Designed for university faculty, instructional librarians, OER coordinators, and ESL department chairs. Select your course track below to generate a ready-to-paste CC BY 4.0 Syllabus Module backed by our Harvard Dataverse (doi:10.7910/DVN/IOIADB) and CERN Zenodo (10.5281/zenodo.23541347) open-access publications.
University Sociolinguistics, Pragmatics & Lexical Innovation
Target courses: Undergraduate linguistics, English language, communication studies, and digital anthropology courses.
Learning Objectives
- Analyze how hyperbolic exhaustion markers (cooked, crash out) undergo pragmatic weakening in peer-to-peer messaging.
- Compare transatlantic lexical variation across US, British, and Hiberno-English conversational registers (craic, grand, sound, knackered vs. bet, lowkey).
- Evaluate how algorithmic content moderation induces deliberate lexical substitution (algospeak).
Assigned Open-Access Readings (Harvard Dataverse & CERN Zenodo)
- SlangLane Open Reference Corpus & Dataset (Harvard Dataverse Edition) ↗ — DataCite DOI:
10.7910/DVN/IOIADB - The 2026 State of Internet English & Regional Slang Report ↗ — DataCite DOI:
10.5281/zenodo.23547890 - SlangLane Lexicon Working Paper & Sense-Frame Methodology ↗ — DataCite DOI:
10.5281/zenodo.23541347
Seminar Discussion & Lab Prompts
- Why do contemporary speakers prefer ironic hyperbole (“I am cooked for this midterm”) over literal hedging (“I feel unprepared”)?
- Using the SlangLane Dialect Matrix (/dialects), compare how Irish “grand” and American “bet” perform conversational alignment.
Fieldwork & Assessment Assignment
Collect 3 attested conversational expressions from student messaging or regional speech. Using SlangLane’s schema (/methodology), document each term’s part of speech, pragmatic tone, regional scope, and one non-slang paraphrase.
Copy-Paste Text for Your Syllabus, Canvas, Moodle, or LibGuide
COURSE MODULE: University Sociolinguistics, Pragmatics & Lexical Innovation (LING 204 / ENGL 315 · Undergraduate Seminar) Open Educational Resource (CC BY 4.0) · Archived in Harvard Dataverse (doi:10.7910/DVN/IOIADB) & CERN Zenodo (doi:10.5281/zenodo.23541347) LEARNING OBJECTIVES: 1. Analyze how hyperbolic exhaustion markers (cooked, crash out) undergo pragmatic weakening in peer-to-peer messaging. 2. Compare transatlantic lexical variation across US, British, and Hiberno-English conversational registers (craic, grand, sound, knackered vs. bet, lowkey). 3. Evaluate how algorithmic content moderation induces deliberate lexical substitution (algospeak). ASSIGNED OPEN-ACCESS READINGS (ZERO COST / NO LOGIN): • SlangLane Open Reference Corpus & Dataset (Harvard Dataverse Edition): https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IOIADB (DOI: https://doi.org/10.7910/DVN/IOIADB) • The 2026 State of Internet English & Regional Slang Report: https://slanglane.com/report (DOI: https://doi.org/10.5281/zenodo.23547890) • SlangLane Lexicon Working Paper & Sense-Frame Methodology: https://slanglane.com/data/SlangLane-Lexicon-Working-Paper.pdf (DOI: https://doi.org/10.5281/zenodo.23541347) SEMINAR DISCUSSION PROMPTS: • Why do contemporary speakers prefer ironic hyperbole (“I am cooked for this midterm”) over literal hedging (“I feel unprepared”)? • Using the SlangLane Dialect Matrix (/dialects), compare how Irish “grand” and American “bet” perform conversational alignment. GRADED FIELDWORK ASSIGNMENT: Collect 3 attested conversational expressions from student messaging or regional speech. Using SlangLane’s schema (/methodology), document each term’s part of speech, pragmatic tone, regional scope, and one non-slang paraphrase.
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