“Trump Shit Goes into Overdrive”: Tracing Trump on 4chan/pol/





4chan, actor-network theory, Trump, computational methods, text mining

How to Cite

Hagen, S. (2020). “Trump Shit Goes into Overdrive”: Tracing Trump on 4chan/pol/. M/C Journal, 23(3). https://doi.org/10.5204/mcj.1657
Vol. 23 No. 3 (2020): dissimulation
Published 2020-07-07

Content warning: although it was kept to a minimum, this text displays instances of (anti-Semitic) hate speech.


During the 2016 U.S. election and its aftermath, multiple journalistic accounts reported on “alt-right trolls” emanating from anonymous online spaces like the imageboard 4chan (e.g. Abramson; Ellis). Having gained infamy for its nihilist trolling subcultures (Phillips, This Is Why) and the loose hacktivist movement Anonymous (Coleman), 4chan now drew headlines because of the alt-right’s “genuinely new” concoction of white supremacy, ironic Internet humour, and a lack of clear leadership (Hawley 50). The alt-right “anons”, as imageboard users call themselves, were said to primarily manifest on the “Politically Incorrect” subforum of 4chan: /pol/. Gradually, a sentiment arose in the titles of several news articles that the pro-Trump “alt-right trolls” had successfully won the metapolitical battle intertwined with the elections (Phillips, Oxygen 5). For instance, articles titled that “trolls” were “The Only True Winners of this Election” (Dewey) or even “Plotting a GOP Takeover” (Stuart).

The headlines were as enticing as questionable. As trolling-expert Whitney Phillips headlined herself, the alt-right did not attain political gravity solely through its own efforts but rather was “Conjured Out of Pearl Clutching and Media Attention” (“The Alt-Right”), with news outlets being provoked to criticise, debunk, or sensationalise its trolling activities (Faris et al. 131; Phillips, “Oxygen” 5-6). Even with the right intentions, attempts at denouncement through using vague, structuralist notions–from “alt-right” and “trolls” to “the basket of deplorables” (Robertson) – arguably only strengthened the coherence of those it was meant to disavow (Phillips, Oxygen; Phillips et al.; Marantz). Phillips et al. therefore lamented such generalisations, arguing attributing Trump’s win to vague notions of “4chan”, “alt-right”, or “trolls” actually bestowed an “atemporal, almost godlike power” to what was actually an “ever-reactive anonymous online collective”. Therefore, they called to refrain from making claims about opaque spaces like 4chan without first “plotting the landscape” and “safeguarding the actual record”. Indeed, “when it comes to 4chan and Anonymous”, Phillips et al. warned, “nobody steps in the same river twice”.

This text answers the call to map anonymous online groups by engaging with the complexity of testing the muddy waters of the ever-changing and dissimulative 4chan-current. It first argues how anti-structuralist research outlooks can answer to many of the pitfalls arising from this complex task. Afterwards, it traces the word trump as it was used on 4chan/pol/ to problematise some of the above-mentioned media narratives. How did anons consider Trump, and how did the /pol/-current change during the build-up of the 2016 U.S. elections and afterwards?

On Researching Masked and Dissimulative Extremists

While potentially playing into the self-imagination of malicious actors (Phillips et al.), the frequent appearance of overblown narratives on 4chan is unsurprising considering the peculiar affordances of imageboards. Imageboards are anonymous – no user account is required to post – and ephemeral – posts are deleted after a certain amount of activity, sometimes after days, sometimes after minutes (Bernstein et al.; Hagen). These affordances complicate studying collectives on imageboards, with the primary reasons being that 1) they prevent insights into user demographics, 2) they afford particularly dissimulative, playful discourse that can rarely be taken at face value (Auerbach; de Zeeuw and Tuters), and 3) the sheer volume of auto-deleted activity means one has to stay up-to-date with a rapid waterfall of subcultural ephemera. Additionally, the person stepping into the muddy waters of the chan-river also changes their gaze over time. For instance, Phillips bravely narrates how she once saw parts of the 4chan-stream as “fun” to only later realise the blatantly racist elements present from the start (“It Wasn’t Just”).

To help render legible the changing currents of imageboard activity without relying on vague understandings of the “alt-right”, “trolls”, or “Anonymous”, anti-structuralist research outlooks form a possible answer. Around 1900, sociologists like Gabriel Tarde already argued to refrain from departing from structuralist notions of society and instead let social compositions arise through iterative tracing of minute imitations (11). As described in Bruno Latour’s Reassembling the Social, actor-network theory (ANT) revitalises the Tardean outlook by similarly criticising the notion of the “social” and “society” as distinct, sui-generis entities. Instead, ANT advocates tracing “flat” networks of agency made up of both human and non-human actors (165-72). By tracing actors and describing the emerging network of heterogeneous mediators and intermediaries (105), one can slowly but surely get a sense of collective life. ANT thus takes a page from ethnomethodology, which advocates a similar mapping of how participants of a group produce themselves as such (Garfinkel).

For multiple reasons, anti-structuralist approaches like ANT can be useful in tracing elusive anonymous online groups and their changing compositions. First, instead of grasping collectives on imageboards from the outset through structuralist notions, as networked individuals, or as “amorphous and formless entities” (see e.g. Coleman 113-5), it only derives its composition after following where its actors lead. This can result in an empirical and literally objective mapping of their collectivity while refraining from mystifications and non-existent connections–so often present in popular narratives about “trolls” and the “alt-right”. At the same time, it allows prominent self-imaginations and mythologizations – or, in ANT-parlance, “localisations of the global” (Latour 173-190) – rise to the surface whenever they form important actors, which, as we will see, tends to happen on 4chan.

Second, ANT offers a useful lens with which to consider how non-human actors can uphold a sense of collectivity within anonymous imageboards. This can include digital objects as part of the infrastructure–e.g. the automatically assigned post numbers having mythical value on 4chan (Beran, It Came From 69)–but also cultural objects like words or memes. Considering 4chan’s anonymity, this focus on objects instead of individuals is partly a necessity: one cannot know the exact amount and flow of users. Still, as this text seeks to show, non-human actors like words or memes can form suitable actors to map the changing collectivity of anonymous imageboard users in the absence of demographic insights.

There are a few pitfalls worth noting when conducting ANT-informed research into extremist spaces like 4chan/pol/. The aforementioned ironic and dissimulative rhetoric of anonymous forum culture (de Zeeuw and Tuters) means tracing is complicated by implicit (yet omnipresent) intertextual references undecipherable to the untrained eye. Even worse, when misread or exaggerated, such tracing efforts can play into trolling tactics. This can in turn risk what Phillips calls “giving oxygen” to bigoted narratives by amplifying their presence (“Oxygen”). Since ANT does not prescribe what sort of description is needed (Latour 149), this exposure can be limited and/or critically engaged with by the researcher. Still, it is inevitable that research on extremist collectives adds at least some garbage to already polluted information ecologies (Phillips and Milner 2020), even when “just” letting the actors speak (Venturini). Indeed, this text will unfortunately also show hate speech terms below.

These complications of irony and amplification can be somewhat mitigated by mixing ethnographic involvement with computational methods. Together, they can render implicit references explicit while also mapping broad patterns in imitation and preventing singular (misleading) actors from over-dominating the description. When done well, such descriptions do not only have to amplify but can also marginalise and trivialise. An accurate mapping can thereby counter sensationalist media narratives, as long as that is where the actors lead. It because of this potentiality that anti-structuralist tracing of extremist, dissimulative online groups should not be discarded outright.

Stopping Momentarily to Test the Waters

To put the above into practice, what follows is a brief case study on the term trump on 4chan/pol/. Instead of following users, here the actor trump is taken an entry point for tracing various assemblages: not only referring to Donald J. Trump as an individual and his actions, but also to how /pol/-anons imagine themselves in relation to Trump. In this way, the actor trump is a fluid one: each of its iterations contains different boundaries and variants of its environment (de Laet and Mol 252). By following these environments, can we make sense of how the delirious 2016 U.S. election cycle played out on /pol/, a space described as the “skeleton key to the rise of Trump” (Beran, 4chan)?

To trace trump, I use the 4plebs.com archive, containing almost all posts made on /pol/ between late-2013 and early 2018 (the time of research). I subsequently use two text mining methods to trace various connections between trump and other actors and use this to highlight specific posts. As Latour et al. note, computational methods allow “navigations” (593) of different data points to ensure diverse empirical perspectives, preventing both structuralist “zoomed-out” views and local contexts from over-dominating. Instead of moving between micro and macro views, such a navigation should therefore be understood as a “circulation” around the data, deploying various perspectives that each assemble the actors in a different way. In following this, the case study aims to demonstrate how, instead of a lengthy ethnographic account, a brief navigation using both quali- and quantitative perspectives can quickly demystify some aspects of seemingly nebulous online groups.

Tracing trump: From Meme-Wizard to Anti-Semitic Target

To get a sense of the centrality of Trump on /pol/, I start with post frequencies of trump assembled in two ways. The first (Figure 1) shows how, soon after the announcement of Trump’s presidential bid on 16 June 2015, around 100,000 comments mention the word (2% of the total amount of posts). The frequencies spike to a staggering 8% of all comments during the build-up to Trump’s win of the Republican nomination in early 2016 and presidential election in November 2016.


Figure 1: The absolute and relative amount of posts on 4chan/pol/ containing the word trump (prefixes and suffixes allowed).

To follow the traces between trump and the more general discourse surrounding it, I compiled a more general “trump-dense threads” dataset. These are threads containing thirty or more posts, with at least 15% of posts mentioning trump. As Figure 2 shows, at the two peaks, 8% of any thread on /pol/ was trump-dense, accounting for approximately 15,000 monthly threads. While Trump’s presence is unsurprising, these two views show just how incredibly central the former businessman was to /pol/ at the time of the 2016 U.S. election.


Figure 2: The absolute and relative amount of threads on 4chan/pol/ that are “trump-dense”, meaning they have thirty comments or more, out of which at least 15% contain the word trump (prefixes and suffixes allowed).

Instead of picking a certain moment from these aggregate overviews and moving to the “micro” (Latour et al.), I “circulate” further with Figure 3, showing another perspective on the trump­-dense thread dataset. It shows a scatter plot of trump-dense threads grouped per week and plotted according to how similar their vocabulary is. First, all the words per week are weighted with tf-idf, a common information retrieval algorithm that scores units on the basis if they appear a lot in one of the datasets but not in others (Spärck-Jones). The document sets are then plotted according to the similarity of their weighted vocabulary (cosine similarity). The five highest-scoring terms for the five clusters (identified with K-means) are listed in the bottom-right corner. For legibility, the scatterplot is compressed by the MDS algorithm. To get a better sense of specific vocabulary per week, terms that appeared in all weeks are filtered out (like trump or hillary). Read counterclockwise, the nodes roughly increase in time, thus showing a clear temporal change of discourse, with the first clusters being more similar in vocabulary than the last, and the weeks before and after the primary election (orange cluster) showing a clear gap.


Figure 3: A scatterplot showing cosine distances between tf-idf weighted vocabularies of trump-dense threads per week. Compressed with MDS and coloured by five K-means clusters on the underlying tf-idf matrix (excluding terms that appeared in all weeks). Legend shows the top five tf-idf terms within these clusters. denotes the median week in the cluster.

With this map, we can trace other words appearing around trump as significant actors in the weekly documents. For instance, Trump-supportive words like stump (referring to “Can’t Stump the Trump”) and maga (“Make America Great Again”) are highly ranked in the first two clusters. In later weeks, less clearly pro-Trump terms appear: drumpf reminds of the unattractive root of the Trump family name, while impeached and mueller show the Russia probe in 2017 and 2018 were significant in the trump-dense threads of that time. This change might thus hint at growing scepticism towards Trump after his win, but it is not shown how these terms are used. Fortunately, the scatterplot offers a rudder with which to navigate to further perspectives.

In keeping with Latour’s advice to keep “aggregate structures” and “local contexts” flat (165-72), I contrast the above scatterplot with a perspective on the data that keeps sentence structures intact instead of showing abstracted keyword sets. Figure 4 uses all posts mentioning trump in the median weeks of the first and last clusters in the scatterplot (indicated with ★) and visualises word trees (Wattenberg and Viégas) of most frequent words following “trump is a”. As such, they render explicit ontological associations about Trump; what is Trump, according to /pol/-anons? The first word tree shows posts from 2-8 November 2015, when fifteen Republican competitors were still in the race. As we have seen in Figure 1, Trump was in this month still “only” mentioned in around 50,000 posts (2% of the total). This word tree suggests his eventual nomination was at this point seen as an unlikely and even undesirable scenario, showing derogatory associations like retard and failure, as well as more conspiratorial words like shill, fraud, hillary plant, and hillary clinton puppet. Notably, the most prominent association, meme, and others like joke and fucking comic relief, imply Trump was not taken too seriously (see also Figure 5).


Figure 4: Word trees of words following “trump is a” in the median weeks of the first and last clusters of the scatterplot. Made with Jason Davies’s Word Tree application.


Figure 5: Anons who did not take Trump seriously. Screencapture taken from archive.4plebs.org (see post 1 and post 2 in context).

The first word tree contrast dramatically with the one from the last median week from 18 to 24 December 2017. Here, most associations are anti-Semitic or otherwise related to Judaism, with trump most prominently related to the hate speech term kike. This prompts several questions: did /pol/ become increasingly anti-Semitic? Did already active users radicalise, or were more anti-Semites drawn to /pol/? Or was this nefarious current always there, with Trump merely drawing anti-Semitic attention after he won the election? Although the navigation did not depart from a particular critical framework, by “just following the actors” (Venturini), it already stumbled upon important questions related to popular narratives on 4chan and the alt-right. While it is tempting to stop here and explain the change as “radicalisation”, the navigation should continue to add more empirical perspectives. When doing so, the more plausible explanation is that the unlikely success of Trump briefly attracted (relatively) more diverse and playful visitors to /pol/, obscuring the presence and steady growth of overt extremists in the process.

To unpack this, I first focus on the claim that a (relatively) diverse set of users flocked to /pol/ because of the Trump campaign. /pol/’s overall posting activity rose sharply during the 2016 election, which can point to already active users becoming more active, but is likely mostly caused by new users flocking to /pol/. Indeed, this can be traced in actor language. For instance, many anons professed to be “reporting in” from other 4chan boards during crucial moments in the campaing. One of the longest threads in the trump-dense threads dataset (4,504 posts) simply announces “Cruz drops out”. In the comments below, multiple anons state they arrived from other boards to join the Trump-infused activity. For instance, Figure 6 shows an anon replying “/v/ REPORTING IN”, to which sixty other users reacted by similarly affirming themselves as representatives from other boards (e.g. “/mu/ here. Ready to MAGA”). While but another particular view, this implies Trump’s surprising nomination stimulated a crowd-like gathering of different anons jumping into the vortex of trump-related activity on /pol/.


Figure 6: Replies by outside-anons “reporting in” the sticky thread announcing Ted Cruz's drop out, 4 May 2016. Screenshots taken from 4plebs.org (see post 1 and post 2 in context).

Other actor-language further expresses Trump’s campaign “drew in” new and unadjusted (or: less extreme) users. Notably, many anons claimed the 2016 election led to an “invasion of Reddit users”. Figure 7 shows one such expression: an annotated timeline of /pol/’s posting activity graph (made by 4plebs), posted to /pol/ on 26 February 2016 and subsequently reposted 34 times. It interprets 2016 as a period where “Trump shit goes into overdrive, meme shit floods /pol/, /pol/ is now reddit”. Whether these claims hold any truth is difficult to establish, but the image forms an interesting case of how the entirety “/pol/” is imagined and locally articulated. Such simplistic narratives relate to what Latour calls “panoramas”: totalising notions of some imagined “whole” (188-90) that, while not to be “confused with the collective”, form crucial data since they express how actors understand their own composition (190). Especially in the volatile conditions of anonymous and ephemeral imageboards, repeated panoramic narratives can help in constructing a sense of cohesion–and thereby also form interesting actors to trace. Indeed, following the panoramic statement “/pol/ is now reddit”, other gatekeeping-efforts are not hard to find. For instance, phrases urging other anons to go “back to reddit” (occurring in 19,069 posts in the total dataset) or “back to The_Donald” (a popular pro-Trump subreddit, 1,940 posts) are also particularly popular in the dataset.


Figure 7: An image circulated on /pol/ lamenting that "/pol/ is now reddit" by annotating 4plebs’s posting metrics. Screenshot taken from archive.4plebs.org (see posts).

Did trump-related activity on /pol/ indeed become more “meme-y” or “Reddit-like” during the election cycle, as the above panorama articulates? The activity in the trump-dense threads seems to suggest so. Figure 8 again uses the tf-idf terms from these threads, but here with the columns denoting the weeks and the rows the top scoring tf-idf terms of their respective week. To highlight relevant actors, all terms are greyed out (see the unedited sheet here), except for several keywords that indicate particularly playful or memetic vernacular: the aforementioned stump, emperor, referring to Trump’s nickname as “God Emperor”; energy, referring to “high energy”, a common catchphrase amongst Trump supporters; magic, referring to “meme magic”, the faux-ironic belief that posting memes affects real-life events; and pepe, the infamous cartoon frog. In both the tf-idf ranking and the absolute frequencies, these keywords flourish in 2016, but disappear soon after the presidential election passes. The later weeks in 2017 and 2018 rarely contain similarly playful and memetic terms, and if they do, suggest mocking discourse regarding Trump (e.g. drumpf). This perspective thus pictures the environment around trump in the run-up to the election as a particularly memetic yet short-lived carnival. At least from this perspective, “meme shit” thus indeed seemed to have “flooded /pol/”, but only for a short while.


Figure 8: tf-idf matrix of trump-dense threads, columns denoting weeks and rows denoting the top hundred most relevant terms per week. Download the full tf-idf matrix with all terms here.

Despite this carnivalesque activity, further perspectives suggest it did not go at the expense of extremist activity on /pol/. Figure 9 shows the absolute and relative counts of the word "jew" and its derogatory synonym "kike". Each of these increases from 2015 onwards. As such, it seems to align with claims that Trump’s success and /pol/ becoming increasingly extremist were causally related (Thompson). However, apart from possibly confusing correlation with causation, the relative presence remains fairly stable, even slightly decreasing during the frenzy of the Trump campaign. Since we also saw Trump himself become a target for anti-Semitic activity, these trendlines rather imply /pol/’s extremist current grew proportionally to the overall increase in activity, and increased alongside but not but necessarily as a partisan contingent as a result of Trump’s campaign.


Figure 9: The absolute and relative frequency of the terms "jew" and "kike" on 4chan/pol/.


Combined, the above navigation implies two main changes in 4chan/pol/’s trump-related current. First, the climaxes of the 2016 Republican primaries and presidential elections seem to have invoked crowd-like influxes of (relatively) heterogeneous users joining the Trump-delirium, marked by particularly memetic activity. Second, /pol/ additionally seemed to have formed a welcoming hotbed for anti-Semites and other extremists, as the absolute amount of (anti-Semitic) hate speech increased. However, while already-present and new users might have been energised by Trump, they were not necessarily loyal to him, as professed by the fact that Trump himself eventually became a target. Together with the fact that anti-Semitic hate speech stayed relatively consistent, instead of being “countercultural” (Nagle) or exclusively pro-Trump, /pol/ thus seems to have been composed of quite a stable anti-Semitic and Trump-critical contingent, increasing proportionally to /pol/’s general growth.

Methodologically, this text sought to demonstrate how a brief navigation of trump on 4chan/pol/ can provide provisional yet valuable insights regarding continuously changing current of online anonymous collectives. As the cliché goes, however, this brief exploration has left more many questions, or rather, it did not “deploy the content with all its connections” (Latour 147). For instance, I have not touched on how many of the trump-dense threads are distinctly separated and pro-Trump “general threads” (Jokubauskaitė and Peeters). Considering the vastness of such tasks, the necessity remains to find appropriate ways to “accurately map” the wild currents of the dissimulative Web–despite how muddy they might get.


This text is a compressed and edited version of a longer MA thesis available here.


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Author Biography

Sal Hagen, University of Amsterdam

Sal Hagen is a PhD candidate at the University of Amsterdam, Department of Media Studies. He co-founded OILab during his MA in Media Studies and has studied Internet subcultures since. His work combines cultural research and media theory with digital and computational methods.