JayNightmare/Misinformation-Dataset

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

from datasets import load_dataset

dataset = load_dataset(
    "json",
    data_files="normalized_records.jsonl",
    split="train",
)

print(dataset[0])

CSV Flat Schema

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:

# 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:

@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:

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. The adapter was trained on cleaned splits from the master dataset and is intended for research on context-aware misinformation classification.