Scrape academic papers by keyword

Scraping academic papers by keyword involves extracting metadata or full-text content of papers from academic databases or repositories based on specific search terms. However, due to legal, ethical, and technical considerations, direct scraping is often discouraged or restricted by websites’ terms of service. Instead, most academic platforms provide APIs or search tools designed for programmatic access.

Here’s a guide on how to collect academic papers by keyword in a responsible and effective way:

1. Use Official APIs of Academic Databases

Many academic databases offer APIs that allow you to query papers by keywords and retrieve metadata or abstracts legally:

• Semantic Scholar API
  Free access to metadata, abstracts, citations for millions of papers.
  Example: Search papers by keyword and get JSON responses.
  https://api.semanticscholar.org/

• arXiv API
  Open access repository for preprints mainly in physics, math, computer science, etc.
  https://arxiv.org/help/api/

• CrossRef API
  Access to DOI metadata across a broad spectrum of journals.
  https://api.crossref.org/

• PubMed API (Entrez Programming Utilities)
  Biomedical literature database with a powerful API for keyword searches.
  https://www.ncbi.nlm.nih.gov/home/develop/api/

2. Using Python Libraries for API Access

Python is commonly used to automate keyword-based searches through these APIs.

Example: Using Semantic Scholar API with Python

python
CopyEdit
import requests

def search_papers(keyword, limit=10):
    url = f"https://api.semanticscholar.org/graph/v1/paper/search?query={keyword}&limit={limit}&fields=title,authors,year,abstract,url"
    response = requests.get(url)
    data = response.json()
    papers = data.get('data', [])
    for paper in papers:
        print(f"Title: {paper['title']}")
        print(f"Authors: {[author['name'] for author in paper['authors']]}")
        print(f"Year: {paper['year']}")
        print(f"Abstract: {paper['abstract'][:200]}...")
        print(f"URL: {paper['url']}")
        print("-" * 80)

search_papers("machine learning", limit=5)

3. Using Scholarly Libraries

• scholarly (Python package) for Google Scholar data extraction (limited and slower due to Google restrictions).

• arxiv Python package to search arXiv papers by keyword.

4. Considerations & Ethics

• Respect robots.txt and API usage policies. Avoid unauthorized scraping.

• Rate limit your requests to prevent overloading servers.

• Use official APIs whenever possible.

• Cite data sources properly.

• Avoid downloading full papers unless they are open access. For paywalled papers, only retrieve metadata.

Summary

Scraping academic papers by keyword is best done using official APIs and open repositories. Python tools like requests, scholarly, or arxiv libraries enable automation of this process. Avoid unauthorized web scraping to stay within legal and ethical boundaries.

If you want, I can help write a detailed tutorial or script for a specific academic API or repository. Just let me know which one!