How to Scrape Google Scholar with ProxyTee and Python

Google Scholar is a valuable resource for accessing academic data, including scientific articles, research papers, and theses. However, manually sifting through endless results can be time-consuming. Automating this process allows you to quickly extract key data like titles, authors, and citations. In this guide, you’ll learn how to scrape Google Scholar using ProxyTee’s robust network of Residential Proxies and Python.

Why Use ProxyTee for Google Scholar Scraping?

ProxyTee offers a suite of features that make it an ideal choice for web scraping, especially when dealing with academic sites like Google Scholar:

1. Unlimited Bandwidth: Enjoy uninterrupted data collection without worrying about data overages. ProxyTee's Unlimited Residential Proxies provide all the bandwidth you need.
2. Extensive Global Coverage: Access over 20 million IP addresses from more than 100 countries, allowing you to scrape location-specific data efficiently. ProxyTee ensures broad geographical reach.
3. Multiple Protocol Support: Utilize both HTTP and SOCKS5 protocols for compatibility with a range of scraping tools and applications. This flexibility allows seamless integration into your existing setup, particularly with Residential Proxies.
4. Auto Rotation: Protect your anonymity and prevent bans with IP auto-rotation. Configure rotation intervals to match your needs, a critical feature for efficient web scraping. ProxyTee dynamically changes your IP address at regular intervals, minimizing the risk of getting detected by Google Scholar.
5. Simple API: The straightforward API allows seamless integration with your existing applications, streamlining your workflows, especially when automating tasks. See more about our API integration.

Setting Up Your Environment

Before you start, ensure you have Python installed. You can download it from the official website. Then, create a Python file (e.g., main.py) in your project directory. Open the file in your preferred code editor and continue following the tutorial:

Installing Dependencies

You’ll need the `requests` and `Beautiful Soup` libraries. Run this command in your terminal:

pip3 install requests bs4

Writing the Code

Here is a step-by-step guide on scraping Google Scholar:

1. Import Libraries

import requests
from bs4 import BeautifulSoup

2. Preparing the API Request

To make a request, use ProxyTee, you need the proxy URL and port. You also need a Google Scholar page URL you want to scrape. For demonstration, we will use global warming as an example

proxy_url = "YOUR_PROXY_URL"
proxy_port = "YOUR_PROXY_PORT"
url = "https://scholar.google.com/scholar?q=global+warming+&hl=en&as_sdt=0,5"

3. Retrieving HTML

Here is the `get_html_for_page()` function to send request using ProxyTee Residential Proxy.

def get_html_for_page(url):
    proxies = {
        "http": f"http://{proxy_url}:{proxy_port}",
        "https": f"http://{proxy_url}:{proxy_port}",
    }

    try:
        response = requests.get(url, proxies=proxies)
        response.raise_for_status()
        return response.content
    except requests.exceptions.RequestException as e:
        print(f"Error during request: {e}")
        return None

4. Parsing the Data

The `parse_data_from_article()` function will extract details for each article

def parse_data_from_article(article):
    title_elem = article.find("h3", {"class": "gs_rt"})
    title = title_elem.get_text()
    title_anchor_elem = article.select("a")[0]
    url = title_anchor_elem["href"]
    article_id = title_anchor_elem["id"]
    authors = article.find("div", {"class": "gs_a"}).get_text()

    return {
        "title": title,
        "authors": authors,
        "url": url,
        "citations": get_citations(article_id),
    }

5. Getting Citations

The `get_citations` function retrieves citation details for each article by sending another API call

def get_citations(article_id):
    url = f"https://scholar.google.com/scholar?q=info:{article_id}:scholar.google.com&output=cite"
    html = get_html_for_page(url)
    soup = BeautifulSoup(html, "html.parser")
    data = []

    for citation in soup.find_all("tr"):
        title = citation.find("th", {"class": "gs_cith"}).get_text(strip=True)
        content = citation.find("div", {"class": "gs_citr"}).get_text(strip=True)
        entry = {"title": title, "content": content}
        data.append(entry)
    return data

6. Scraping Multiple Pages

Use the following `get_url_for_page` and `get_data_from_page` to navigate and fetch data from multiple pages of search results from Google Scholar:

def get_url_for_page(url, page_index):
    return url + f"&start={page_index}"

def get_data_from_page(url):
    html = get_html_for_page(url)
    if not html:
        return []
    soup = BeautifulSoup(html, "html.parser")
    articles = soup.find_all("div", {"class": "gs_ri"})
    return [parse_data_from_article(article) for article in articles]

7. Main Script

Here’s the main loop for your scraping process:

if __name__ == "__main__":

    data = []
    url = "https://scholar.google.com/scholar?q=global+warming+&hl=en&as_sdt=0,5"
    NUM_OF_PAGES = 2
    page_index = 0

    for _ in range(NUM_OF_PAGES):
        page_url = get_url_for_page(url, page_index)
        entries = get_data_from_page(page_url)
        data.extend(entries)
        page_index += 10

    print(data)

Conclusion

Using ProxyTee and Python is an effective method for collecting structured data from Google Scholar. ProxyTee’s Unlimited Residential Proxies, with their unlimited bandwidth, global IP coverage, and flexible rotation options, provide a reliable infrastructure for your data needs. See how easy it is to start collecting valuable information with ProxyTee. Check our pricing plans now.