CrediBench: Building Web-Scale Network Datasets for Information Integrity
Bibliographic record
Abstract
Automatically assessing the credibility of online sources presents an invaluable tool for navigating today's information ecosystem. However, existing approaches either depend on scarce and costly human annotations, or focus exclusively on assessments at the level of individual claims. Misinformation often spreads via interlinked web domains, whose connections evolve over time. Focusing on claims alone ignores these structural and temporal credibility signals evident in the changing web topology. Existing datasets fail to capture these central modalities in web domain credibility prediction: namely, internet topology, temporality and text (webpage) content. To address this gap, we present CrediBench, a dataset containing eights months of web graph data; of which we analyze the three months surrounding the 2024 U.S. federal elections, a time of heightened misinformation propagation online. Each monthly snapshot contains over 40 million nodes, their scraped webpage content, and over 1 billion hyperlink edges. CrediBench supports credibility prediction as both a regression (continuous credibility score) and a binary classification task (credible or not). For classification, we curate a novel binary label set containing 662,575 web domains labelled for boolean credibility, spanning four areas (misinformation, crowd-sourced, malware and phishing). Our empirical experiments support that all task modalities-graph, text and time-contribute significantly to achieving the best performance. In particular, our multi-modal regression model trained on CrediBench outperforms other configurations and existing baselines, decreasing Mean Average Error from 0.162 to 0.107 on the regression task, while the multi-modal classifier improves accuracy from 56% to 85% on the classification one. CrediBench, our proposed web-scale multi-modal dataset, is available on Huggingface for future research.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".