Memes and Slang: The Role of Visual Content in the Emergence and Stabilization of Persistent Expressions in Contemporary English Slang

Contemporary English slang evolves under conditions that change faster than traditional accounts of lexical innovation. A persistent puzzle appears in this environment: some slang phrases stabilize quickly, even when communities vary in topic, platform, and style, while other candidates fade despite similar semantic appeal. Visual meme formats offer a practical mechanism for reuse, because the image–caption package lowers the effort of replication and helps audiences recognize a target wording on sight. At the same moment, this raises a linguistic question about how visual templates interact with lexical and phraseological choices to produce expressions that keep recurring across time and groups.

The study examines how memes contribute to the emergence and stabilization of persistent expressions in contemporary English slang. It sets out to systematize the concept of memetic language change for multimodal data, to operationalize stabilization, variation, and replicability as analyzable constructs, and to trace form–meaning pathways from early attestations to later spread. It further seeks to compare meme-driven diffusion with slang that circulates through text-only channels, and to interpret observed differences using frequency-based measures, phraseological checks, pragmatic annotation, and computational template matching.

The object of the study is the set of multimodal meme units and slang instances collected from contemporary English-speaking online communication. The subject of the study concerns the relationship between visual template features (image formats, layout routines, caption placement) and linguistic outcomes (recurring words, phrase patterns, and pragmatic meanings) as expressions move from variation toward stabilization.

The work builds a conceptual lens that connects three strands: memetics as a framework for repeatable cultural units, multimodality as a way to treat images and text as co-structured meaning resources, and usage-based language change as an account of how recurrent patterns become entrenched. Within this framework, “persistent expressions” are treated as outcomes of stabilization, while “variation” and “replicability” describe the pathways by which alternative wordings remain compatible with a shared meme format. This framing guides analysis toward the interaction between visual cues and linguistic packaging rather than toward slang meanings in isolation.

Empirically, the project constructs a corpus from contemporary English-speaking platforms where memes and slang circulate, including meme repositories and social media feeds featuring captioned images. It applies inclusion criteria grounded in a defined time window, a focus on English use, and platform type, while capturing metadata such as date, source community, and format. Candidate persistent expressions are then identified by tracking recurring alignments between specific visual templates and particular lexical or phraseological realizations, enabling close attention to how re-captioning and lexical substitution reshape meaning without breaking recognizability.

To explain emergence, the study follows form–meaning pathways by documenting early attestations of meme-based templates and the subsequent spread of the accompanying slang expressions. Variation receives systematic treatment: changes in wording, shifts in syntactic shape, and pragmatic re-anchoring through new captions are assessed for how they remain constrained by the visual context. The analysis then compares these trajectories with text-only slang diffusion to evaluate differences in adoption speed, the degree of formal stabilization, and the resilience of expressions to paraphrase. Linguistic and computational procedures–frequency and dispersion measures, collocation and phraseology checks, pragmatic-function annotation, and template-matching for visual formats–support an account of why repeated recognition can reinforce durable wording, standardize interpretation across communities, and reduce ambiguity for new users.

Finally, the findings are used to support predictive approaches that identify which emerging slang expressions are likely to persist based on template features and measurable diffusion characteristics. The project also outlines extensions to longitudinal monitoring and to broader generalization across communities, languages, and media formats, aiming to refine how researchers can anticipate meme-driven language change rather than describe it only after stabilization has already occurred.