Did you know that learning how to scrape Google Scholar can supercharge your research papers? This search engine is a gold mine of citations and scholarly articles that you could be analyzing at scale with a web scraper. With a reliable scraping service like ScrapingBee and some basic Python, you can automate repetitive research tasks more efficiently.
Why ScrapingBee, you may ask? Well, let’s get one thing straight – Google Scholar has tight anti-scraping measures. It means that you need a reliable Google Scholar scraper that can handle IP bans, annoying CAPTCHAs, and JavaScript rendering. Our web scraper is built with all these features, allowing you to scrape Google Scholar data without coding everything from scratch.
So, in this guide, I'll walk you through extracting article titles, authors, and links using Python and our web scraper API. By the end of it, you’ll have an efficient solution to gather scholarly data without worrying about constant blocks and bans
Quick Answer (TL;DR)
If you are eager to start analyzing your scraped data, ScrapingBee provides an API that simplifies the process of fetching Google Scholar search results, though parsing and data handling still require some setup in Python.
With our Google Scholar API, you get customized API parameters that mimic real browsers, enable JavaScript rendering, and use residential proxies.
Here’s a complete code snippet that scrapes Google Scholar with just a few lines of Python:
import requests
from urllib.parse import quote_plus
from bs4 import BeautifulSoup
# Your ScrapingBee API key
api_key = "YOUR_API_KEY"
# The search query you want to run on Google Scholar
search_query = "machine learning"
encoded_query = quote_plus(search_query)
# Construct the Google Scholar URL
google_scholar_url = f"https://scholar.google.com/scholar?q={encoded_query}"
# Parameters for ScrapingBee
params = {
'api_key': api_key,
'url': google_scholar_url,
'country_code': 'us',
'custom_google': 'true'
}
# Make the request
response = requests.get('https://app.scrapingbee.com/api/v1/', params=params)
# Check if the request was successful
if response.status_code == 200:
print("Success! Response received.")
# Parse the HTML content
soup = BeautifulSoup(response.content, 'html.parser')
# Extract article titles
titles = [el.text for el in soup.select('.gs_rt')]
# Extract author information
authors = [el.text for el in soup.select('.gs_a')]
# Extract links to papers
links = []
for title_element in soup.select('.gs_rt a'):
if 'href' in title_element.attrs:
links.append(title_element['href'])
# Print the results
for i in range(min(len(titles), len(authors))):
print(f"Title: {titles[i]}")
print(f"Authors: {authors[i]}")
if i < len(links):
print(f"Link: {links[i]}")
print("---")
else:
print(f"Error: {response.status_code}")
print(response.text)
If you wish to scrape Google, use the custom_google=True parameter. Each request will cost only 20 credits
Just replace YOUR_API_KEY with your actual ScrapingBee key, and you’re good to go.
If you need a much more detailed walkthrough on how to scrape Google Scholar, continue reading.
Set Up Your ScrapingBee Environment
Let’s begin with the basics – setting up your environment for web scraping. All you need to do is follow these 5 steps, and you’ll be ready to analyze Google Scholar data right away.
1. Create Your ScrapingBee Account
Head to the ScrapingBee website and click “Start Free Trial.” Sign up with your email and password—no credit card required. Once you verify your email, you’ll be taken to your dashboard with 1,000 free credits, ready to start extracting Google Scholar articles and citations.
2. Get Your API Key
Your API key connects your code to ScrapingBee’s proxy network. After logging in, you’ll find it on your dashboard. Make sure to copy and store the key securely. Never share it or commit it to version control. I once pushed mine to GitHub by mistake and had to revoke it fast to prevent misuse!
3. Install the Python SDK
Now let’s set up your Python environment with the necessary tools to scrape Google Scholar:
Open your terminal or command prompt.
It’s best to create a virtual environment for your project:
# Create a virtual environment python -m venv scraping_env # Activate on Windows scraping_env\Scripts\activate # Activate on macOS/Linux source scraping_env/bin/activateInstall the ScrapingBee Python SDK:
pip install scrapingbee
For a deeper dive into scraping techniques, be sure to explore our comprehensive Python Web Scraping Tutorial.
4. Install Required Libraries
Along with the ScrapingBee SDK, you’ll need a few additional installed libraries for a complete scraping toolkit:
pip install requests beautifulsoup4
This installs:
requests: for making HTTP requests to access the https://scholar.google.com scholar pages
beautifulsoup4: for parsing and navigating HTML content from the web page
5. Verify Your Google Scholar Scraper
Let’s make sure everything is working correctly with a simple test:
from scrapingbee import ScrapingBeeClient
# Initialize the client with your API key
client = ScrapingBeeClient(api_key='YOUR_API_KEY')
# Make a test request to a simple website
response = client.get('https://www.example.com')
# Check if it worked
if response.status_code == 200:
print("Success! Your ScrapingBee setup is working.")
print(f"Response size: {len(response.content)} bytes")
else:
print(f"Something went wrong. Status code: {response.status_code}")
Replace ‘YOUR_API_KEY’ with the actual key from your dashboard, run the script, and you should see a success message. Your Google Scholar scraper is now ready, and you can start scraping article data.
Make Your First Google Scholar API Request
This is where the fun begins. Let’s launch your first request to Google Scholar. In my experience, the trickiest part of scraping Google Scholar isn’t parsing the HTML; it’s getting past the site’s defenses to extract data.
But don’t worry. ScrapingBee makes it easy by handling IP rotation, JavaScript rendering, and CAPTCHA challenges for you.
Here’s how we’ll structure your request using ScrapingBee:
import requests
from urllib.parse import quote_plus
from bs4 import BeautifulSoup
# Your ScrapingBee API key
api_key = "YOUR_API_KEY"
# The search query you want to run on Google Scholar
search_query = "machine learning"
encoded_query = quote_plus(search_query)
# Construct the Google Scholar URL
google_scholar_url = f"https://scholar.google.com/scholar?q={encoded_query}"
# Parameters for ScrapingBee
params = {
'api_key': api_key,
'url': google_scholar_url,
'country_code': 'us',
'custom_google': 'true'
}
# Make the request
response = requests.get('https://app.scrapingbee.com/api/v1/', params=params)
# Check if the request was successful
if response.status_code == 200:
print("Success! Response received.")
# Parse the HTML content
soup = BeautifulSoup(response.content, 'html.parser')
# Extract article titles
titles = [el.text for el in soup.select('.gs_rt')]
# Extract author information
authors = [el.text for el in soup.select('.gs_a')]
# Extract links to papers
links = []
for title_element in soup.select('.gs_rt a'):
if 'href' in title_element.attrs:
links.append(title_element['href'])
# Print the results
for i in range(min(len(titles), len(authors))):
print(f"Title: {titles[i]}")
print(f"Authors: {authors[i]}")
if i < len(links):
print(f"Link: {links[i]}")
print("---")
else:
print(f"Error: {response.status_code}")
print(response.text)
Let me break down what’s happening here.
We’re constructing a URL for your Google Scholar scraper with our search parameters, then passing it to ScrapingBee along with some important API parameters.
Behind the scenes, our solution renders the JavaScript of the page using a real browser, which helps bypass many anti-bot measures. We have a comprehensive guide on Web Scraping with JavaScript for those interested in learning more.
When you run this code, ScrapingBee will send your request through its proxy network, render the page with a real browser, and return the HTML content containing Google Scholar articles, citations, and other data.
It’s like having a team of web scraping experts working for you behind the scenes!
Handling Pagination
One limitation of the basic approach is that it only gets the first page of results. Here’s how to handle pagination to get more comprehensive data:
import time
# Number of pages to scrape
num_pages = 3
results = []
for page in range(num_pages):
# Calculate the start parameter (10 results per page)
start = page * 10
# Construct the Google Scholar URL with pagination
paginated_url = f"https://scholar.google.com/scholar?q={encoded_query}&start={start}"
# Update the URL parameter
params['url'] = paginated_url
try:
# Make the request
response = requests.get('https://app.scrapingbee.com/api/v1/', params=params)
if response.status_code == 200:
print(f"Successfully scraped page {page+1}")
# Store the HTML content for later parsing
results.append(response.content)
else:
print(f"Error on page {page+1}: {response.status_code}")
print(response.text)
# Be nice to the service - add a delay between requests
time.sleep(5)
except Exception as e:
print(f"An exception occurred on page {page+1}: {e}")
# Continue with the next page even if one fails
continue
I’ve added a 5-second delay between requests to be respectful to both Google Scholar and ScrapingBee’s services.
Extract Article Data with BeautifulSoup
Now that we have our HTML content, we need to parse it to get specific data from the Google Scholar results.
This complete code brings out the BeautifulSoup:
from bs4 import BeautifulSoup
# Parse the HTML content
soup = BeautifulSoup(response.content, 'html.parser')
# Extract article titles
titles = [el.text for el in soup.select('.gs_rt')]
# Extract author information
authors = [el.text for el in soup.select('.gs_a')]
# Extract links to papers
links = []
for title_element in soup.select('.gs_rt a'):
if 'href' in title_element.attrs:
links.append(title_element['href'])
# Print the results
for i in range(min(len(titles), len(authors))):
print(f"Title: {titles[i]}")
print(f"Authors: {authors[i]}")
if i < len(links):
print(f"Link: {links[i]}")
print("---")
Google Scholar’s HTML structure uses specific CSS classes for different elements – .gs_rt for article titles, .gs_a for author information, and so on. I’ve found these selectors to be fairly stable, but Google occasionally changes their structure, so you might need to adjust them if you notice missing data.
If you prefer to export data to formats like JSON, CSV, or even Google Sheets, you'll need to take additional steps. Our data extraction documentation page covers everything from converting organic results into an HTML table to exporting structured data as a JSON file. So, check it out, if you need extra help.
Best Practices to Avoid Blocking
Even with ScrapingBee handling the heavy lifting, it’s good to follow some best practices to ensure reliable scraping of Google Scholar. From my experience scraping academic sources, here are some tips:
Respect rate limits. Don’t hammer Google Scholar with requests. Space them out, even when using ScrapingBee. I recommend at least 5-10 seconds between requests.
Rotate user-agents. ScrapingBee can handle this for you with the browser_headers parameter, which simulates different browsers.
Implement exponential backoff. If you encounter errors, wait longer before retrying. Start with a few seconds and double the wait time with each failure.
These practices work because they make your scraping behavior look more like a human user and less like an automated scraper. Google Scholar’s anti-bot systems look for patterns that indicate automation, so breaking those patterns is key to extracting data from Google Scholar successfully.
Power Up Your Research
By now, you’ve seen how just a few lines of Python, paired with ScrapingBee’s powerful API, can transform Google Scholar’s vast academic repositories into clean, structured data. This guide has laid the groundwork: from setting up your environment and handling pagination to parsing with BeautifulSoup.
Ready to dive in? Sign up for ScrapingBee, grab your API key, and with a single API call you’ll be harvesting titles, authors, links, and more. As you scale up, remember to pace your requests with respectful rate limiting and intelligent backoff.
Frequently Asked Questions (FAQs)
Is it legal to scrape Google Scholar?
Scraping Google Scholar exists in a gray area. Google’s Terms of Service prohibit scraping without permission, but many researchers do it for academic research purposes. I recommend scraping at a reasonable rate, using the data for non-commercial research, and consulting legal advice for your specific situation.
How does ScrapingBee handle CAPTCHA on Google Scholar?
ScrapingBee uses a combination of residential proxies and browser fingerprinting to bypass Captcha when you scrape Google. When CAPTCHAs do appear, ScrapingBee’s rendering engine can sometimes solve simple ones automatically. For more complex CAPTCHAs, the system uses proxy rotation to try different IP addresses until it finds one that doesn’t trigger the CAPTCHA, making it a reliable Google Scholar scraper.
Can I extract citation counts automatically?
Yes, you can extract citations and even h-index data from Google Scholar by targeting the .gs_fl class elements that contain citation information. Look for text containing “Cited by” followed by a number to find cite results. Keep in mind that parsing these accurately might require additional text processing since they’re often mixed with other information in the same element.
How many requests can I make per minute?
With ScrapingBee’s standard plan, you can make about 10-15 requests per minute to Google Scholar. However, I recommend staying on the conservative side with academic sources – 5-10 requests per minute is safer for long-term scraping and extracting Google Scholar data. Your specific limit depends on your ScrapingBee plan and the complexity of the pages you’re scraping.
Can I customize my Google Scholar scraper with parameters like hl=en or as_sdt=0,5?
Yes. You can include parameters like hl=en and as_sdt=0,5 in your https://scholar.google.com search URL. ScrapingBee will render the full HTML, letting you extract titles, links, and citations using Python. Just build your query, pass it into the API, and parse the data with BeautifulSoup.
Kevin worked in the web scraping industry for 10 years before co-founding ScrapingBee. He is also the author of the Java Web Scraping Handbook.