litrl/litrl_code: Litrl Browser Experimental 0.14.0.0 Public
Bibliographic record
Abstract
The Litrl (pronouned "literal") Browser is a research tool for news readers, journalists, editors or information professionals. The tool analyzes the language used in digital news web pages to determine if they are clickbait, satirical news, or falsified news. The current online news environment is one that incentivizes speed and spectacle in reporting at the cost of fact-checking and verification, encouraging the proliferation of misinformation and disinformation. The LiT.RL News Verification (NV) Browser is a system that offers a first step counter-measure by automatically detecting and highlighting clickbait (to 94% accuracy on a test set of 5670 texts), satire (to 84% accuracy on a test set of 95 texts), and falsified text (to 71% accuracy on a test set of 28 texts). The browser was built to study the effectiveness of these deception detectors when applied to real-world internet use, where the accuracy of these detectors may vary considerably given the range of text online. Digital literacy is key for everyone to effectively evaluate potential misinformation online, and the Litrl Browser is <strong>NOT</strong> a replacement for that. All processing is completed on the local machine - clickbait, satirical news, and falsified news results are not sent to or from a remote server. Results may be saved locally to a standard SQLite database for further analysis. Please note that Litrl Browser is not perfect and is not always correct.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.600 | 0.163 |
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; both teacher heads agree on what is shown here.
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".