Datasets:
Modalities:
Text
Formats:
json
Sub-tasks:
explanation-generation
Languages:
English
Size:
100K - 1M
Tags:
fake-news-detection
fact-checking
claim-verification
multimodal-metadata
context-ablation
research
DOI:
License:
| pretty_name: FakeNews Master Dataset | |
| size_categories: | |
| - 100K<n<1M | |
| task_categories: | |
| - text-classification | |
| - text-generation | |
| task_ids: | |
| - explanation-generation | |
| tags: | |
| - fake-news-detection | |
| - fact-checking | |
| - claim-verification | |
| - multimodal-metadata | |
| - context-ablation | |
| - research | |
| license: other | |
| language: | |
| - en | |
| multilinguality: | |
| - multilingual | |
| # FakeNews Master Dataset | |
| The FakeNews Master Dataset is a merged, cleaned, and deduplicated research corpus for studying misinformation classification under different context conditions. It combines locally available mirrors of ClaimReview, Fakeddit, FakeNewsNet, and MuMiN into one canonical schema while preserving source-dataset provenance. | |
| This dataset is intended for experiments that ask whether open-source models can classify misinformation when given different amounts or qualities of context, such as full context, minimal context, or misleading context. | |
| ## Dataset Summary | |
| | Field | Value | | |
| | ------------------------------- | -------------------------------------------------- | | |
| | Total rows before deduplication | 374,738 | | |
| | Total rows after deduplication | 360,348 | | |
| | Source datasets | ClaimReview, Fakeddit, FakeNewsNet, MuMiN | | |
| | Primary task | Binary misinformation classification | | |
| | Internal label convention | `mapped_label: 0 = real`, `mapped_label: 1 = fake` | | |
| | Flat export label convention | `Real: 1 = real`, `Real: 0 = fake` | | |
| | Modalities | Text plus image/news metadata where available | | |
| ## Source Composition | |
| | Source dataset | Cleaned rows | Raw rows used | Retention rate | Acquisition notes | | |
| | -------------- | -----------: | ------------: | -------------: | ---------------------------- | | |
| | ClaimReview | 265,884 | 271,083 | 0.9809 | Pinned `2026_06_12` snapshot | | |
| | Fakeddit | 85,577 | 100,000 | 0.8558 | Sample fallback mirror | | |
| | FakeNewsNet | 21,889 | 23,196 | 0.9437 | Local CSV mirror | | |
| | MuMiN | 1,388 | 1,404 | 0.9886 | CSV export fallback | | |
| ## Files | |
| The dataset folder includes three export families: | |
| | File pattern | Purpose | | |
| | -------------------------------------- | ----------------------------------------------------------------------------------------------- | | |
| | `test split.{json,jsonl,csv}` | Test split for adapter training and evaluation. | | |
| | `train split.{json,jsonl,csv}` | Training split for adapter training. | | |
| | `validation split.{json,jsonl,csv}` | Validation split for adapter training. | | |
| | `normalized_records.{json,jsonl,csv}` | Deduplicated canonical `UnifiedRecord` corpus for experiments and training. | | |
| | `master_records.{json,jsonl,csv}` | Provenance-rich master records with normalized source fields. | | |
| | `master_flat_records.{json,jsonl,csv}` | Flat analysis export with `id`, `Title`, `Claim`, `news source`, `Real`, and `tweet_num`. | | |
| | `master_dataset_manifest.json` | Build manifest with source counts, acquisition notes, cleaning reports, and coverage summaries. | | |
| | `master_dataset_coverage.json` | Aggregate and per-source coverage report for flat export fields. | | |
| For most Hugging Face `datasets` workflows, start with `normalized_records.jsonl` if you want the full canonical schema, or `master_flat_records.csv` if you want a compact spreadsheet-like table. | |
| ## Schema | |
| ### Canonical Records | |
| The canonical exports use the repository's `UnifiedRecord` schema. Key fields include: | |
| | Field | Description | | |
| | --------------------- | -------------------------------------------------------------------------- | | |
| | `dataset` | Source dataset name. | | |
| | `sample_id` | Source-aware stable sample identifier. | | |
| | `text` | Primary claim, post, headline, or article text used by the model pipeline. | | |
| | `context_text` | Additional source context when available. | | |
| | `original_label` | Source-specific label value. | | |
| | `original_label_name` | Human-readable source-specific label. | | |
| | `mapped_label` | Internal binary label, where `0 = real` and `1 = fake`. | | |
| | `mapped_label_name` | Internal binary label name, `real` or `fake`. | | |
| | `split` | Dataset split value when assigned. | | |
| | `modality` | Source modality descriptor. | | |
| | `has_image` | Whether image metadata is available. | | |
| | `metadata` | Source-specific provenance and auxiliary fields. | | |
| | `cleaning_notes` | Notes emitted by the cleaning pipeline. | | |
| ### Flat Records | |
| The flat export is designed for inspection and analysis tools: | |
| | Field | Description | | |
| | ------------- | ----------------------------------------------------------- | | |
| | `id` | Master dataset row identifier. | | |
| | `Title` | Title/headline when available. | | |
| | `Claim` | Primary claim text. | | |
| | `news source` | Source, publisher, platform, or domain field. | | |
| | `Real` | Flat binary label where `1 = real` and `0 = fake`. | | |
| | `tweet_num` | Tweet/social count when available, mostly from FakeNewsNet. | | |
| ## Field Coverage | |
| Aggregate flat-export coverage: | |
| | Field | Non-null rows | Null rate | | |
| | ------------- | ------------: | --------: | | |
| | `Title` | 106,125 | 0.7055 | | |
| | `Claim` | 360,348 | 0.0000 | | |
| | `news source` | 360,348 | 0.0000 | | |
| | `Real` | 360,348 | 0.0000 | | |
| | `tweet_num` | 20,732 | 0.9425 | | |
| High null rates for `Title` and `tweet_num` are expected because not every source dataset provides those fields. | |
| ## Loading Examples | |
| ### JSONL Canonical Schema | |
| ```python | |
| from datasets import load_dataset | |
| dataset = load_dataset( | |
| "json", | |
| data_files="normalized_records.jsonl", | |
| split="train", | |
| ) | |
| print(dataset[0]) | |
| ``` | |
| ### CSV Flat Schema | |
| ```python | |
| from datasets import load_dataset | |
| dataset = load_dataset( | |
| "csv", | |
| data_files="master_flat_records.csv", | |
| split="train", | |
| ) | |
| print(dataset.features) | |
| ``` | |
| ### Label Conversion Reminder | |
| The canonical pipeline and flat analysis export use opposite binary conventions: | |
| ```python | |
| # Canonical normalized_records: mapped_label 0=real, 1=fake | |
| canonical_label = row["mapped_label"] | |
| # Flat master_flat_records: Real 1=real, 0=fake | |
| is_real = row["Real"] | |
| ``` | |
| ## Dataset Creation Pipeline | |
|  | |
| The builder reuses each source-specific loader, applies the shared cleaning pipeline, maps labels into a binary research schema, deduplicates records by stable fingerprints, and writes the canonical, master, and flat exports. | |
| ## Intended Uses | |
| This dataset is intended for: | |
| - Research on misinformation and fake-news classification. | |
| - Context-ablation experiments with open-source language models. | |
| - Comparing model behavior across datasets, labels, and context settings. | |
| - Building deterministic train/validation/test splits for adapter fine-tuning. | |
| - Inspecting source coverage and field availability across merged misinformation datasets. | |
| ## Out-of-Scope Uses | |
| This dataset should not be used as a sole source of truth for: | |
| - Automated content moderation. | |
| - Real-world fact-checking without human review. | |
| - Legal, medical, election, financial, or safety-critical decisions. | |
| - Ranking people, publishers, communities, or organizations as trustworthy or untrustworthy. | |
| Source labels reflect the conventions, sampling choices, and historical snapshots of the underlying datasets. They are research labels, not universal truth claims. | |
| ## Biases, Risks, and Limitations | |
| - The merged corpus is source-imbalanced: ClaimReview accounts for most rows. | |
| - Individual source datasets use different collection methods, label definitions, time periods, languages, and domains. | |
| - Fakeddit and MuMiN are represented through local/sample fallback mirrors in this build. | |
| - Some rows contain little context beyond a claim or title, which affects context-sensitive experiments. | |
| - Image URLs or image metadata may be present for some records, but this master export does not guarantee image availability or long-term URL validity. | |
| - Deduplication reduces repeated content but cannot guarantee complete removal of near-duplicates. | |
| - The flat `Real` label intentionally inverts the internal label convention for analysis compatibility; check labels carefully before training. | |
| ## Licensing and Source Terms | |
| This master dataset is an aggregated research artifact derived from multiple upstream datasets. Users are responsible for complying with the terms, licenses, and citation requirements of each original source dataset. | |
| The repository code is MIT licensed, but that does not automatically grant identical rights for every upstream data record. Treat this dataset as `license: other` unless and until each upstream source license is verified for your use case. | |
| ## Citation | |
| The project paper is currently being drafted for submission to a peer-reviewed conference. | |
| **BibTeX:** | |
| ```bibtex | |
| @misc{compaan_fakenews_master_dataset_2026, | |
| title = {FakeNews Master Dataset for Context-Aware Misinformation Classification}, | |
| author = {Compaan, Jay Bell and Peiling, Yi and Charles, Michael J. C.}, | |
| year = {2026}, | |
| note = {Research dataset artifact; paper in preparation} | |
| } | |
| ``` | |
| **APA:** | |
| Compaan, J. B., Peiling, Y., & Charles, M. J. C. (2026). *FakeNews Master Dataset for Context-Aware Misinformation Classification*. Research dataset artifact; paper in preparation. | |
| ## Authors and Contact | |
| - Dr. Yi Peiling - Kingston University, UK | |
| - Jay Bell Compaan (JayNightmare) - Kingston University, UK | |
| - Michael JC Charles - Kingston University, UK | |
| For questions or comments about this dataset, please contact Dr. Yi Peiling at <yi.peiling@kingston.ac.uk>. | |
| ## Reproducibility | |
| The master dataset can be rebuilt from the repository with: | |
| ```bash | |
| python src/datasets/fetch_datasets.py | |
| python src/build_master_dataset.py --dataset all --output-dir src/datasets/data/master_dataset | |
| ``` | |
| Important outputs: | |
| - `master_dataset_manifest.json` | |
| - `master_dataset_coverage.json` | |
| - `normalized_records.jsonl` | |
| - `master_records.jsonl` | |
| - `master_flat_records.csv` | |
| ## Dataset in Use | |
| This dataset was used to train the `FakeNews` adapter, which is available on Hugging Face at [JayNightmare/FakeNews](https://huggingface.co/JayNightmare/FakeNews). The adapter was trained on cleaned splits from the master dataset and is intended for research on context-aware misinformation classification. |