Or, the political implications of Lil Nas X, Tekashi 69 and Childish Gambino
There’s been a ton of interest in this new platform and the role it might play in politics. However, that discussion has largely been structured by journalists, who do not have the same goals as social scientists. These case studies are useful but they are no replacement for analysis with a broader scope.
The following is part of a new project with my excellent co-authors Fabio Votta and Benjamin Guinaudeau. They have done fantastic work collecting and analyzing the data, which we think will be of significant interest. But it’s not done yet. Our hope is to have a discussion on our theory of TikTok, and to do so before the empirics are finalized.
At a minimum, we don’t think that anything like this document exists, and we think it will help inform discussions about what TikTok is and what TikTok can do.
(A note on terminology: we use “TikTok” to refer to the company and “tiktok” to refer to the individual post.)
What is TikTok?
TikTok is a social media platform targeted at young, mobile-first users. Chinese company ByteDance owns both TikTok and its China-only cousin Douyin, which was founded in September 2016. TikTok was launched a year later, and kickstarted its growth in the US by acquiring and merging with lip-synching app musical.ly in late 2017. TikTok was the most downloaded app in the US in 2019, and second in the world to WhatsApp.
Each tiktok is a 3 to 60 second-long video that loops when finished. The majority of the screen is taken up by the video uploaded by the user. The app offers an extremely wide range of options for customizing these videos, including: video taken with the user’s smartphone; photos uploaded from the web; emojis and other text superimposed on the video; and a library of filters and video-distorting effects.
Other users can leave comments on each tiktok, including comment threads which the creator can choose to endorse. The bottom of the screen contains information about the “sound” the tiktok uses, which can either be user-uploaded or chosen from a library of popular sounds.
Upon opening the app, the user encounters a tiktok that starts playing; this is the “For You Page,” which plays tiktoks that TikTok’s algorithm recommends for that user. To go to the next tiktok, the user swipes up. To see the account which uploaded the current tiktok, swipe right. The user’s profile is spare, with a brief bio and the catalog of that user’s previously uploaded tiktoks. The metrics for the account include well-known follower and following numbers, but introduce a new metric that reflects the relative unimportance of “following” on TikTok: the total number of “likes” that user has received across all of their tiktoks. The presence of this metric also discourages users from deleting their old tiktoks, as is now common practice on Twitter and Instagram.
Political Communication, Pre- and Post-TikTok
Social scientists have accumulated a wealth of knowledge about political communication on social media. We have to use this knowledge to understand TikTok; if we take an empirical, inductive approach to study every new platform, we will always be fighting yesterday’s war. TikTok represents the synthesis of four of the most powerful trends in social media: a feed that displays many distinct and complete pieces of content per minute; the televisual medium that has always been the most broadly popular and powerful; algorithmic recommendation that structures the user’s experience to a greater extent than any major social media platform to date; and a mobile-only interface designed to take advantage of a smartphone’s user-facing camera.
On the other hand, we cannot allow theories developed to explain political communication in other contexts to define the space of permissible research questions. We follow Settle (2018)’s call for researchers to avoid shoehorning new media formats into classic theories of political communication in favor of re-orienting our theories to reflect how and why these new media are used. In the case of social media, the primary purpose is social: users want to learn about and perform for their peers. We expand on this style of analysis in the case of TikTok below. We define four theoretical angles to understand politics on TikTok. Each of them is a synthesis of previous research on a subset of the affordances listed above: information density, ease of posting, algorithmic rewards, and sounds.
The fusion of these first two elements means that the key theoretical feature of TikTok Politics from the perspective of the literature on media effects is thus information density. The televisual medium, relative to text, affords increased capacity for the content creator to communicate emotion (Houwer and Hermans, 1994) and increases their propensity to generate an emotional response (Paivio, 1990). This is because video takes advantage of both cognitive systems that separately process verbal and visual information, whereas text only provides verbal information. The prevalence of political televisual content has traditionally been constrained by high capital costs of production and the related need for temporally linear and extensive media. The feed allows for non-linear and punctuated media, but has for years been restricted to mediums (text and photos) that can be produced and consumed quickly. On TikTok, the first televisual feed, the information density afforded by each of the feed and the televisual is multiplicative.
Whatever the effect of political media on TikTok is, this information density predicts that it will be larger, per hour consumed, than political media on other platforms. We can also theorize about the type of information that is being communicated through the visual, peripheral pathway.
The primary format for the political tiktoks we describe is the vlog, in which the creator’s bedroom is visible and they look into the camera and either dance or emote in combination with music or superimposed textual images. This represents an extension of the credibility-via-relatability described by Lewis (2020), Abidin (2018) and other theorists of “influencers” or “micro-celebrities.” The point is for the creator to communicate a “mood” or “vibe” that signals to the audience that they should take the creator seriously.
Our clear difficulty in explaining this format in words reflects the relative paucity of information that can be conveyed in the textual medium, or alternatively, the information density of a single tiktok. Much of this information does not concern the topics that have traditionally comprised “Political Knowledge”; the brand of water bottle on the tiktoker’s desk or the song to which they choose to dance not inform the viewer about whether the unemployment rate is going up or down or who is currently representing their district in the House. These televisual stimuli do, however, communicate the social position of the tiktok creator, creating in the viewer a map between the explicit political information and orientation advanced and that social position. The viewer can then triangulate their own position against that of the creator and decide whether the creator and their political position should be taken seriously.
A standard theory of information processing in American Politics involves partisan motivated reasoning. Partisan media consumers want to understand the implications of a given news item from the perspective of how it impacts the party with which they identify (Bolsen, Druckman, and Cook, 2014). Although partisan consumers might agree on a given political fact, they are quick to contextualize it with a model of the world that accounts for that fact (Bisgaard, 2019).
There are useful points of synthesis between this literature and the growing literature on identity alignment (Mason, 2018). Affective polarization—the tendency for partisans to dislike and distrust out-partisans (Iyengar, Sood, and Lelkes, 2012)—has been more pronounced among partisans who do not have cross-cutting identities (Mason, 2016). The logic is analogous for partisan motivated reasoning: if the goal of consuming political media is to become informed about topics that “people like me” are informed about, then the viewer should be deeply invested in the high-dimensional identity data intrinsic to these tiktoks but more difficult to come by in more textual social media platforms.
There has also been considerable work in political sociology about the proliferation of nuanced cultural signifiers of a person’s politics (DellaPosta, Shi, and Macy, 2015). The classic example is of “Latte Liberals,” where the preference over a seemingly innocuous choice of consumption (style of coffee) is correlated with political ideology. Polarization has become more acute in the realm of “lifestyle politics,” but not because conservatives’ abhorrance of lattes has grown sharper. DellaPosta (2020) finds evidence of what he calls “oil spill” polarization: lifestyle political alignment has become broader, encompassing a larger swath of lifestyle.
Reinforcing this effect, the young userbase means that even highly politically interested and informed individuals may have unusually weak partisan attachments. The “space” of political tiktok is thus likely to have more than one dimension, matching the space afforded by the thick signifiers of televisual communication. This weak partisanship also means that other forms of identity might be more important bases for motivated reasoning.
Ease of Posting//Algorithmic Rewards
Algorithmic recommendation is perhaps more central to the user’s experience of TikTok than any other platform. Like YouTube (and unlike Facebook, Twitter and Instagram), the recommendation algorithm on TikTok can draw from the entire universe of tiktoks, not only ones created or shared by an account the user has “followed” or “liked.” One feature of any social media feed in environments of content abundance is the necessity of sorting to determine the order in which pieces of content are shown to the consumer, and thus ultimately (given a fixed time period) which pieces of content are viewed at all. TikTok’s user interface centers recommendations (on the “For You Page”) to a far greater degree than other platforms by rendering its internal architecture more opaque.
This opacity also makes it difficult for researchers to collect data about how the algorithm operates in terms of what kind of content is shown to whom; a major concern about algorithmic curation on YouTube, for example, is that it increases viewership of extremist content and is thus a vector for far-right radicalization (Tufekci, 2018).
The present paper cannot speak to this concern. Instead, we highlight the intersection of the recommendation algorithm with the fourth major affordance: TikTok is primarily available as a mobile phone app, explicitly optimized for the front-facing, vertical-orientation camera that feels most natural for its mobile-native target audience. This camera style enhances the user’s sense of immersion and social presence (Wang, 2020).
The combination of algorithmic recommendation and mobile-first design produces what we see as the most theoretically relevant aspect of TikTok from a content production standpoint: it lower the barriers to entry and encourages a high number of viewers to become posters.
All social media networks have to solve the problem of the construction of a network. No one wants to post into the void, but others don’t want to create a network tie with someone who never posts.
Facebook, LinkedIn and SnapChat aim to become essential for certain forms of social life, relying on users to import their own social networks to the platform. Twitter and Instagram relied on a similar strategy initially, but then developed the hashtag as a way for users with similar interests to find each other and create follower networks (Thorson et al., 2016). As the platforms have matured, this represents a significant barrier to entry for new users (unless they have a specific commercial interest or another source of fame), a particularly acute problem for Twitter’s recent growth in the US market. YouTube is distinct in that it has a huge ratio of media producers to consumers, allowing the platform to create different affordances for producers.
TikTok tries to short-circuit this process by guaranteeing an audience for every post. When the user first downloads and opens the app, a tiktok starts playing immediately. The default feed is the “For You Page,” which will continue to provide new videos based on the extent to which the user engaged with previous recommendations. Part of this process involves recommending videos with extremely few views.
The tiktok-production also includes a variety of menus with audio and visual effects that enable to the user to create novel kinds of videos with minimal effort. This mimics Instagram’s strategy for kickstarting early growth: provide users with “filters” that make their photos look cooler. Each tiktok also has a “sound” (discussed in more detail below), allowing the user to participate in popular meme formats.
More insidiously, the centrality of the algorithm disrupts one of the most fundamental laws of political media: audiences have always been stocks, not flows. Matthew Hindman has done the best work on this topic, first in the context of the blogosphere in Hindman (2008) and then on all web traffic, in Hindman (2018). Two trends in online audiences re-occur, approaching the status of social scientific laws: web traffic is distributed according to a power law, and The behavioral micro-foundation of these “laws” is user habit. The web offers unfathomable consumer choice, ironically heightening our dependence on heuristics and habits. Social networks based on “following” other entities (which are then algorithmically sorted according to the accounts we interact with most often) wear the grooves of user habit ever deeper, but these patterns were discernable in the mid-2000s when readers retraced their steps to visit the same handful of blogs and news websites.
The “For You Page” supplants “following” behavior entirely. Tiktoks simply appear on the screen, granting the platform incredible power in determining the fate of a given tiktok, whether it goes viral or “flops.”
The fickleness of virality in contexts with algorithmic recommendation is well established. Early web-native media companies like Upworthy relied on viral Facebook posts to distribute their articles. Their strategy was to optimize for shareability, relying on human endorsements to increase their visibility. They then saw their readership decimated by Facebook’s algorithm changes in 2014 (Munger, 2020). Facebook’s network-based model could merely change the “rank” a given post would appear in the user’s NewsFeed, but TikTok can go farther: at every point in every user’s “For You Page,” they can choose from any of the trillions of tiktoks on their servers.
We still don’t know how users interact with the app—how many people use it without following anyone, looking only at the purely algorithmic “For You Page,” and how many people use the more traditional “Accounts you Follow” option. But TikTok is the only social media platform where the former option is possible at all. Furthermore, many of the users of the platform are keenly aware of the metrics of their popularity, and pay close attention to how each of their videos performs.
This is an equalizing force for new or unpopular accounts: even without cultivating any following whatsoever, every tiktok is seen by someone. If they engage with it at all (a lower bar than retweeting on Twitter, the only other platform where this viralityfrom-nowhere is possible), it is shown to more people.
The combination of the complete opacity of the algorithm and the ease of posting means that there is an unbelievable range of tiktoks that might appear while scrolling the “For You Page.” Many of these videos are similar; as we discuss below, many of them are iterations of the latest meme format, Unlike retweets or social endorsements like playcounts (already a fickle mapping from quality to success (Salganik, Dodds, and Watts, 2006)), the passive nature of engagement on TikTok gives the app unprecedented discretion over the ultimate popularity of many roughly similar tiktoks.
That is, every tiktok has a chance to go viral—mimicking the logic of variable rewards that BF Skinner found to be the most effective schedule for operant conditioning. This insight has long been used by designers of machine gambling devices to optimize their slot machines for addiction (Schull, 2014), and has more recently been applied to video game “loot boxes” (where rewards for achievements take the form of a random prize ), which have also been shown to have an addictive quality (Drummond and Sauer, 2018).
Although there is no conclusive evidence that posting tiktoks is addictive, the company seems to admit that watching them may be. The app shows a “public service announcement” from the account tiktoktips when a user has been scrolling the “For You Page” for over 90 minutes.
There does exist something of a cargo cult of “the algorithm.” It is commonplace for tiktoks to be captioned that the user has been “shadowbanned” (their content is not being shown to others), and the phrase “don’t let this flop” evinces the anxiety and desire for viewership that accompanies each upload.
The importance of the algorithm can be estimated from the variance in the viewership numbers for tiktoks created by a single user. The old model of the web, based on audience habits, implies that audiences are largely stable across time; a newspaper based on subscriptions is an antiquated and strong example, but the principle for follower- or subscriber-based social media is similar. ,
“Sounds” and Embodied Memes
In addition to the four extant affordances synthesized in TikTok, the platform has a feature that is truly novel and may play an outsized role in structuring how content flows through its networks. Literat and Kligler-Vilenchik (2019) provides a useful discussion of how memes operate on TikTok, but we think that hashtags are no longer the best way to create a “mimetic corpus.”
Instead, we locate TikTok memes in the “Sounds” feature that comprises the audio for every tiktok. This feature has already upended the music industry. While ambitious record labels once trucked in favors or bribery to get their artists on the radio, today they pay TikTok creators to use their artists’ “sounds” in their tiktoks (Strapagiel, 2019).
An audio file (or “sound”) is saved, shared and remixed by people uploading their own video to comprise a tiktok. These sounds come to define a genre of video; each sound is a “meme format” and each TikToker’s take on that sound is the “meme.”
For example, the most popular song of all time (with the record for the longest streak atop the Billboard chart) is Lil Nas X’s “Old Town Road.” The song was originally uploaded on TikTok and attracted some initial attention, but only took off with the development of a meme format that used a clip from the song. The TikTok user begins dressed normally, takes a drink from a bottle labeled as “Yee Yee Juice,” then transforms into a cowboy and dances along with the infectious rap-inflected country beat.
Political tiktoks are similarly structured by the sounds that define popular meme formats. The API provides information about which sound each tiktok uses, allowing researchers an unprecedented capacity for studying meme diffusion without needing expensive and still-imperfect computer vision. We thus argue that this affordance is the first in which “memes” are encoded directly into the structure and metadata of the platform.
To take a step back, memes have long recognized as a key component of how young people engage with politics on social media and recently embraced by President Trump and other prominent politicians. Shifman (2014)’s book-length treatment of memes in digital culture provides a useful definition. Memes are “(a) a group of digital items sharing common characteristics of content, form, and/or stance, which (b) were created with awareness of each other, and (c) were circulated, imitated, and/or transformed via the Internet by many users” (p41).
Rephrasing this definition, memes are meta-referential inside jokes shared among a group of internet users that shares a common content, form or stance—which for political memes implies ideology. Memes, we argue, bear a strong resemblancee to the theory of frames in communication.
Political memes do not promote deliberation or even toleration; they act as mutually constructed tropes that can only be understood by their intended audience. In keeping with framing theory, they have no effect among viewers who do not possess the relevant cognitive schema to which they refer. To illustrate, consider some of the most prominent “sounds” in our dataset. Among left-leaning accounts, the sound “This is America” (taken from a song by rapper Childish Gambino) is frequently used in concert with footage of police violence in the context of the Black Lives Matter Protests of 2020.
Among right-leaning accounts, a popular “sound” is the chorus from the song “Gooba” by rapper Tekashi69. The lyrics in the clip are “Are you dumb, stupid, or dumb, huh?” This “sound” is frequently paired with the creator dancing in front of an image of one of their ideological opponents’ expressing a view with which they disagree.
In both cases, deliberation is not the point. Each meme format is an emotional and social appeal to either endorse or reject a stance in contemporary politics. The frames allow a given stance to appeal through the lens of established schema, of varying degrees of sophistication.
For example, a TikTok user might want to send a message that they are in support of universal mask wearing. They could use the “This is America” sound in combination with screenshots of US COVID-19 case growth and screenshots of posts on social media decrying the medical consensus in favor of universal mask adoption. Alternatively, they could type text, superimposed on an image of themselves making faces, about their older relative who refuses to wear a mask. The stance in both cases is the same, but the effect on the viewer depends on their recognition of the schema being invoked: the science denialist movement in the US, or the intransigence and susceptibility to misinformation among older people.