# Semantic Scholar Scraper - Cheap 📚🔎🤖 (`scrapestorm/semantic-scholar-scraper---cheap`) Actor

🔎 Easily collect research papers from Semantic Scholar

Provide one or multiple search keywords, paper URLs or author profiles and extract structured academic data such as 📄 Paper Title👨‍🔬 Authors 📅 Publication Year 🔗 Paper URL & more

Perfect for academic research & AI research monitoring 📚

- **URL**: https://apify.com/scrapestorm/semantic-scholar-scraper---cheap.md
- **Developed by:** [Storm_Scraper](https://apify.com/scrapestorm) (community)
- **Categories:** Automation, Developer tools
- **Features:** Limited permissions
- **Stats:** 4 total users, 0 monthly users, 100.0% runs succeeded, 1 bookmarks
- **User rating**: 5.00 out of 5 stars

## Pricing

from $0.00005 / actor start

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

### Semantic Scholar Papers Scraper 📚🔎🤖

The Semantic Scholar Papers Scraper is a powerful automation tool designed to extract structured academic research data directly from:

👉 Semantic Scholar

Whether you're monitoring research trends, tracking citations, analyzing scientific publications, building academic datasets, or collecting AI research papers — this scraper helps you collect clean, structured paper data quickly and reliably.

***

### 🔍 What It Does

Simply provide one or more Semantic Scholar search URLs, author pages, or paper listings, and the scraper will:

✅ Scrape research papers from each URL independently

✅ Automatically paginate through results to collect more papers

✅ Respect maxItemsPerUrl limits

✅ Extract clean, structured academic metadata

✅ Provide ready-to-use dataset views

***

### 📊 Data Extracted

🔹 Overview Fields

| Field            | Description          |
| ---------------- | -------------------- |
| 🆔 paperId       | Paper identifier     |
| 📄 title         | Paper title          |
| 👨‍🔬 authors    | Paper authors        |
| 📅 year          | Publication year     |
| 📊 citationCount | Number of citations  |
| 🏫 venue         | Journal / conference |
| 🔗 paperUrl      | Direct paper page    |
| 🌐 sourceUrl     | Original search URL  |

🔹 Detailed Fields

| Field                    | Description               |
| ------------------------ | ------------------------- |
| paperId                  | Paper identifier          |
| title                    | Paper title               |
| abstract                 | Paper abstract            |
| authors                  | Authors list              |
| authorIds                | Author identifiers        |
| year                     | Publication year          |
| venue                    | Journal / conference      |
| publicationDate          | Publication date          |
| citationCount            | Number of citations       |
| referenceCount           | Number of references      |
| influentialCitationCount | Influential citations     |
| fieldsOfStudy            | Research fields           |
| publicationTypes         | Publication types         |
| journalName              | Journal name              |
| openAccessPdf            | Open access PDF link      |
| pdfUrl                   | PDF download link         |
| paperUrl                 | Paper page URL            |
| sourceUrl                | Source search URL         |
| scrapeTimestamp          | Data extraction timestamp |

***

### 🛠 How to Use

1️⃣ Deploy the Actor on Apify

2️⃣ Provide one or more Semantic Scholar search URLs

Examples:

https://www.semanticscholar.org/search?q=machine%20learning

or

https://www.semanticscholar.org/author/

3️⃣ Set maxItemsPerUrl

0 = unlimited

Any number = maximum papers per URL

4️⃣ Run the scraper

5️⃣ Export your data in:

✅ JSON

✅ CSV

✅ Excel

✅ XML

✅ HTML

***

### 💸 Pricing

This scraper runs on a **monthly subscription model**.

You only pay for **successful runs**.

💳 **Price:** $19.89 / month

***

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- [Seek Job / JobStreet Scraper (Advanced & Cheap) 💼](https://apify.com/scrapestorm/seek-job-jobstreet-scraper-advanced-cheap)
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- [Jobs Email Scraper – Advanced Cheap & Reliable 🔍💼](https://apify.com/scrapestorm/jobs-email-scraper---advanced-cheap-reliable)
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- [Greenhouse Job Board Scraper 🔍💼 - Rental](https://apify.com/scrapestorm/greenhouse-job-board-scraper---rental)
- [Jobs.ch Search Scraper (Fast & Cheap) 💼 🇨🇭](https://apify.com/scrapestorm/jobs-ch-search-scraper-fast-cheap)
- [Bundesagentur für Arbeit (Arbeitsagentur) Scraper 🇩🇪 💼 Cheap](https://apify.com/scrapestorm/bundesagentur-fur-arbeit-arbeitsagentur-scraper-cheap)
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- [Pracuj.pl Jobs Scraper 🇵🇱💼 - Cheap (Tani)](https://apify.com/scrapestorm/pracuj-pl-jobs-scraper---cheap-tani)
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- [Y Combinator Company Scraper 🧠💼 - Cheap & Fast](https://apify.com/scrapestorm/y-combinator-company-scraper---cheap-fast)
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- [Firmy.cz Search Scraper 🔍🏢 🇨🇿 - Cheap Levný](https://apify.com/scrapestorm/firmy-cz-search-scraper---cheap-levny)
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- [YouTube Transcript Short Scraper 📝 (⚡ Fast & 💸 Cheap)](https://apify.com/scrapestorm/youtube-transcript-short-scraper-fast-cheap)
- [YouTube Transcript Video Scraper 📝 (⚡ Fast & 💸 Cheap)](https://apify.com/scrapestorm/youtube-transcript-videos)
- [YouTube Email Extractor 📩📍 - Fast & cheap 💬⭐](https://apify.com/scrapestorm/youtube-email-extractor---fast-cheap)
- [YouTube Email & Phone Scraper 📧📞](https://apify.com/scrapestorm/youtube-email-phone-scraper)
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- [YouTube Community Posts Scraper 🚀📄💬](https://apify.com/scrapestorm/youtube-community-posts-scraper)
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***

You can even apply sentiment analysis on the data text we've extracted! 😃📊:

- [Sentiment Analysis Text Classification 📈](https://apify.com/scrapestorm/sentiment-analysis-text-classification)

### ⚙️ Input Configuration

#### 📥 Input Example

```
{
  "urls": [
    "https://www.semanticscholar.org/search?q=AI&sort=relevance",
    "https://www.semanticscholar.org/search?q=machine+learning&sort=relevance"
  ],
  "maxItemsPerUrl": 30
}
```

#### Input Fields

| Field              | Type    | Description                                                 |
| ------------------ | ------- | ----------------------------------------------------------- |
| **urls**           | array   | One or more Semantic Scholar search URLs                    |
| **maxItemsPerUrl** | integer | Maximum number of papers to collect per URL (0 = unlimited) |

***

### 📤 Output Example

```
  {
    "paperId": "530a059cb48477ad1e3d4f8f4b153274c8997332",
    "title": "Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI",
    "authors": "Alejandro Barredo Arrieta, Natalia Díaz Rodríguez, Francisco Herrera",
    "fieldsOfStudy": "Computer Science, Philosophy",
    "venue": "Information Fusion",
    "pubDate": "22 October 2019",
    "citationCount": 7,
    "pdfUrl": "https://arxiv.org/pdf/1910.10045.pdf",
    "abstract": "Previous efforts to define explainability in Machine Learning are summarized, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought, and a taxonomy of recent contributions related to the explainability of different Machine Learning models are proposed.",
    "paperUrl": "https://www.semanticscholar.org/paper/Explainable-Artificial-Intelligence-(XAI)%3A-and-AI-Arrieta-Rodr%C3%ADguez/530a059cb48477ad1e3d4f8f4b153274c8997332",
    "sourceUrl": "https://www.semanticscholar.org/search?q=AI&sort=relevance"
  },
  {
    "paperId": "81600fd653a828d69f6160705be6814dd101beb7",
    "title": "From local explanations to global understanding with explainable AI for trees",
    "authors": "Scott M. Lundberg, G. Erion, Su-In Lee",
    "fieldsOfStudy": "Computer Science, Mathematics",
    "venue": "Nature Machine Intelligence",
    "pubDate": "1 January 2020",
    "citationCount": 6,
    "pdfUrl": "https://doi.org/10.1038/s42256-019-0138-9",
    "abstract": "An explanation method for trees is presented that enables the computation of optimal local explanations for individual predictions, and the authors demonstrate their method on three medical datasets.",
    "paperUrl": "https://www.semanticscholar.org/paper/From-local-explanations-to-global-understanding-AI-Lundberg-Erion/81600fd653a828d69f6160705be6814dd101beb7",
    "sourceUrl": "https://www.semanticscholar.org/search?q=AI&sort=relevance"
  }
```

***

### 📊 Preconfigured Dataset Views

🔎 Overview View

Quick summary of collected papers.

Fields included:

Paper ID

Title

Year

Authors

Citation Count

Source URL

📚 Detailed View

Complete research metadata for each paper.

Fields included:

Paper ID

Title

Year

Authors

Venue

Journal Name

Journal Volume

Journal Pages

Fields of Study

Citation Count

TLDR summary

DOI

DOI URL

PDF URL

Paper URL

Open Access status

Abstract

Source URL

🌐 By Source URL View

Groups papers based on the search URL they were scraped from.

Fields included:

Source URL

Paper ID

Title

Year

Authors

Citation Count

Paper URL

***

### 🌍 Why Use This Scraper?

📚 Literature Reviews
Collect large sets of papers for academic research.

🤖 AI Research Monitoring
Track new publications in fields like Machine Learning, NLP, and Computer Vision.

📊 Citation Analysis
Analyze citation counts and identify influential research.

🏫 Academic Dataset Creation
Build datasets for bibliometric analysis or research trend analysis.

🔬 Scientific Trend Tracking
Monitor emerging topics across scientific publications.

***

### Disclaimer

This scraper is an independent automation tool and is not affiliated with, endorsed by, or sponsored by Student.com.

***

### 📫 Support & Contact

😊 Leave a **5-star rating ⭐⭐⭐⭐⭐** if you’re satisfied

🌪️ **Storm Scraper**
<https://apify.com/scrapestorm>

For questions, feature requests, or custom scraping solutions, contact us directly via **Apify** or email.

# Actor input Schema

## `urls` (type: `array`):

One or more Semantic Scholar search URLs.

Examples:
• https://www.semanticscholar.org/search?q=AI\&sort=relevance
• https://www.semanticscholar.org/search?q=machine+learning\&sort=relevance
• https://www.semanticscholar.org/search?q=deep+learning\&sort=Relevance\&fos\[]=Computer+Science

## `maxItemsPerUrl` (type: `integer`):

Maximum number of papers to collect per URL. Set to 0 for no limit.

## `proxy` (type: `object`):

Optional.

## Actor input object example

```json
{
  "urls": [
    "https://www.semanticscholar.org/search?q=AI&sort=relevance",
    "https://www.semanticscholar.org/search?q=machine+learning&sort=relevance"
  ],
  "maxItemsPerUrl": 30,
  "proxy": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ],
    "apifyProxyCountry": "US"
  }
}
```

# Actor output Schema

## `overview` (type: `string`):

No description

## `detailed` (type: `string`):

No description

## `by_source` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "urls": [
        "https://www.semanticscholar.org/search?q=AI&sort=relevance",
        "https://www.semanticscholar.org/search?q=machine+learning&sort=relevance"
    ],
    "maxItemsPerUrl": 30,
    "proxy": {
        "useApifyProxy": true,
        "apifyProxyGroups": [
            "RESIDENTIAL"
        ],
        "apifyProxyCountry": "US"
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("scrapestorm/semantic-scholar-scraper---cheap").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "urls": [
        "https://www.semanticscholar.org/search?q=AI&sort=relevance",
        "https://www.semanticscholar.org/search?q=machine+learning&sort=relevance",
    ],
    "maxItemsPerUrl": 30,
    "proxy": {
        "useApifyProxy": True,
        "apifyProxyGroups": ["RESIDENTIAL"],
        "apifyProxyCountry": "US",
    },
}

# Run the Actor and wait for it to finish
run = client.actor("scrapestorm/semantic-scholar-scraper---cheap").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "urls": [
    "https://www.semanticscholar.org/search?q=AI&sort=relevance",
    "https://www.semanticscholar.org/search?q=machine+learning&sort=relevance"
  ],
  "maxItemsPerUrl": 30,
  "proxy": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ],
    "apifyProxyCountry": "US"
  }
}' |
apify call scrapestorm/semantic-scholar-scraper---cheap --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,scrapestorm/semantic-scholar-scraper---cheap"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/sQgbMOBCDuajQBRC3/builds/3bZd9ZZQDgmT0CfTP/openapi.json