see Yuill and Skeggs, 2020). Burton and Weltevrede (forthcoming) demonstrate this by visualizing both swipes by the user and back-end communication on Tinder, showing how the user's interactions are ‘arrhythmic’ in relation to the mechanical possesses occurring on the server. A qualitative approach to defining platform rhythms can be found in Lupinacci's (2024) method of interviewing users of social media. Here, participants are asked to themselves describe the temporalities and daily engagements with whatever social media they use, and through this get an emergent sense of lived, affective and bodily rhythms across platforms instead of merely within them.

During the 2021 Digital Methods Winter School, we experimented with letting a platform define its own rhythm through the Stream Modulator (Venturini et al., 2021). The idea was to repurpose the pace of a live chat next to a livestream to define important temporal ‘beats’ instead of using pre-set time bins. We did so by replaying the video at a speed inverse to the chat speed. This resulted in a playback that fast-tracked idle moments in the chat and highlighted moments of excitement, with the slowed-down pace allowing for qualitative listening when most warranted.¹⁴ Participants then used this method to write ethnographic accounts of affective eruption. Specifically, a conspiracist stream on DLive was interrupted by a technical issue just prior to the Capitol Hill breach in January 2021. In the few minutes that the video was down, the chat went into a state of paranoia where users started to frantically discuss the possibility of the channel being deplatformed. Such articulations of digital rhythms through repurposing the pace of user activity could become a key tool in the rhythmanalytical toolbox, since it follows the logics of streaming-based digital phenomena while allowing to ‘tune in to’ affective intensities. These methods will grow even more pertinent with emergent trends like the Chinese danmu (弹幕 or bullet curtain) where chat messages are projected onto videos and float across the screen–letting audiences collectively define their own affective rhythm and in so doing create ‘polyrhythms’ between streamers and audiences (Zhang and Cassany, 2020).

This mapping of rhythmic intensities underlines how not all time is created equal: short-lived yet intense moments can be more significant than long periods of idleness (Dayan and Katz, 1992). These ‘media events’, ‘media storms’ (Boydstun et al., 2014) or more generally ‘controversies’ (Munk and Venturini, 2022) often come along with a form of synchronization that in aural metaphors may be described as ‘attunement’, ‘harmonies’, ‘choruses’ or ‘refrains’ (Miyazaki, 2013: 136; Sampson, 2020; Tuters, 2023; Yuill and Skeggs, 2020). Knowing that both ‘murmurs’ and ‘silences’ may be worthwhile to listen to, it is important to decide whether these refrains ought to be sought after or avoided. Data from moments of unrest may for instance overshadow more mundane but equally insightful daily rhythms (see also Wells et al., 2019: 4023).¹⁵ The question of what moments to focus on can be guided by time series analyses capable of quantifying temporal (ir)regularities (Wells et al., 2019). But the decision on whether to focus on specific moments or broader movements may also emerge from long-term ethnographic engagement, which over time can help in attaining a critical distance from short-lived events and controversies.

In another project, we conducted a (proto-)digital rhythmanalysis of Reddit ‘megathreads’ (large discussion threads) dedicated to the 2020 U.S. Presidential election results. Elections are usually interesting ‘rhythmic events’ (Ikoniadou, 2023) that see large online platforms and their users behave irregularly, like by throttling or disabling core technical features.¹⁶ The usual rhythms of Reddit's r/politics subreddit were indeed reconfigured because of the sheer rate of comments, which made the subreddit function less as a forum and more as an ephemeral shouting match of ‘real-time’ commentary (Weltevrede et al., 2014). Our project aimed to convey both the feeling of participating in the fast-paced megathread and to quantify their rhythms as the votes trickled in. By ‘doomscrolling’ (Herman et al., 2023) until the final results ourselves, we shared the subreddit's feeling of bewilderment regarding both the excruciating slowness of the results and the unprecedented speed of the Reddit threads. By the last thread, what started as fairly mundane political discussion had turned into a sleep-deprived yet flourishing subculture, rife with niche memes and fan fiction on CNN presenters. Figure 1 shows the number of comments and the total lifetime of all megathreads. A simple time series analysis like this already showed clear circadian cycles and moments of affective intensity, matching our lived experience of seeing the commentary dwindling at night and exploding with exit polls. What set the project apart from a ‘regular’ digital methods project was thus how these rhythms were both measured and felt; we used methods that could map movements imperceptible from an ethnographic viewpoint, but also qualitatively described the affective experience with the megathreads, which no visualization could ever accurately translate.

Figure 1.

Amount of comments per hour within the 83 live megathreads on r/politics during the 2020 U.S. Presidential elections. Coloured by thread. Data gathered with Pushshift and PRAW.

A digital rhythmanalysis of memes and vibes?

The examples so far have repurposed concrete objects like chat messages and Reddit comments. Can we imagine a digital rhythmanalysis that ‘tunes into’ the rhythm of more fuzzy, non-representational and intangible phenomena? Affective rhythms are nigh impossible to visualize or quantify, but both qualitative rhythmanalyses and audio-visual representations may act as a proxy for them. While the rhythm of memes is also bound to imperceptible affect (Gries, 2015), they also take shape as tangible proxies that can be followed and listened to. Like rhythms, memes by definition encompass repetition and variation, as theorized in earlier work (Hagen and Venturini, 2024). Just as ‘one isolated data point is intrinsically worthless’ when studying rhythms (Yuill and Skeggs, 2020: 128), analysing memes as singular objects may miss how they, by definition, only attain meaning when mobilized and transformed through various cultural and technical processes. This synergy between memes and rhythms renders analyses of ‘memetic rhythms’ promising as it potentially leads to insightful accounts on complex cultural-material procedures online.

Some of our prior work has already engaged in temporal tracing of memes (Hagen, 2024; Hagen and Venturini, 2024). In this last section, we discuss new experiments that emphasize their rhythmic dimension. Returning to Reddit, we can, for instance, study threads of highly memetic subreddits where every post is a memetic image or video. Taking as a test case the subreddit r/BatmanArkham, Figure 2 shows the position and popularity of threads based on Reddit's ‘hot’ algorithm across 11 days. The graph highlights how the subreddit rhythmically churns out popular memetic iterations on a daily basis (the large red flows). Being familiar with this subreddit ourselves, the descent of the red flows quite aptly conveys the rhythmic and affective sensation of witnessing the space's memetic trends. Replacing these abstract flows with images emphasizes their visual dimensions (Figure 3). These ‘subreddit tapestries’ show how digital rhythms differ between more idle topic-oriented forums (r/Conservative) and more erratic memetic ones (r/2westerneurope4u). In contrast to corpus-driven cultural analytics, a proper digital rhythmanalysis would use these visualizations as one ‘device’ to convey platform rhythms, potentially complementing it with ethnographic engagement, phenomenological accounts or intermittent content analyses.

Figure 2.

Eleven days of r/BatmanArkham. The size and color of the flows indicate the post score at capture time (the taller and redder, the higher the score). Flow graph made with RankFlow (Rieder, 2015).

Figure 3.

Seventeen days of the ‘hot’ algorithm on Reddit (r/Conservative and r/2westerneurope4u). See oilab.eu/dra for the full-size image.

These methods spatialize ‘memetic rhythms’ but do not engage in the challenge of mapping the rhythm of digital creations that resemble each other but are not formally defined as the same ‘meme’. We already discussed how deep learning methods are used by modern platforms to designate similarity between cultural objects, using a countless number of features to determine what will emerge with the next swipe. Following these new ‘methods of the medium’ would mean repurposing or reverse-engineering these algorithmic rhythms (Miyazaki, 2012) or developing our own deep learning methods to approximate how distances in latent spaces are at the core of temporal feeds (and with it, connect the spatial to the temporal). While this may require significant computational resources and thus renew the risk of a ‘crisis of empirical sociology’ (Savage and Burrows, 2007), relatively simple deep learning techniques and open-access models can already be used to identify and visualize rhythms in memetic contents.

To demonstrate this, Figure 4 shows the same type of Reddit-thread flow graph as above, but with a colour overlay showing the similarity between an ‘original’ meme and subsequent variations using a multimodal transformer model (CLIP ViT-B/32).¹⁷ Here, we show the start of one of the most popular memes of r/BatmanArkham: ‘Officer Bald’, an image of a smiling baby that was originally posted with the text ‘I FUCKED 9 months ago. What should I name it?’.¹⁸ This sentence built on a previous memetic phrase (‘what the FUCK should we name […]?’) and successive versions of ‘office Bald’ posts tweaked both the image and the text. To capture this multimodal rhythm, we assign each ‘hot’ post in the span of 5 days an embedding using both the image data and the post title. In Figure 4 we see threads that fall below (grey) and above (green) the cosine similarity threshold for what counts as an ‘officer Bald’ iteration (0.6). While the model generates a few false positives, our first attempt is already remarkably accurate at not only identifying clear visual copies, but also posts that are visually different yet iterated on the meme in more indirect or ‘vibey’ ways. These include an ultrasound scan, an image of sperm cells, a picture of an action figure with a similar post title (‘Who the FUCK is this?’) and even a screenshot of a Reddit post containing the word ‘son’. The blue-green flows indicate how such variations kept the memetic rhythm going days after the original officer Bald post was posted. Despite the numerous computational challenges of deriving meaning from memes (Pandiani et al., 2024), this brief attempt already demonstrates how multimodal models can be effective in identifying fuzzy associations in memetic rhythms.

Figure 4.

A possible way to represent the memetic rhythm of the ‘Officer Bald’ meme on r/BatmanArkham. See oilab.eu/dra for the full-size image.

Our brief showcase of possible directions for digital rhythmanalysis cannot do justice to its practical complexity. Defining rhythmic patterns in fuzzy cultural data, for instance, raises all sorts of problems, in particular with deciding how much repetition is needed and how much variation is tolerated when drawing the boundaries between different objects. Working with affective and memetic rhythms means accepting that such boundaries are uncertain and should be represented as such – a problem that has been discussed in the context of statistical charts (Drucker, 2011) and network visualizations (Venturini et al., 2021) but not yet for rhythmanalyses. Then there are the practical issues of longitudinal analyses. Since digital rhythmanalysis may require listening to rhythms as they happen, research cases often need to be known before they happen. Data access from large online platforms is moreover already difficult but becomes even more complicated when having to be done intermittently.¹⁹ Long-term engagement is resource-intensive but often required for critically examining to what extent ‘something new and unforeseen […] introduces itself into the repetitive’ (Lefebvre, 1992/2004: 6). Even before encountering the difficulties of quali-quantitative analyses and conveying rhythmic sensations, then, digital rhythmanalysis requires careful planning. At the same time, its recognition that rhythms exist at the most granular detail and cosmic levels offers flexibility to study large movements as well as minute processes through the analysis of ‘small data’ (Kitchin and Lauriault, 2014), as we have demonstrated with the cases above.

Conclusion

In this paper, we explored how we can approach the study of digital culture and platforms through a framework that is sensitive to the affective temporality of today's Web. Multisensory streams and ephemeral feeds are complemented by intangible and fuzzy genres, often captured via metaphors like ‘aesthetics’, ‘energies’ and ‘vibes’. We put forward ‘digital rhythmanalysis’ as a framework for researching these new platformed temporalities, including the affective and memetic rhythms that traverse them. We drew from Lefebvre's work to propose a quali-quantitative approach that ‘listens’ to platforms and makes itself ‘more sensitive to times than to spaces’ (1992/2004: 22). Digital rhythmanalysis extends the analysis beyond moments of viral intensity and controversy to also consider the more mundane ritual habits of everyday digital life in conjunction with algorithmic logics or ‘algorhythms’ (Miyazaki, 2012, 2013). Compared to topological methods and memetic frameworks, rhythmanalytical perspectives can better approximate the logics of many new native digital-cultural processes, including fuzzy affects and pulsing, emergent and ephemeral vibes. Still, the conceptual and practical challenges are numerous. Our hope is that our contribution will prompt experimentation and innovation with conveying and capturing diverse rhythms.

While we can read our contribution as a response to Lefebvre's call for the rhythmanalyst to ‘professionalize themselves’, in reality we might not be the true inheritors of his vision. If any entity embodies Lefebvre's ambition today, it would arguably be the platforms themselves, capturing and analysing our everyday personal, social, cultural and political lives in granular detail. This imbalance will likely persist in the near future, if nothing else because of unequal access to data and computing resources. What do our rhythmanalyses then have to offer compared to those of large platforms? We may once more turn to Lefebvre for an answer. As mentioned above, he had argued that space and time are both subject to historical and material power relations, presciently observing how ‘the increasing intensity of communications harbors the reinforcement of daily life, its consolidation and confinement’ while critiquing the ‘information society’ for ‘fetishiz[ing] its own process of production and its ingression into everyday existence’ (qtd. in Aronowitz, 2015: 87–89). Indeed, platforms ‘listen’ to their users not to develop critical understandings, nor to suggest progressive, revolutionary or emancipatory interventions in their digital environments. Instead, under the auspices of ‘surveillance capitalism’ (Zuboff, 2019), they seek to maximize engagement and profit. Academic digital rhythmanalysis, then, ought to refrain from flattening complex human interactions and culture and rather ‘negotiate rather than reduce [their] complexity’ (Yuill and Skeggs, 2020: 129). Drawing from its (post-)Marxist implications, our rhythmanalyses may even dedicate itself to a critique of how everyday cyclical habits fuel platform capitalism (Bennett, 2023; Chun, 2016). Scholarly rhythmanalysis will need to ‘professionalize’ indeed, yet by taking a more critical trajectory than engagement-optimizing platforms.

Footnotes

Acknowledgements

Our thanks go out to the members of the ASCA Digital Rhythmanalysis Reading Group, the DMI Winter School 2021 participants, for contributing to the project ‘Streams of Conspiratorial Folklore’, Alessandra Facchin in particular, and Stijn Peeters for his work on the video wall processor in .

ORCID iDs

Sal Hagen

Daniël de Zeeuw

Tommaso Venturini

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was partly funded by the European Research Council project “Deep Culture—Living with Difference in the Age of Deep Learning” (Grant no. 101141330, ).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Notes

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