The Borderer Signal: Separating Meme Style from Ideology

Executive summary Link to heading

This framework turns a cultural intuition into a falsifiable content-analysis design. The proposed intuition is that some internet meme communities—especially some right-wing or reactionary communities—may disproportionately exhibit a borderer-like communicative style characterized by low deference, antagonistic humor, taboo-breaking, anti-elitism, ironic distance, status inversion, performative aggression, and dense in-group signaling.

The inherited framework could not validly test that claim because it defined the categories in advance as moral opposites: “right-coded” memes were coded as aggressive, callous, crude, anonymous, and out-group oriented, while “left-coded” memes were coded as empathetic, vulnerable, polished, identifiable, and sincere. A classifier built from those definitions could only rediscover its own assumptions.

Existing evidence directly contradicts such a clean binary. A study of 366 popular r/DankLeft memes found left-wing taboo-breaking, threatening out-group construction, and some representations of political violence. Merrill, Gardell, and Lindgren A study of 636 Spanish political memes found confrontation and disqualification in a cross-ideological political-meme environment. Paz, Mayagoitia-Soria, and González-Aguilar At the same time, far-right-specific studies show that aggressive humor, anti-elitism, hate speech, conspiratorial narratives, and humorous ideological packaging can be important in particular right-wing corpora. Schmid et al. Cross-national research also finds that memes are not equally central to all far-right organizations. McSwiney et al.

The redesigned study therefore separates source ideology from meme style.

• Ideology is assigned independently from the meme’s stylistic features, using source-community, account, organization, or external contextual evidence.
• Meme style is coded using neutral observable dimensions such as target orientation, hostility, taboo-breaking, irony, anti-elitism, status inversion, in-group signaling, self-disclosure, care/solidarity, and aesthetic register.
• Platform, topic, format, time, source identity, and engagement are treated as contextual variables or controls rather than covert ideological labels.
• The “borderer” construct is tested for internal coherence and predictive validity. If its proposed dimensions do not cluster, or if any ideological association disappears outside the platforms or communities used to construct it, the construct should be weakened or rejected.

This design can discover asymmetry if it exists without presupposing it.

1. Research question Link to heading

The primary research question is:

Do independently identified political meme communities differ systematically in a reproducible bundle of low-deference, antagonistic, taboo-breaking, anti-elite, ironic, status-inverting, and in-group-signaling features after platform, topic, format, time, and community effects are taken into account?

A secondary question asks whether those features form a coherent style at all:

Do the proposed dimensions covary strongly enough to justify a latent “borderer-style” construct, or are they better treated as separate meme practices?

This order matters. The study should not assume that “borderer” exists as one stable thing merely because the dimensions sound conceptually related.

A third question concerns consequences:

When a meme exhibits antagonistic or borderer-like features, is it associated with greater engagement, diffusion, polarization, or other outcomes, and do those associations differ by political community?

The consequences question should remain separate from the classification question. Engagement, virality, or political effect must not be used to define the style whose effects are later measured.

2. Conceptual architecture: keep ideology and style separate Link to heading

Limor Shifman’s influential account distinguishes meme content, form, and stance. Shifman, Memes in Digital Culture That distinction suggests a clean architecture for this study.

2.1 Source ideology Link to heading

Ideology is a contextual variable assigned independently from stylistic coding. Possible categories depend on the corpus but might include:

• far left;
• left;
• center-left;
• center/mixed;
• center-right;
• right;
• far right;
• libertarian or anti-statist where substantively distinct;
• apolitical/unclear.

The important methodological rule is that a meme must not be labeled right-wing because it is aggressive, ironic, anonymous, crude, masculine, anti-elite, or hostile. Those are variables to be tested.

Likewise, a meme must not be labeled left-wing because it is sincere, caring, vulnerable, polished, feminine, or associated with a particular mainstream platform.

Ideological assignment should instead come from evidence external to the style code: the declared identity of a community or organization; the sustained political orientation of a source account; human contextual coding based on manifest political content; or a pre-established source list with documented inclusion criteria.

For ambiguous memes, ideology can be coded as unknown rather than forced.

2.2 Meme content Link to heading

Content variables describe what the meme is about:

• named political actor;
• political party or movement;
• economic issue;
• race/ethnicity/nationality;
• gender/sexuality;
• religion;
• immigration;
• war/foreign policy;
• institutions or experts;
• class/capital/labor;
• environment;
• personal life/mental health;
• nonpolitical everyday life;
• other topic.

Topic must be controlled because a meme about war will naturally differ in hostility from a meme about public transit even within the same ideology.

2.3 Meme form Link to heading

Form variables include:

• static image macro;
• screenshot;
• comic/panel sequence;
• photograph with text;
• short video;
• GIF/animation;
• text-only post functioning memetically;
• heavily remixed/deep-fried image;
• template reuse versus novel composition.

Aesthetic roughness is coded here as a stylistic property, not as a political identity.

2.4 Meme stance and style Link to heading

The proposed borderer hypothesis lives mainly at the stance/style level. These variables should be coded independently and, where possible, ordinally rather than forced into two ideological poles.

3. Core coding dimensions Link to heading

3.1 Target orientation Link to heading

Record the primary target:

• none;
• self;
• in-group member;
• political out-group;
• named individual opponent;
• elite/institution;
• broad social category;
• mixed/unclear.

A separate variable records whether the target is represented as high status, low status, threatening, contemptible, incompetent, immoral, ridiculous, or dangerous.

This prevents “attacks an out-group” from becoming synonymous with “right-wing.” Merrill, Gardell, and Lindgren’s r/DankLeft study demonstrates why that assumption fails. Merrill, Gardell, and Lindgren

3.2 Hostility Link to heading

Suggested ordinal scale:

0. no hostility;
1. mild teasing or criticism;
2. ridicule/contempt;
3. dehumanization or severe derogation;
4. implied threat or celebration of harm;
5. explicit threat or endorsement of violence.

Coders should judge the artifact rather than the presumed virtue or vice of the target.

3.3 Humor and ridicule Link to heading

Code humor type separately:

• no apparent humor;
• affiliative/playful;
• self-deprecating;
• parody;
• satire;
• sarcasm;
• aggressive/ridiculing;
• dark/taboo humor;
• absurdist/nonsequitur;
• mixed.

Schmid et al. distinguish aggressive humor from other humor within a far-right Telegram corpus, illustrating why “humorous” and “aggressive” should not be collapsed. Schmid et al.

3.4 Taboo-breaking Link to heading

Code the presence and type of norm violation:

• profanity;
• sexual taboo;
• racial/ethnic taboo;
• religious sacrilege;
• gore or bodily disgust;
• threat/violence;
• suicide/death taboo;
• misogynistic or misandrist taboo;
• other deliberate decorum violation.

The coding should identify the practice before assigning its politics. Left-wing taboo-breaking is empirically documented. Merrill, Gardell, and Lindgren

3.5 Irony and commitment ambiguity Link to heading

Suggested categories:

• literal/sincere;
• humorous but literal claim;
• obvious parody;
• obvious irony;
• layered quotation/remix;
• ambiguous sincerity;
• explicit distancing such as “just joking” or equivalent;
• indeterminate.

Irony should not be inferred merely from the meme’s ideology or platform.

3.6 Anti-elitism Link to heading

Record:

• absent/present;
• target: political office, bureaucracy, media, academia, business/wealth, cultural celebrity, party leadership, religious authority, movement leadership, other;
• frame: corrupt, hypocritical, incompetent, alien/out-of-touch, exploitative, censorious, weak/effeminate, oppressive, other.

This permits comparison of left and right anti-elitism rather than treating only one side as anti-elite.

3.7 Status inversion Link to heading

Status inversion occurs when the meme symbolically lowers a prestigious or authoritative target or elevates an outsider, ordinary person, stigmatized identity, dissident, troll, worker, “common man,” or other lower-status figure.

Code direction:

• none;
• high-status target lowered;
• low-status/in-group figure elevated;
• both.

3.8 Performative aggression Link to heading

Record communication that displays toughness, domination, threat, willingness to offend, or pleasure in confrontation even when no literal violence is endorsed.

This should be distinguished from ordinary criticism and from aggressive humor.

3.9 In-group signaling and intertextual density Link to heading

Code the degree to which understanding requires:

• platform-specific slang;
• ideological jargon;
• template literacy;
• named movement symbols;
• historical references;
• layered references to previous memes;
• coded or euphemistic terminology.

A high score indicates subcultural density, not an ideology.

3.10 Self-disclosure and vulnerability Link to heading

Record whether the meme expresses:

• personal weakness or anxiety;
• embarrassment;
• loneliness;
• economic hardship;
• identity insecurity;
• self-mockery;
• confession or emotional disclosure.

This variable is included because the inherited framework associated vulnerability with the left. The present design treats that claim as testable.

3.11 Care and solidarity Link to heading

Record explicit affiliative content:

• reassurance;
• mutual aid;
• empathy for another person/group;
• collective hope;
• protection of an in-group;
• universalistic care;
• no care/solidarity content.

Care toward an in-group and hostility toward an out-group can coexist in the same meme. Coding both prevents a false one-dimensional morality scale.

3.12 Aesthetic register Link to heading

Rather than “right = crude / left = polished,” code observable form:

• ordinary reused template;
• intentionally rough or degraded;
• technically polished;
• minimalist;
• maximalist/collage;
• nostalgic/retro;
• photographic/commemorative;
• corporate/professional mimicry;
• other.

Trillò and Shifman’s Italian far-right commemorative memes are an explicit reason not to treat roughness as a necessary right-wing feature. Trillò and Shifman

4. Corpus design Link to heading

4.1 Sampling frame Link to heading

The study should sample communities, platforms, topics, and time periods, not simply collect memorable examples.

A defensible design might use four ideological strata—left, right, far right, and apolitical/mixed—across several platforms or community types. The exact labels depend on available sources. Each ideological stratum should appear on more than one platform whenever possible.

A schematic balanced design:

The purpose is not mathematical symmetry for its own sake. It is to prevent ideology from being perfectly confounded with platform, age, or topic.

4.2 Do not select only popular memes Link to heading

Selecting only the most popular memes creates an engagement-conditioned sample. If shock or hostility affects engagement, then sampling on engagement can make the style appear more common than it is.

A better strategy is to sample from defined posting windows and either:

• randomly sample eligible memes regardless of engagement; or
• stratify by engagement bands and preserve the sampling weights.

Engagement can then be analyzed as an outcome.

4.3 Template duplication Link to heading

Memes are deliberately repetitive. The same image template or near-identical artifact may appear many times. Random train/test splits can therefore leak almost identical content across validation sets and exaggerate predictive performance.

The corpus should assign template or near-duplicate families where feasible and ensure that held-out validation does not place trivial variants of the same meme in both training and test sets.

4.4 Negative controls Link to heading

Include apolitical or weakly political meme communities. If the proposed borderer dimensions also strongly characterize ordinary gaming, sports, fandom, or absurdist meme culture, then a supposed ideological association may actually be a general internet-subculture style.

Negative controls are therefore central to the theory rather than decorative.

5. Independent ideological labeling Link to heading

Source ideology should be determined before and separately from stylistic coding.

Possible procedures:

1. Community-level classification: select communities with explicit political self-identification and document inclusion rules.
2. Account-level classification: classify sustained source-account orientation from external context rather than the focal meme’s tone.
3. Artifact-level political content: where community identity is mixed, a separate coder team can classify manifest ideological position using a different codebook that does not contain the borderer-style variables.
4. Unknown category: retain genuinely ambiguous cases instead of forcing a label.

The style coders should be blinded to source identity where technically feasible. Blinding will never be perfect because political content can reveal ideology, but hiding usernames, platform chrome, and community metadata reduces avoidable expectancy effects.

6. Coder training and reliability Link to heading

The codebook should contain:

• explicit definitions;
• positive examples;
• negative examples;
• boundary cases;
• rules for mixed or ambiguous artifacts;
• rules for multimodal conflicts, such as sincere text over an ironic image;
• a procedure for adjudication after independent coding.

A pilot sample should be independently coded before the full corpus. Variables with weak reliability should be rewritten or removed rather than retained because they are theoretically attractive.

For two coders and simple nominal variables, Cohen’s kappa is one option. For multiple coders, missing data, or ordinal variables, Krippendorff’s alpha is often more flexible. The study should report reliability for each important variable, not one global number that hides unreliable dimensions.

Reliability is not validity. Two coders can consistently apply a bad construct. The subsequent validation steps test whether the resulting dimensions actually cohere and generalize.

7. Analysis plan Link to heading

7.1 Descriptive analysis first Link to heading

Before constructing a borderer score, report distributions by ideology, platform, community, topic, and format.

Examples:

• proportion of memes with out-group targets;
• hostility distributions;
• prevalence of anti-elitism;
• types of humor;
• taboo categories;
• self-disclosure and care/solidarity rates;
• aesthetic-register distributions.

This can reveal whether the proposed bundle is plausible without forcing all variables into one index.

7.2 Does a latent borderer style exist? Link to heading

Use exploratory factor analysis, principal-components analysis, latent-class analysis, or another appropriate dimensional method depending on measurement levels and sample size.

The aim is not to maximize a single factor at all costs. Possible outcomes include:

• one coherent borderer-like dimension;
• several distinct dimensions, such as antagonism, anti-elitism, and subcultural irony;
• no stable factor structure.

The third result is a legitimate falsification outcome.

If a factor structure is discovered in one subset, confirm it in another subset or preregister a confirmatory model for a replication corpus.

7.3 Does style predict ideology? Link to heading

Only after the style variables or factors are defined should the study model ideology.

Possible models include multinomial logistic regression or other interpretable classifiers. Predictors can include style dimensions while controlling for:

• platform;
• source community;
• topic;
• format;
• posting period;
• template family;
• source identifiability;
• language/country where relevant.

The important output is not merely statistical significance but effect size, uncertainty, overlap between ideological distributions, and out-of-sample performance.

7.4 Out-of-domain validation Link to heading

A model that distinguishes a left subreddit from a far-right Telegram channel may only have learned Reddit versus Telegram.

Validation should therefore include at least one difficult split:

• train on some platforms, test on a held-out platform;
• train on some communities, test on unseen communities;
• train on one time period, test on a later one;
• where possible, train in one national context and test another.

A borderer-right theory that works only on the original communities is a community classifier, not a robust ideological-style theory.

7.5 Engagement analysis Link to heading

Engagement should be analyzed within platform because likes, shares, replies, views, and upvotes are not equivalent measures.

A useful question is whether antagonism, taboo, irony, or ideological-humor interactions predict relative engagement within the same platform and community context.

Schmid et al.’s finding that humorous far-right narratives achieved greater reach in German-language Telegram channels provides a concrete replication target rather than a universal assumption. Schmid et al.

8. Preregistered hypotheses Link to heading

The study should distinguish hypotheses from expected truths.

H1 — Antagonistic-style association Link to heading

Memes from independently coded right and far-right sources will, on average, score higher on some antagonistic dimensions—such as aggressive humor, anti-elitism, or ironic distancing—than memes from independently coded left sources.

Falsification: no meaningful between-group difference, or the difference disappears after controls.

H2 — Cross-ideological overlap Link to heading

All major ideological strata will contain nontrivial levels of out-group targeting, ridicule, taboo-breaking, and anti-elitism.

This hypothesis is motivated by left-wing and cross-ideological meme research. Merrill, Gardell, and Lindgren; Paz et al.

H3 — Context matters substantially Link to heading

Platform, community, topic, and format will explain a substantial share of stylistic variation independent of ideology.

Falsification: ideological effects remain stable and large across those contexts.

H4 — Far-right humor/reach interaction Link to heading

Within far-right corpora comparable to the Telegram environment studied by Schmid et al., memes combining explicit ideological narratives with humor will receive greater reach than comparable non-humorous ideological memes.

External-validity test: determine whether the interaction reproduces on other platforms and national contexts.

H5 — Vulnerability/care is not left-exclusive Link to heading

Self-disclosure and care/solidarity will not map cleanly onto left ideology; right-wing and apolitical communities will also display these practices.

This directly tests rather than assumes the inherited “left = empathy/vulnerability” claim.

H6 — Aesthetic roughness is context-dependent Link to heading

Intentionally rough or degraded aesthetics will be more strongly associated with some platform/template families than with ideology itself.

Trillò and Shifman’s far-right commemorative genre provides a reason to expect substantial right-wing aesthetic heterogeneity. Trillò and Shifman

9. Political-effects extension Link to heading

A content analysis cannot by itself show that a style persuades, radicalizes, erodes trust, or produces intolerance.

If effects are part of the research program, they require a separate design.

Galipeau’s longitudinal Facebook experiment found generally limited average effects but evidence of backlash/polarization from out-group memes, especially among strong identifiers. Galipeau Masood, Tuzov, and Skoric’s two-wave Hong Kong panel study found political meme use predicted political intolerance. Masood, Tuzov, and Skoric

A follow-up experiment could therefore manipulate independently:

• source ideology;
• target direction;
• humor versus literal presentation;
• hostility level;
• ironic ambiguity;
• in-group versus out-group target.

Outcomes might include affect toward groups, perceived social distance, policy attitude, willingness to share, perceived humor, perceived offensiveness, and perceived sincerity.

This is methodologically cleaner than inferring effects from which memes happen to receive engagement.

10. Ethical and archival considerations Link to heading

Political meme corpora can contain slurs, threats, sexual content, identifiable private individuals, and extremist symbols. A public research archive should therefore distinguish between coding data and redistribution of source media.

Recommended practice includes:

• record durable source identifiers and collection metadata where lawful and ethically appropriate;
• avoid republishing unnecessary copyrighted or personally identifying images;
• preserve analytic descriptions when redistribution rights are uncertain;
• document deletion or account-loss risks;
• avoid reproducing hateful content merely for decoration;
• define procedures for potentially illegal or platform-restricted material;
• report how removed/deleted posts affect the corpus.

These concerns are one reason the converted illustrative images from the inherited framework should not form part of the final research artifact. They were not necessary to the method and lacked recorded publication provenance.

11. Decision rules for the “borderer” construct Link to heading

The framework should state in advance what would count as success, revision, or failure.

Retain a borderer-style construct if Link to heading

• multiple proposed dimensions show acceptable coding reliability;
• they form a reasonably stable latent dimension or reproducible cluster;
• the structure replicates in held-out data;
• the factor is meaningfully associated with independently coded political communities after controls;
• the association generalizes beyond the exact source communities used to build the measure.

Split the construct if Link to heading

• antagonism, anti-elitism, irony, and in-group signaling form distinct factors with different political relationships.

In that case, “borderer” may be a useful family resemblance but not a single quantitative score.

Reject the ideological borderer-right claim if Link to heading

• style dimensions do not cluster;
• ideological differences are small relative to within-group variance;
• platform/community/topic controls eliminate the association;
• left/right/apolitical distributions overlap heavily;
• out-of-platform prediction collapses;
• the classifier succeeds mainly because it learned slang, source markers, or platform artifacts.

A result that rejects the hypothesis is not a failed study. It is the principal reason to redesign the framework this way.

12. Recommended reporting table Link to heading

The final empirical paper should report claims at the level the design actually supports.

Conclusion Link to heading

The original two-category framework was not a neutral test because ideology and moral style were defined together. It would have labeled a hostile left meme as anomalous and a caring right meme as anomalous by construction, then reported the construction as an empirical pattern.

The revised framework removes that circularity. Ideology is contextual and independently labeled; style is multidimensional and neutrally coded; platform and topic are controls; engagement is an outcome; and the borderer construct must demonstrate coherence and cross-context validity before it is treated as real.

This makes the central hypothesis genuinely interesting. If right-wing meme environments do exhibit a reproducible low-deference, antagonistic, taboo-breaking, status-inverting style after all of those controls, the result will be substantially stronger than the inherited intuitive classification. If they do not, the study will show which parts of the intuition actually belong to internet subculture, platform ecology, political antagonism generally, or particular communities rather than to the right as such.

Sources Link to heading

• Galipeau, Thomas. “The Impact of Political Memes: a Longitudinal Field Experiment.” Journal of Information Technology & Politics 20, no. 4 (2023): 437–453. https://doi.org/10.1080/19331681.2022.2150737
• Masood, Muhammad, Viktor Tuzov, and Marko Skoric. “Political Meme Use Can Lead to Political Intolerance: Evidence from a Panel Study.” International Journal of Public Opinion Research 36, no. 4 (2024): edae052. https://doi.org/10.1093/ijpor/edae052
• McSwiney, Jordan, Michael Vaughan, Annett Heft, and Matthias Hoffmann. “Sharing the hate? Memes and transnationality in the far right’s digital visual culture.” Information, Communication & Society 24, no. 16 (2021): 2502–2521. https://doi.org/10.1080/1369118X.2021.1961006
• Merrill, Samuel, Mattias Gardell, and Simon Lindgren. “How ‘the left’ meme: Analyzing taboo in the Internet memes of r/DankLeft.” New Media & Society 27, no. 7 (2025): 3950–3972; first published online 2024. https://doi.org/10.1177/14614448241232144
• Paz, María Antonia, Ana Mayagoitia-Soria, and Juan-Manuel González-Aguilar. “From Polarization to Hate: Portrait of the Spanish Political Meme.” Social Media + Society 7, no. 4 (2021). https://doi.org/10.1177/20563051211062920
• Schmid, Ursula K., et al. “Memes, humor, and the far right’s strategic mainstreaming.” Information, Communication & Society (2024). https://doi.org/10.1080/1369118X.2024.2329610
• Shifman, Limor. Memes in Digital Culture. MIT Press, 2014. https://mitpress.mit.edu/9780262525435/memes-in-digital-culture/
• Trillò, Tommaso, and Limor Shifman. “Memetic commemorations: remixing far-right values in digital spheres.” Information, Communication & Society 24, no. 16 (2021): 2482–2501. https://doi.org/10.1080/1369118X.2021.1974516