Academic Tweets in Applied Linguistics: An Intertextuality Odyssey through Forms and Functions in Five English-Speaking Countries
Bibliographic record
Abstract
Social media platforms, particularly Twitter, have transformed how academics communicate, disseminate research, and engage with broader audiences. This study explored intertextuality within academic tweets crafted by applied linguists across five English-speaking countries: the United States, the United Kingdom, Australia, Canada, and Ireland. By analyzing tweets from prominent Applied Linguistics associations, the research identified intertextual representations and examined how they refer to or incorporate other texts. The study used a qualitative approach to uncover the forms and functions of intertextuality, highlighting the complex relationships between texts and social actors on Twitter. A corpus of 300 tweets from major associations in Applied Linguistics provided a rich dataset for analysis. Key findings indicated that intertextual practices in academic tweets are crucial for self-promotion, publicizing research outputs, and building academic communities. Multimodal quotations, digital mentions, and hyperlinks enhance engagement, extend reach, and provide additional context. Tweets served multiple functions, including community building, networking, and public dissemination of academic knowledge. The study highlighted the evolving nature of academic communication on social media, suggesting that applied linguistics groups strategically use Twitter to enhance their scholarly presence and impact. Practical implications included the strategic use of hashtags, multimodal elements, and active engagement through retweets, mentions, and replies, which improve visibility, impact, and foster a sense of community within the field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".