rajatagarwal457/gdelt-forecast-binary

GDELT-Forecast Binary

1,215 yes/no forecasting questions generated from clusters of news articles in the GDELT 2.0 corpus (Aug 2025 – Apr 2026). Each question is paired with the original seed-event articles, top-5 retrieved evidence articles dated strictly before the question creation date, and a verified ground-truth answer.

Intended use

Training and evaluating LLM-based forecasting models in a strict forecasting posture — the model sees only news that was publicly available before the question's creation date, then predicts the resolution. Companion to gdelt-forecast-freeform, which contains 924 non-binary questions (name, number, date, free-form) built from the same corpus.

How it was built

Five-stage pipeline over the GDELT 2.0 corpus (~31.7 M articles):

1. Politics/geopolitics filter — GPT-4o-mini classifies articles by topic; ~2 % retained.
2. Event clustering — articles describing the same event clustered with a similarity-graph approach. 2,721 clusters of size 3-5 retained.
3. Question generation — GPT-4o reads each cluster and writes a forecasting question that resolves after the most recent article in the cluster. Answer type pre-assigned (here: yes/no). Sources must agree on the answer.
4. Evidence retrieval — for each question, top-5 articles retrieved from the corpus via TF-IDF ∪ OpenAI-embed FAISS → OpenAI rerank, filtered to publication date strictly < question creation date.
5. Aggregation — questions with ≥ 5 evidence articles kept. No similarity-based dedup at this stage (we leave that to downstream consumers).

Schema

Column	Type	Description
qid	int	Synthetic question ID. Stable within this release.
cluster_id	int	ID of the source event cluster.
cluster_size	int	Number of articles in the source cluster (3-5).
question_title	string	The forecasting question itself.
background	string	One- or two-sentence summary of the event context.
resolution_criteria	string	How the answer is defined and what counts as a match.
answer_type	string	Always yes_no in this dataset.
ground_truth_answer	string	Either yes or no.
question_start_date	string (YYYY-MM-DD)	The cutoff. No information from this date or later is allowed when forecasting.
resolution_date	string (YYYY-MM-DD)	When the question's outcome is known.
seed_urls	list[string]	URLs of the original cluster articles (the ones GPT-4o read to generate the question).
seed_dates	list[string]	Publication dates of the seed articles.
evidence_articles	list[struct]	Top-5 retrieved evidence articles. Each: {url, date, source, text}. All dated strictly before question_start_date.
sources_agree	bool	Whether the seed articles' implied answers agreed during generation. 5 rows have False; treat with caution.
quality_flags	list[string]	Empty in this release.
required_type	string	The answer type that was requested at generation time (always yes_no here).

Statistics

• Total questions: 1,215
• Cluster size: 3 → 538, 4 → 251, 5 → 426
• Sources agree: 1,212 / 1,215
• Date range (question_start_date): late Aug 2025 → mid Mar 2026
• Median forecast horizon (cutoff → resolution): ~14 days
• Ground-truth balance: roughly 60 / 40 yes / no

Strict forecasting posture

question_start_date is the cutoff. The retrieval pipeline filters all evidence to dates < question_start_date. If you build your own retrieval, respect this cutoff — articles dated on or after question_start_date may contain explicit answers, leading to leakage.

Known limitations

• GDELT date metadata is trusted; we have not audited mis-dating systematically.
• Some questions may be answerable from priors alone if the model has memorised related real-world events. We recommend pairing with a no-context baseline run.
• Some questions are near-duplicates of others (no dedup at this stage). If you need a dedup'd subset, embed question_title and drop pairs with cosine sim ≥ 0.85.

Citation

If you use this dataset, please cite:

@misc{anthral2026gdeltforecast,
  title  = {GDELT-Forecast: Strict-cutoff forecasting benchmarks from GDELT news clusters},
  author = {Rajat Agarwal and Anthral Labs},
  year   = {2026},
  url    = {https://huggingface.co/datasets/rajatagarwal457/gdelt-forecast-binary},
}

License

CC-BY-4.0, matching the upstream GDELT 2.0 license. Attribution: cite this dataset and acknowledge GDELT as the source corpus.