How to Scrape Google Scholar With Python?

Google Scholar is a search engine for accessing scholarly data. With Google Scholar, you can retrieve scientific articles, research papers, and dissertations. However, academic research often requires collecting and analyzing large amounts of data from Google Scholar search results.

Manually wading through countless results is a daunting task. That's why a reliable Google Scholar scraper can help make the process less painful. With automation, you can scrape Google Scholar to extract data such as titles, authors, and citations from each result on a Google Scholar page in seconds.

In this tutorial, you'll learn how to build an effective Google Scholar scraper by making HTTP requests using the Scrapeless Google Scholar API and Python.

Keep scrolling to learn more!

🎓 What is Google Scholar Scraper?

A Google Scholar scraper is a tool designed to extract publicly available academic data from Google Scholar, such as research papers, citations, authors, and publication details. It enables researchers, academics, and organizations to gather valuable insights for analysis, trend tracking, and academic research. However, web scraping Google Scholar comes with significant challenges due to its robust anti-scraping mechanisms.

Why Is Data on Google Scholar Valuable?

• Review and Research: Find papers, articles, theses, and books related to academic research or projects. Compare different methods and theoretical frameworks.
• Academic Analysis: Identify emerging trends and topics in academic publications and calculate academic metrics such as H-index and citation counts.
• Potential Collaboration: Identify experts in a specific field for potential collaborations, conferences, or peer reviews.
• Product Development: R&D professionals can scrape Google Scholar for in-depth research, breakthroughs, and to track competitor publications in related scientific or technological fields.

Google Scholar Crawling Challenges and Solutions

Google Scholar is a powerful academic search engine that provides a large number of academic papers, patents, books, and conference articles. However, scraping Google Scholar data faces many technical and legal challenges. Here are the main problems you may encounter when web scraping Google Scholar and their solutions:

In addition, there are API restrictions for scraping Google Scholar. Since Google Scholar does not have an official API, web scraping Google Scholar requires directly parsing web pages, which increases complexity and instability.

Fortunately, you can try using powerful third-party API services. They guarantee convenient, fast, and accurate data extraction. Moreover, among many API service providers, Scrapeless also has built-in CAPTCHA decoding service, rotation proxy, and web unlocker.

Join Community and get the Free Trial!

Step by Step: Build Your Python Google Scholar Scraper

Next we will start crawling Google Scholar using Python Google Scholar scraper. You will see how to get data such as article title, publication information, and article title.

Step 1. Configure the environment

• Python: The software https://www.python.org/downloads/ is the core of running Python. You can download the version we need from the official website as shown below. However, it is not recommended to download the latest version. You can download 1.2 versions before the latest version.

• Python IDE: Any IDE that supports Python will work, but we recommend PyCharm. It is a development tool specifically designed for Python. For the PyCharm version, we recommend the free PyCharm Community Edition.

Note: If you are a Windows user, do not forget to check the "Add python.exe to PATH" option during the installation wizard. This will allow Windows to use Python and commands in the terminal. Since Python 3.4 or later includes it by default, you do not need to install it manually.

Now you can check if Python is installed by opening the terminal or command prompt and entering the following command:

python --version

Step 2. Install Dependencies

It is recommended to create a virtual environment to manage project dependencies and avoid conflicts with other Python projects. Navigate to the project directory in the terminal and execute the following command to create a virtual environment named google_scholar_env:

python -m venv google_scholar_env

Activate the virtual environment based on your system:

• Windows:

google_scholar_env\Scripts\activate

• MacOS/Linux:

source google_scholar_env/bin/activate

After activating the virtual environment, install the required Python libraries for web scraping. The library for sending requests in Python is requests, and the main library for scraping data is BeautifulSoup4. Install them using the following commands:

pip install requests
pip install beautifulsoup4

Step 3. Scrape Data

Open Google Scholar in your browser and search for "biology". Below is the search result:

• Scrape Titles:

Parse the relevant HTML page elements. The detailed Python code is as follows:

def scrape_scholar_title(listing):
    title_element = listing.select_one('h3.gs_rt a')
    return title_element .text.strip()

• Scrape Publication Information:

The publication information can be directly scraped using the div class attribute. The detailed Python code is as follows:

 def scrape_scholar_publication_info(listing):
    publication_info_element = listing.select_one('div.gs_a')
    return publication_info_element .text.strip()

• Scrape Article Snippets:

The article snippets can also be directly scraped using the div class attribute. The detailed Python code is as follows:

def scrape_scholar_snippet(listing):
    snippet_element = listing.select_one('div.gs_rs')
    return snippet_element  .text.strip()

Since we need to scrape all the data on the page, not just one, we need to loop through and scrape the above data. The complete code is as follows:

# Import necessary libraries
import time

import requests
from bs4 import BeautifulSoup
import json

# Function to scrape listing elements from google_scholar
def scrape_listings(soup):
    return soup.select('div.gs_r.gs_or.gs_scl')

# Function to scrape title from google_scholar
def scrape_scholar_title(listing):
    title_element = listing.select_one('h3.gs_rt > a')
    print(title_element.text)
    return title_element.text.strip()

# Function to scrape publication_info from google_scholar
def scrape_scholar_publication_info(listing):
    publication_info_element = listing.select_one('div.gs_a')
    print(publication_info_element.text)
    return publication_info_element.text.strip()

# Function to scrape snippet from google_scholar
def scrape_scholar_snippet(listing):
    snippet_element = listing.select_one('div.gs_rs')
    print(snippet_element.text)
    return snippet_element.text.strip()

# Main function
def main():
    # Make a request to google_scholar URL and parse HTML
    url = 'https://scholar.google.com/scholar?hl=en&q=biology'
    response = requests.get(url, verify=False)
    time.sleep(2)
    soup = BeautifulSoup(response.text, 'html.parser')

    # Scrape scholar listings
    listings = scrape_listings(soup)
    print(listings)

    # Iterate through each listing and extract scholar information
    scholar_data = []
    for listing in listings:
        title = scrape_scholar_title(listing)
        publication_info = scrape_scholar_publication_info(listing)
        snippet = scrape_scholar_snippet(listing)

        # Store scholar information in a dictionary
        scholar_info = {
            'title': title,
            'publication_info': publication_info,
            'snippet': snippet
        }

        scholar_data.append(scholar_info)

    # Save results to a JSON file
    with open('google_scholar_data.json', 'w') as json_file:
        json.dump(scholar_data, json_file, indent=4)

if __name__ == "__main__":
    main()

Step 4. Output Results

A file named google_scholar_data.json will be generated in your PyCharm directory. The output is as follows:

[
    {
        "title": "A new biology for a new century",
        "publication_info": "CR Woese\u00a0- Microbiology and molecular biology reviews, 2004 - Am Soc Microbiol",
        "snippet": "\u2026 molecular biology's lead \u2026 in biology that 20th century biology, molecular biology, could not \nhandle and, so, avoided. The former course, though highly productive, is certain to turn biology \u2026"
    },
    {
        "title": "Biology",
        "publication_info": "PH Raven, GB Johnson, KA Mason - 2011 - thuvienso.hoasen.edu.vn",
        "snippet": "\u2026 25.1 Overview of Evolutionary Developmental Biology 492 25.2 One or Two Gene \nMutations, New Form 495 25.3 Same Gene, New Function 496 25.4 Different Genes\u00a0\u2026"
    },
    {
        "title": "General biology",
        "publication_info": "R Fayer\u00a0- Cryptosporidium and cryptosporidiosis, 2007 - taylorfrancis.com",
        "snippet": "Some species of Cryptosporidium infect many host species, whereas others appear restricted \nto groups such as rodents or ruminants, and others are known to infect only one host \u2026"
    },
    {
        "title": "The molecular biology of coronaviruses",
        "publication_info": "PS Masters\u00a0- Advances in virus research, 2006 - Elsevier",
        "snippet": "Coronaviruses are large, enveloped RNA viruses of both medical and veterinary importance. \nInterest in this viral family has intensified in the past few years as a result of the \u2026"
    },
    {
        "title": "Biology",
        "publication_info": "SS Mader - 2010 - thuvienso.hoasen.edu.vn",
        "snippet": "\u2026 Comparative Animal Biology 576 \u2026 1.1 How to Define Life 2 1.3 Evolution, the Unifying \nConcept of Biology 6 1.3 How the Biosphere Is Organized 9 1.4 The Process of Science 11\u00a0\u2026"
    },
    {
        "title": "Sealice on salmonids: their biology and control",
        "publication_info": "AW Pike, SL Wadsworth\u00a0- Advances in parasitology, 1999 - Elsevier",
        "snippet": "\u2026 This review examines the voluminous literature on the biology and control of sealice and \nbrings together ideas for developing our knowledge of these organisms. Research on the \u2026"
    },
    {
        "title": "Biology data book",
        "publication_info": "PL Altman, DS Dittmer - 1972 - bionumbers.hms.harvard.edu",
        "snippet": "Embryos were raised at constant temperature in circulating nalis\" u 10% smaller. For \nadditional information on salmowater, from three hours after fertilization. Age= time from nids, \u2026"
    },
    {
        "title": "The biology of Pseudocalanus",
        "publication_info": "CJ Corkett, IA McLaren\u00a0- Advances in marine biology, 1979 - Elsevier",
        "snippet": "Publisher Summary Pseudocalanus is typical of most crustaceans in that after hatching at \nan early stage of development it adds successively new segments and appendages. \u2026"
    },
    {
        "title": "Introduction to a submolecular biology",
        "publication_info": "A Szent-Gyorgyi - 2012 - books.google.com",
        "snippet": "\u2026 Biology is the science of the improbable and I think it is on principle that the body works \nonly with reactions which are statistically improbable. If metabolism were built of a series of \u2026"
    },
    {
        "title": "The biology of mycorrhiza.",
        "publication_info": "JL Harley - 1959 - cabidigitallibrary.org",
        "snippet": "Since Dr. Rayner published her book on mycorrhiza in 1927 there has not been a comprehensive \naccount of this subject, although the need for a critical re-appraisal of the extensive \u2026"
    }
]

Deploy Scrapeless Google Scholar API Easily

Why is Scrapeless Scholar API crucial?

Absolutely! You just need an API service that is affordable, stable, and secure. However, finding one that meets all these criteria is incredibly challenging! Fortunately, Scrapeless Google Scholar API stands out among many API products:

• 🔴 Cost-saving: Google Scholar API only needs $0.80, and with a $49 subscription, you get a 10% discount!
• 🔴 Accurate Data: Our developers constantly analyze Google's scraping algorithms and restrictions to ensure the API is updated and optimized.
• 🔴 Stable and High Success Rate: Scrapeless guarantees a 99% success rate and reliability. The stability and accuracy of Google Trends scraping have reached nearly 100%! Currently, the average response time is around 3 seconds, significantly faster than most API providers. Moreover, data is returned in a standardized JSON format, making it ready for immediate use.

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Further Reading:

• How to Scrape Google Search Results?
• How to Scrape Google Trends with Python?
• Get The Cheapest Flight with Google Flights Scraper!

Using steps:

Step 1. Get API Token

1. Log in to the Dashboard.
2. Navigate to API Key Management.
3. Click Create to generate your unique API Key.
4. You just need to simply click on the API Key to copy it.

Step 2: Use Your API Key in the Code

Now you only need to configure the parameters in the API documentation to scrape the required Google Scholar data.

1. Visit the API Documentation.
2. Click "Try it out" for the desired endpoint.
3. Configure the parameters you need in the code body.

• Replace the keyword q with the one you want to query.
• The engine parameter is mandatory, and its value must be google_scholar. However, you can add more specific parameters, such as google_scholar_author.
• Common parameters:

Parameter	Required	Description
engine	TRUE	Set to google_scholar to use this API.
q	TRUE	Search query (e.g., "machine learning").
cites	FALSE	Unique ID to find citing articles.
as_ylo	FALSE	Filter results from a specific year.
as_yhi	FALSE	Filter results up to a specific year.
hl	FALSE	Language setting (default: en).
num	FALSE	Number of results (1-20, default: 10).

1. Enter your API Key in the "Auth" field.
2. Click "Send" to get the scraping response.

Scrapeless Google Scholar also supports crawling:

• Google Scholar Author
• Google Scholar Cite
• Google Scholar Profiles

You can also directly integrate our reference code into your program. Just replace your_token with the token you applied for:

import json
import requests


class Payload:
    def __init__(self, actor, input_data):
        self.actor = actor
        self.input = input_data


def send_request():
    host = "api.scrapeless.com"
    url = f"https://{host}/api/v1/scraper/request"
    token = your_token ## replace with your API Token

    headers = {
        "x-api-token": token
    }

    input_data = {
        "engine": "google_scholar",
        "q": "biology",
    }

    payload = Payload("scraper.google.scholar", input_data)

    json_payload = json.dumps(payload.__dict__)

    response = requests.post(url, headers=headers, data=json_payload)

    if response.status_code != 200:
        print("Error:", response.status_code, response.text)
        return

    print("body", response.text)


if __name__ == "__main__":
    send_request()

Build Your Google Scholar Scraper Now!

Scraping Google Scholar is a great way to extract academic information. Whether you're looking for code or no-code ways to scrape Google Scholar or other search engines, we have a simple and fast solution for you.

Scrapeless offers a one-month free trial, where you can enjoy all the services to collect data. How to find detailed data from Google Scholar? You can collect a lot of data in a very short time with Scrapeless.