A benchmark dataset and evaluation suite mapping Product Hunt launches to Series A outcomes for predictive modeling of startup funding.
Key features
Large-Scale Mapping: Links 67,292 featured Product Hunt posts to 528 verified Series A outcomes within an 18-month horizon, enabling longitudinal outcome prediction.
Engineered Signal Set: Provides 61 engineered features per post including engagement signals (votes, comments, reviews), rank signals (daily/weekly/monthly), maker features (maker count, followers), temporal features, topic flags, and interaction terms to support rich modeling.
Structured Splits and Imbalanced Labels: Published train/validation/test splits (Train: 47,071; Val: 6,753; Test: 13,468) with measured positive rates (~0.76–0.79%), plus withheld test labels for blind benchmark evaluation.
Evaluation & Submission Workflow: Test labels are withheld and researchers submit predictions (email to benchmark@vela.partners) for centralized scoring to enable fair comparison between models.
Open License & Citation: Distributed under CC BY 4.0 (per Hugging Face dataset page) with a required citation (Ihlamur et al., PHBench arXiv 2026) for academic and research use.
Supporting Code & Graph Tools: Associated code and GNN/graph-analysis workflows are available (Weave project on GitHub) to build graph representations and run node-classification experiments; dataset access may require contacting Vela Partners due to access conditions.
Mapped dataset of 67,292 Product Hunt featured posts linked to 528 verified Series A outcomes (18-month horizon, 2019–2025).
61 engineered features per post: engagement signals (votes, comments, reviews), rank signals (daily, weekly, monthly), maker features (maker count, followers), temporal features, topic flags, and interaction terms.
Standard train/validation/test splits with class imbalance details (Train: 47,071 posts, 372 positives; Val: 6,753 posts, 53 positives; Test: 13,468 posts, test labels withheld).
Withheld test labels and centralized scoring: submit predictions to benchmark@vela.partners for evaluation.
Hosted on Hugging Face Datasets with CC-BY-4.0 license; access requires agreeing to share contact information.
Suitable for benchmarking binary classification models, feature-ablation studies, imbalanced learning experiments, and startup outcome research.
Tabular data format compatible with common ML tooling (Hugging Face Datasets, pandas, scikit-learn, PyTorch, TensorFlow).
Includes citation: Ihlamur et al., "PHBench: A Benchmark for Predicting Startup Series A Funding from Product Hunt Launch Signals", arXiv 2026.
Best for
Early-Stage Deal Prioritization: Train classifiers to rank Product Hunt launches by probability of raising Series A within 18 months to help investors triage and prioritize founder outreach.
Research on Launch Signals: Analyze which launch-day signals (engagement, rank, maker attributes) most strongly correlate with later funding to inform product and marketing strategies.
Benchmarking Models: Use the withheld-test benchmark to compare classical ML, deep learning, and LLM-based approaches for startup outcome prediction under standardized splits.
Feature Engineering Studies: Develop and validate new derived signals or temporal interaction features using PHBench’s engineered feature set to improve predictive performance.
Graph & GNN Experiments: Construct graph representations of makers, posts, and interactions (using the Weave tooling) to evaluate graph neural networks for node-level fundraising prediction.
Tooling for Founders: Build launch-advising tools that estimate fundraising likelihood from Product Hunt metrics and suggest actions to improve discovery and traction.
Benchmarking binary classifiers for predicting Series A funding from early launch signals.
Feature engineering and ablation studies on engagement, rank and maker features.
Research on imbalanced classification methods and calibration for rare events.
Startup scouting and signal analysis for VC or accelerator decision support.
Time-window outcome modeling and survival/time-to-event approximations using launch temporal features.
