Born-Digital Memes as Archival Discourse: A Linked-Data Analysis of Cultural Sentiment and Polarization
Bibliographic record
Abstract
This study investigates how born-digital memes about high-profile events can serve as rich archival resources for understanding contemporary cultural phenomena and public sentiment by using a linked-data framework. Using a mixed-method approach, this study analyzes memes from a high-profile trial through web scraping and linked-data structures to map themes, sentiments, and cultural references. The linked-data frame includes data collection and integration, semantic web technologies, ontology development, and API data access. The findings point to dominant narratives and shifting sentiment, which further illustrate how such memes reflect and contribute to the polarization of the societal discourse concerning the event. This research is relevant for understanding digital culture, exploring the archival potential of born-digital materials, and assessing the dynamics of public opinion in widely publicized cases. By showing the efficiency of linked data methodologies in the analysis of born-digital discourse, we add valuable insights to both digital humanities and social sciences, offering a new approach of studying ephemeral online content as cultural artifacts.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".