🔗 Semantic Scholar Citations Scraper

🚀 Export Semantic Scholar citations and references for any paper in seconds. Clean tabular records, ready for download.

🕒 Last updated: 2026-06-05 · 📊 12 fields per record · Public API · No login required

The Semantic Scholar Citations Scraper turns the public Semantic Scholar graph API into a flat dataset of citing papers (or references), with citation intent, contexts, and influence flags.

Filter the source, pick how many rows you want, and download.

🎯 Target Audience	💡 Primary Use Cases
🧑‍🔬 Researchers	Pull a clean dataset for downstream analysis
👩‍💻 Developers	Mirror the API into your own database
🏢 Data teams	Snapshot metadata on a schedule
📰 Journalists	Verify claims against the source of record

📋 What the Semantic Scholar Citations Scraper does

• Calls the public endpoint with the parameters you supply.
• Parses each record into a flat row.
• Casts numeric fields to numbers.
• Surfaces upstream rate limits as a clean error record.
• Exports to every Apify dataset download format.

💡 Why it matters. A flat tabular shape drops straight into pandas, BigQuery, or a Google Sheet without further wrangling.

🎬 Full Demo

🚧 Coming soon.

⚙️ Input

Field	Type	Required	Description
paperId	string	Yes	Semantic Scholar paper ID, DOI, arXiv ID, or URL.
mode	enum	No	Citations or references.
maxItems	integer	No	Free 10, paid up to 1,000,000.
apiKey	secret string	No	Optional Semantic Scholar API key for higher rate limits.

Example 1, citations of attention-is-all-you-need:

{
  "paperId": "10.48550/arXiv.1706.03762",
  "mode": "citations",
  "maxItems": 50
}

Example 2, references of a paper:

{
  "paperId": "DOI:10.1038/nature14539",
  "mode": "references"
}

⚠️ Good to Know. The upstream public API may rate-limit aggressive callers. The actor surfaces rate-limit notes as a clean error record instead of crashing.

📊 Output

Each record is a flat object.

Field	Type	Description
🆔 paperId	string	Source paper ID.
🆔 citingPaperId	string	Citing or cited paper ID.
📝 title	string	Paper title.
👥 authors	string	Comma-separated authors.
📅 year	number	Publication year.
🏛️ venue	string	Publication venue.
🎯 intent	string	Citation intent (background, method, result).
🔢 contextsCount	number	Number of citation contexts.
⭐ influential	boolean	Whether the citation is flagged as influential.
🔗 doi	string	DOI of citing or cited paper.
🕒 scrapedAt	string	When this row was fetched.
❌ error	string	Set if the upstream response was an error.

✨ Why choose this Actor

| 🆓 | Free to try, no signup needed. | | 🧹 | Clean column names ready for BI tools. | | 🔢 | Numeric casting handled for you. | | 🛟 | Rate-limit notes surface as a single error record. | | 🔍 | Filter the source through the input schema. | | 💾 | Push to dataset, instant tabular export. |

📈 How it compares to alternatives

Approach	Setup time	Clean schema	Filters	Rate-limit handling
Roll your own fetch	30+ minutes	manual	manual	manual
This Actor	5 seconds	✅	✅	✅

🚀 How to use

1. Click Try for free.
2. Adjust the inputs to match your query.
3. Click Start. Your dataset is ready in seconds.

💼 Business use cases

📊 Market intelligence. Snapshot the catalog and track new entries week over week.

🤖 ML feature engineering. Build a clean training set from public records.

📰 Newsroom fact-checking. Verify claims in 30 seconds.

🏢 Procurement. Pipe records straight into your warehouse.

🔌 Automating Semantic Scholar Citations Scraper

• Make and Zapier. Trigger on a schedule, push to Airtable or Google Sheets.
• Apify scheduler. Run it daily, weekly, or on demand.
• Webhooks. POST to your endpoint when a run finishes.
• Pipe to BigQuery, Snowflake, Postgres via native Apify integrations.

🌟 Beyond business use cases

🎓 Education. Use real public datasets for coursework.

🧪 Personal research. Track topics that matter to you.

🤝 Non-profit and open data. Build public dashboards without writing scrapers.

🧰 Tinkering. Spin up a dataset to test a new chart library or notebook idea.

🤖 Ask an AI assistant about this scraper

Pop this README into ChatGPT, Claude, or any AI assistant and ask it to map your workflow to the actor's inputs. The schema, examples, and field list above contain everything an LLM needs to design a working pipeline.

❓ Frequently Asked Questions

❓ Do I need an API key. No. The Semantic Scholar endpoint used here is public.

❓ Can I filter the source. Yes. See the input schema for the filters available.

❓ Can I schedule runs. Yes, use the Apify native scheduler.

❓ Is this scraping or API. API. The endpoint is fully public.

❓ Will the schema change. Core fields are stable. Optional fields surface as null when the source omits them.

❓ What format can I download. Every Apify dataset download format from the dataset UI.

❓ How do you handle rate limits. A clean error record is pushed to the dataset.

❓ Can I run partial pulls. Yes, set maxItems to the size you need.

❓ Is it free. Free tier gives you 10 records, paid plans up to 1,000,000.

❓ Does the actor cache results. No, every run hits the live endpoint.

🔌 Integrate with any app

Apify ships native integrations with Make, Zapier, Slack, Discord, Google Drive, Google Sheets, Gmail, Airbyte, Keboola, Telegram, GitHub, and any REST API or webhook endpoint.

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💡 Pro Tip. Browse the complete ParseForge collection for 900+ production-grade scrapers across business intelligence, real estate, e-commerce, sports, finance, and public records.

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