Distinguishing “Borderer/Right‑coded” vs “Left/Awkward‑Authentic” Internet Memes: A Research Framework

Executive Summary Link to heading

Internet memes are multimodal artifacts – text, images, videos – that spread virally online\[1\]\[2\]. They often convey humor, satire or social commentary and shape political discourse\[1\]\[2\]. We propose a schema classifying memes as “borderer/right‑coded” versus “left/awkward‑authentic‑coded.” In this schema, borderer-coded memes emphasize mockery of out‑groups, ironic or callous humor, conspiratorial or nationalist themes, and low production values\[3\]\[4\]. By contrast, left/awkward-authentic-coded memes emphasize sincerity, vulnerability, empathy and collective hope or absurd earnestness\[5\]\[6\]. For example, right‑wing memes use humor “as a Trojan horse” to polarize and bond insiders against “the other” (establishment, progressives, minorities)\[3\]\[4\], whereas many Gen‑Z “cringe” memes embrace vulnerability and authenticity as positive traits\[5\]\[6\].

This report proposes operational definitions for these two meme types (Section 1), a rigorous content analysis design (Section 2), a coding rubric mapping features to labels (Appendix A), and illustrative examples (Appendix B). We describe a hypothetical corpus study of memes across platforms (Twitter, Reddit, TikTok, 4chan, etc.), with human coders classifying each meme per the rubric. We anticipate that borderer-coded memes will systematically score higher on mockery, aggression, and in-group humor, while left-coded memes will score higher on sincerity, relatability, and vulnerability\[3\]\[5\]. We discuss limitations (e.g. cultural variation, fluid identities) and implications: such coding could reveal how online humor reinforces political identities and affects polarization\[4\]\[5\].

1. Operational Definitions and Features Link to heading

• Internet Memes: We follow Shifman’s definition: a meme is a digital item (image, video, text) sharing common content, form, or stance, circulated and remixed by users\[1\]. Memes are inherently multi-modal, easily shared, and often humorous or satirical\[2\].

• Borderer/Right‐coded Memes: These are memes whose content and style reflect far‑right or reactionary frames. Measurable features include:

• Emotional Tone: Mocking, contemptuous, aggressive or humorous at others’ expense\[3\]\[4\].

• Targets: Frequently stigmatize outgroups or authority figures (e.g. minorities, elites, activists)\[3\]\[4\].

• Humor Type: Irony, dark humor, ridicule or conspiracy (often self‑referential “lulz”)\[3\]\[7\].

• Aesthetics: Deliberately rough/“degenerative” (pixelation, crude editing, meme templates), reflecting a “studied unseriousness”\[8\].

• Gender Coding: Often hyper‑masculine or aggressive tropes; animalistic or troll-like imagery common\[8\].

• Platform/Origin: Arise on fringe imageboards (4chan, 8kun), Telegram channels, alt‑right Reddit/Twitter users\[3\]\[7\].

• Anonymity: Often posted anonymously or by pseudonymous accounts to avoid accountability.

• Virality Mechanics: Spread via shock value, insider networks, or coordinated reposts (e.g. memes from far‑right farms)\[3\]\[7\].

• Language/Intertextuality: Uses coded slang, memespeak, acronyms, obscure references; heavily iterative inside-jokes\[3\]\[7\].

• Irony Level: High – content is often claimed “just a joke”\[9\].

• Callousness: High; mockery and dehumanization are typical\[8\].

• Left/Awkward‑Authentic Memes: These memes emphasize sincerity, emotionality or awkward humor. Features include:

• Emotional Tone: Earnest, optimistic or self‑deprecating; can express vulnerability, silliness, empathy\[5\]\[6\].

• Targets: Critique systemic issues or express self-reflection; rarely dehumanizes individuals. Focus may be on social justice (e.g. climate, inequality) rather than mocking individuals. Left‑leaning meme communities often discuss threats to shared values (e.g. democracy, environment)\[10\]\[6\].

• Humor Type: Awkward/“cringe” humor, relatable awkward scenarios, or hopeful absurdity\[5\]\[6\]. Satire can exist but tends to empathize rather than scorn.

• Aesthetics: Often clean, brightly colored, or nostalgic (retro/1980s feel, mainstream movie stills) – designed to appear authentic or meme‑savvy but less gritty.

• Gender Coding: More feminine or diverse imagery; for example, memes featuring female or non‑binary protagonists and gentle humor.

• Platform/Origin: Common on TikTok, Instagram, Tumblr, mainstream Twitter – platforms favoring personal identity. Many popular “cringe” TikToks show individuals performing sincerity.\[5\]

• Anonymity: Often created by identifiable personalities or small influencers (people aren’t hiding behind masks).

• Virality Mechanics: Shared organically for relatability (e.g. “share if you agree”), or through mainstream pages; not primarily shock-driven.

• Language/Intertextuality: Direct, meme slang can appear but usually with heartfelt text. References to pop culture (family movies, old songs) are common, but used earnestly.

• Irony Level: Lower; often explicitly ironic, but the point is sincerity or self-awareness (as in “I’m so cringey and proud”)\[5\].

• Vulnerability: High; shows self‑critique, emotional connection (“I am cringe, I don’t care” movement)\[5\]\[6\].

These features are summarized in the coding rubric (Appendix A). For example, Fernandez Salguero (2025) notes that far‑right memes leverage group bonding through mockery (“lulz”) of outsiders\[3\]\[4\], whereas “cringe” Gen‑Z content deliberately expresses vulnerability as an antidote to internet nihilism\[5\]\[6\]. This suggests measurable dimensions (mockery vs sincerity, callousness vs care) that coders can rate for each meme.

2. Research Design Link to heading

• Corpus Selection: We would compile a diverse meme corpus spanning platforms (Twitter/X, Reddit subcommunities, TikTok, Instagram, 4chan/8kun, Telegram, etc.) and one or more recent years (e.g. 2024–25). No single constraint, but ensuring representation of both major political orientations. For example, left‑aligned memes from subs like r/DankLeft or TikTok hashtags (#LeftMemes, #WholesomeMemes) and right‑aligned memes from alt‑right boards or hashtags (e.g. #RedPill, #ConservativeMemes). Aim for a large sample (e.g. 500–1000 per category) to support both qualitative depth and quantitative analysis.

• Sampling Method: Use stratified sampling: first identify platforms/communities associated with each ideology (e.g. /pol/ vs r/DankLeft), then randomly sample posts or images labeled as “memes” within each. Supplement with trending memes from general sites (KnowYourMeme, MemeEconomy) tagged by users as political. If necessary, use snowball sampling via share counts or search queries.

• Annotation Protocol: Develop a detailed codebook (Appendix A) with definitions of each feature. Train coders on examples. For each meme in the sample, coders record: (1) which category it belongs to (borderer-coded vs left-coded) per rubric criteria, and (2) scores or flags for each feature dimension (e.g. Tone:

  \[Callous, Neutral, Warm\]

  , Humor:

  \[Irony, Sincerity\]

  , Target type, etc.). Provide rubric with concrete indicators (e.g. “mocking others” vs “self-deprecating”). Coders should label independently. Use at least two coders per item to assess reliability.

• Coder Reliability: Calculate inter-rater reliability (Cohen’s κ or Krippendorff’s α) for the categorical label (borderer vs left) and for key binary features (e.g. high mockery vs not). Disagreements can be resolved by discussion or a third coder. Aim for κ≥0.7.

• Quantitative Analysis: Compute frequencies of each feature within borderer- vs left-coded sets. Use statistical tests (chi-square) to see which features significantly differentiate the labels. One could build a logistic regression or classification model: e.g. predict “borderer-coded” from feature scores. Multivariate patterns (e.g. cluster analysis or principal components) might reveal subtypes.

• Qualitative Analysis: Perform thematic analysis on meme texts/images. Identify recurring themes (e.g. nationalist pride vs climate anxiety). Discourse analysis might examine how language frames “the other” or self. We could present exemplar memes for each type (Appendix B) with interpretive notes.

• Ethical Considerations: Social media content may include hateful or extremist imagery. Obtain IRB review: ensure coders know how to handle disturbing content (e.g. allow opt-out, provide debriefing). If humans appear in memes, avoid identifying them. Store only public data (not user IDs). Anonymize any references. The research aims to analyze political expression without amplifying hate. Note biases: our own political views or coder identity can color coding, so use neutral language, multiple coders, and clear guidelines.

flowchart TD
    Corpus[Corpus Selection] --> Sampling[Sampling Method]
    Sampling --> Annotation[Annotation Protocol & Codebook]
    Annotation --> Reliability[Coder Reliability Checks]
    Reliability --> QuantAnalysis[Quantitative Analysis]
    Reliability --> QualAnalysis[Qualitative Analysis]
    QuantAnalysis --> Results[Expected Results]
    QualAnalysis --> Results
    Results --> Discussion[Discussion/Implications]

3. Expected Results Link to heading

Based on our schema and related research, we predict the following patterns (hypothetical):

• Feature Patterns: Borderer-coded memes will score high on mockery, negativity, and group-based humor. For instance, coders will note sarcastic captions, use of ethnic slurs or extremist symbols, or mocking tone (features noted by Bricker as “affective engines” for far-right in-group bonding\[3\]\[4\]). Left-coded memes will score high on sincerity, optimism or self-awareness: e.g. captions expressing vulnerability or hopes for change. We expect statistically significant differences in features like Irony, Target (others vs self), and Tone (callous vs empathetic) between the two groups.

• Examples (Hypothetical): In the corpus, borderer memes might include images like an Internet cartoon (Pepe) firing a gun at “Social Justice”, with a snarky caption. Left memes might include a TikTok screenshot of a teen saying “I’m so awkward I love being me”\[5\]. (Appendix B lists 12 annotated examples.)

• Platform Differences: We expect borderer memes to cluster on fringe sites (e.g. many from 4chan, Telegram) and left memes on mainstream social media (TikTok, Instagram) – consistent with the idea that the left leverages “participatory art” to build solidarity\[11\]\[4\].

• Quantitative Metrics: A logistic model could show, for example, that containing words like “SJW”, “libtard”, or violent imagery strongly predicts a borderer label, while references to feelings, memories, or symbols of unity predict left-coded. Feature correlations might reveal that high ironic distance correlates with features like anonymity or crude fonts.

• Polarization Effect: Consistent with Fernandez Salguero’s findings, we might find that memes rarely convert beliefs; they mostly reinforce group identity. For example, showing a leftist a conservative meme might not persuade them; indeed, a study cited found such exposure intensifies polarization\[12\]. This suggests our results would align with the idea that these meme styles serve to entrench identity (right uses humor to mock others, left uses sincerity to build community) rather than persuade opponents.

4. Discussion Link to heading

Implications: If confirmed, this schema would indicate that online meme culture encodes distinct affective and ideological strategies. Borderer/right-coded memes function as “digital shock troops”, normalizing extremist ideas via humor\[8\]\[4\]. Left/awkward memes, conversely, use “digital empathy” – vulnerability and shared awkwardness to foster solidarity and optimism\[5\]\[6\]. This reflects broader political communication: left narratives build hope and belonging, while populist right narratives defend against perceived threats\[13\]\[4\]. Such findings could help explain why meme warfare often polarizes: each side’s humor appeals primarily to in-group norms, resonating only with those already aligned.

Limitations: Without actual corpus data this is speculative. Political ideologies are not monolithic: many memes may blend features (e.g. leftist self-irony or rightist nostalgia). Cultural context matters (a “quirky” meme in one country could mean something else elsewhere). Coders’ biases can affect labeling; using multiple coders mitigates but not eliminates this. Also, memes evolve rapidly – the style in 2026 may differ from 2023. We also assume a binary left/right coding, but many memes might defy this (centrist or apolitical absurdist memes).

Alternative Explanations: It may be that author identity rather than content solely drives meme style: for example, the same meme template can be used by both left and right with different captions. Or generational factors (GenZ vs older internet users) might explain some style differences rather than political ideology per se\[5\]. Our design tries to separate ideology from form, but cross-correlations (e.g. GenZ leftists vs older alt-right) complicate interpretation.

Political Culture Implications: This schema highlights how digital humor participates in political socialization. If borderer memes do indeed normalize contempt and scapegoating, this could erode discourse civility and empower extremist groups. Conversely, a rise in “authentic” memes suggests younger activists value sincerity and personal narrative. Understanding these dynamics is vital: memes are tools for emotional mobilization\[3\]\[6\]. Educators and moderators might leverage this by promoting more empathy-driven humor, or at least recognizing how certain meme styles can radicalize or alienate.

<img src=“assets/media/rId37.png” style=“width:5.83333in;height:2.98667in” / />Figure 1. Example of an “awkward-authentic” meme style: a startled bride (sourced from a stock wedding photo) superimposed on a cityscape. The image’s humor comes from its childlike awkwardness and inoffensive absurdity, exemplifying how left‑leaning memes often use vulnerability and naïve humor (cf. RollStone on pro-cringe\[5\]). In contrast, Figures 2–3 below show memes with right-coded mockery (they target others to provoke “lulz”).

<img src=“assets/media/rId40.png” style=“width:5.83333in;height:7.1201in” / />Figure 2. Example of a “borderer/right‑coded” meme: a satire “conspiracy pyramid chart” mocking conspiracy theorists. It uses labeled layers (“Science Denial,” “Will to Power,” etc.) with captions like “you are a threat to yourself.” This high‑irony meme ridicules an out‑group and employs a hard-edged graphic style, consistent with studies that far‑right memes often normalize extremist ideas under a veneer of humor\[8\]\[4\].

<img src=“assets/media/rId43.png” style=“width:4.16667in;height:3.88889in” / />Figure 3. Another left‑coded example: a “Chill Pill” meme (text “CHILL 250000 mg”). This clean, simple image dispenses a gentle message (“relax”), reflecting how left/awkward memes often convey self-aware, empathetic humor with no target. It contrasts with rightist memes’ confrontational tone\[8\]\[4\].

5. Conclusion Link to heading

This framework outlines how to rigorously test whether memes can be classified as borderer-coded vs left-coded based on observable features. By applying the above design, researchers can systematically identify ideological patterns in memetic humor. Ultimately, the study would contribute to understanding how digital culture – even “silly” memes – intersects with political identity and conflict.

Appendices Link to heading

Appendix A: Coding Rubric Link to heading

Notes: Each meme is scored on these features. For instance, far-right memes often show “mockery of ‘the other’”\[3\]\[4\], so a meme ridiculing minority groups would tick Mocking and External targets. A leftist meme about mental health would tick Earnest and Self/Issues.

Appendix B: Sample Annotated Memes (N=12) Link to heading

*Although “This is fine” originated as a general absurdist meme, it is often deployed in political threads to convey callous resignation.

Each entry is labeled per our rubric. For example, Meme 4 (Conspiracy Chart) is borderer-coded due to high irony and mocking of others\[3\]. Meme 6 (Chill Pill) is left-coded for its sincere, non-hostile message. No actual URLs are shown here, but each description corresponds to widely recognized meme types on those platforms.

\[1\] \[2\] Internet meme on social media: A comprehensive review and new perspectives - ScienceDirect

https://www.sciencedirect.com/science/article/abs/pii/S1566253525011649

\[3\] \[8\] \[9\] europeanjournalofhumour.org

https://europeanjournalofhumour.org/ejhr/article/download/1157/906/5770

\[4\] \[6\] \[7\] \[11\] \[12\] \[13\] From Murals to Memes: A Theory of Aesthetic Asymmetry in Political Mobilization

https://arxiv.org/html/2510.15256v1

\[5\] ‘Am I Cringey? Yes. Do I Care? Absolutely Not’

https://rollingstoneindia.com/am-i-cringey-yes-do-i-care-absolutely-not/

\[10\] How “the left” meme : analyzing taboo in the internet memes of r/DankLeft

http://www.diva-portal.org/smash/record.jsf?pid=diva2:1840749