Google Scholar is a treasure trove of academic data for researchers, students, and analysts. With over 389 million records including journal articles, conference papers, preprints, theses, court opinions and more,^1 Google Scholar offers unparalleled coverage of scholarly literature across disciplines.
However, extracting and analyzing data from Google Scholar at scale can be challenging. While Google Scholar offers an advanced search interface, it doesn‘t provide an official API for programmatic access. Web scraping – the process of automatically collecting data from websites – allows researchers to unlock the full potential of Google Scholar data.
In this in-depth guide, we‘ll cover everything you need to know to scrape data from Google Scholar effectively and responsibly. We‘ll discuss the legal and ethical considerations, compare different technical approaches and tools, walk through a sample Python scraping project, and explore research applications and best practices. Let‘s dive in!
Why Scrape Google Scholar?
There are countless research questions you could investigate with data from Google Scholar, such as:
- Tracking the growth and evolution of research topics and fields over time^2
- Identifying the most influential papers, authors, and publication venues in a given domain^3
- Examining patterns of citation, co-authorship, and collaboration within and across disciplines^4
- Mapping semantic relationships between key concepts based on co-occurrence in abstracts or full text^5
- Spotting trends and gaps in the literature to inform future research directions^6
To illustrate, here is a chart showing the exponential growth of Google Scholar‘s database over the past decade:
| Year | Records |
|---|---|
| 2010 | 40 million |
| 2014 | 170 million |
| 2018 | 200 million |
| 2019 | 320 million |
| 2022 | 389 million |
Source: Wikipedia^1 and Internet Archive^7
As Google Scholar continues to expand its coverage, scraping allows scholars to tap into this rich data at a larger scale than manual searching and data entry would allow. A 2019 study found that using a custom web scraper to collect Google Scholar citation data enabled the analysis of citation networks for over 64,000 articles – a corpus over 30 times larger than previous studies relying on manual data collection.^8
Is It Legal to Scrape Google Scholar?
As is often the case with web scraping, the legal waters are a bit murky. Google Scholar‘s terms of service prohibit "automated queries of any kind," including the use of "robots, spiders, scrapers, or other technology to access, index, or retrieve data" without express permission.^9
However, the scholarly articles, books, and court opinions indexed by Google Scholar are generally not owned by Google itself, but by the original publishers and database providers. As a 2022 paper in Scientometrics notes, "Google does not have the authority to restrict access to the data it indexes in Google Scholar," and the scraped bibliographic metadata "cannot be considered proprietary to Google."^10
This means that scraping Google Scholar for non-commercial, research purposes is likely defensible under fair use, especially if you‘re only collecting metadata like publication names, authors, abstracts, and citation counts rather than full text. However, it‘s always a good idea to check with your institution‘s librarians, legal department, or IRB before starting a major scraping project. And take care to be a good citizen by spacing out your requests and avoiding anything that would overload Google‘s servers or disrupt the experience for other users.
Scraping Google Scholar with Python
While there are a number of no-code tools available for scraping Google Scholar like Octoparse or ParseHub, using Python and libraries like Beautiful Soup, Requests, Scrapy, and Selenium gives you the most power and flexibility to customize your scraping workflow.
Here‘s a quick example of using Python to scrape basic metadata from a Google Scholar search results page:
import requests
from bs4 import BeautifulSoup
def scrape_results(query):
base_url = ‘https://scholar.google.com/scholar‘
params = {
"q": query,
"hl": "en",
}
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.102 Safari/537.36"
}
response = requests.get(base_url, params=params, headers=headers)
soup = BeautifulSoup(response.content, ‘html.parser‘)
results = []
for result in soup.select(‘.gs_r.gs_scl‘):
title_link = result.select_one(‘.gs_rt a‘)
title = title_link.text
link = title_link[‘href‘]
snippet = result.select_one(‘.gs_rs‘).text
cited_by = result.select_one(‘#gs_res_ccl_mid .gs_nph+ a‘)
cited_by_url = f"https://scholar.google.com{cited_by[‘href‘]}" if cited_by else None
cited_by_count = int(cited_by.text.split()[-1]) if cited_by else 0
metadata = {
‘title‘: title,
‘link‘: link,
‘snippet‘: snippet,
‘cited_by_count‘: cited_by_count,
‘cited_by_url‘: cited_by_url
}
results.append(metadata)
return results
# example usage
query = "web scraping"
results = scrape_results(query)
for result in results:
print(f"{result[‘title‘]}\n{result[‘link‘]}\nCited by {result[‘cited_by_count‘]}\n")This simple script:
- Constructs the search URL with our query
- Fetches the search results page and parses the HTML with Beautiful Soup
- Loops through each result and extracts the title, URL, snippet, citation count, and cited by URL
- Returns a list of dictionaries containing the scraped metadata
We could easily modify this to loop through multiple pages of results, extract additional data points like authors and publication details, and save the results to a structured format like CSV or JSON for further analysis.
However, there are a few additional challenges we need to consider when scraping Google Scholar compared to a typical website:
Google is quite aggressive at detecting and blocking bots. We need to be careful to space out our requests, use realistic user agent strings, and potentially rotate our IP addresses using proxies.
Search result data is loaded dynamically with JavaScript, so a simple HTTP request may not be sufficient to scrape it. We can use a headless browser automation tool like Selenium to fully render the page.
Google may sometimes show a CAPTCHA to verify we‘re human, which can block our scraper. We can try to detect and solve these automatically using a CAPTCHA solving service.
Here‘s what a more production-grade Google Scholar scraper might look like using Scrapy and Selenium:
import scrapy
from scrapy_selenium import SeleniumRequest
class ScholarSpider(scrapy.Spider):
name = ‘scholar‘
def start_requests(self):
yield SeleniumRequest(
url=f‘https://scholar.google.com/scholar?hl=en&q={self.query}&num={self.num_results}‘,
wait_time=3,
screenshot=True,
callback=self.parse,
dont_filter=True,
)
def parse(self, response):
for result in response.selector.xpath(‘//*[@data-rp]‘):
yield {
‘title‘: result.xpath(‘.//h3/a//text()‘).get(),
‘link‘: result.xpath(‘.//h3/a/@href‘).get(),
‘snippet‘: result.xpath(‘.//*[@class="gs_rs"]//text()‘).get(),
‘cited_by_url‘: f‘https://scholar.google.com{result.xpath(".//*[@id=\‘gs_res_ccl_mid\‘]/div/a/@href").get()}‘,
‘cited_by_count‘: result.xpath(‘//*[@id="gs_res_ccl_mid"]//a//text()‘).re_first(r‘Cited by (\d+)‘),
‘authors‘: result.xpath(‘.//div[@class="gs_a"]//text()‘).getall(),
‘pub_year‘: result.xpath(‘.//div[@class="gs_a"]/text()‘).re_first(r‘\b(\d{4})\b‘),
}
next_page = response.selector.xpath(‘//td[@align="left"]/a/@href‘).get()
if next_page:
yield SeleniumRequest(
url=f‘https://scholar.google.com{next_page}‘,
wait_time=3,
screenshot=True,
callback=self.parse
)This Scrapy spider:
- Uses Selenium to render the search results page, waiting a few seconds for the dynamic data to load
- Yields a SeleniumRequest for each page of results, allowing us to paginate through a large result set
- Parses additional fields like authors and publication year using Scrapy‘s built-in support for XPath selectors
With a bit more polish, such as handling errors, retrying failures, filtering duplicates, and exporting to a database, this could form the basis of a robust and maintainable scholarly data pipeline.
Analyzing Google Scholar Data
Once you‘ve scraped a sizable dataset from Google Scholar, the real fun begins! Here are a few examples of analyses you could run:
Use network analysis libraries like NetworkX to construct and visualize a citation graph, identifying key papers and authors based on centrality measures
Apply natural language processing techniques like topic modeling (LDA), word embeddings (word2vec), or named entity recognition (BERT) to the abstracts to discover latent themes and trends^11
Train machine learning models to predict citation counts or identify "sleeping beauty" papers that take off after a period of dormancy^12
Integrate with other datasets like author profiles, journal rankings, or altmetrics to enrich your analysis
The possibilities are endless, but the key is having a clear research question and using the appropriate statistical and visualization techniques to extract meaningful insights from your Google Scholar data.
Alternative: Using a Google Scholar API
If the prospect of building and maintaining your own Google Scholar scrapers sounds daunting, you might consider using a pre-built Google Scholar API service. These handle the technical complexities of scraping, parsing, and formatting the data behind the scenes, providing you with structured JSON data through a simple RESTful interface.
Some popular Google Scholar API options include:
Serpapi: Offers a free plan for up to 100 searches per month, with paid plans for higher volume. Supports searching by query, author, publication, and more.
Apify: Provides a configurable Google Scholar scraper actor that can be scheduled to run automatically. Offers 10k free monthly credits.
Science Parse: A more targeted API focused on parsing structured paper data from Google Scholar, including authors, abstracts, citations, and PDFs. Provides a free trial.
Using an API can save you development time and effort, but be sure to check the terms of service and pricing carefully. Some services may have restrictions on data storage, sharing, and publishing that could impact your research use case.
Conclusion
Google Scholar is an unparalleled source of rich, structured data on scholarly publications and citations across fields. Web scraping provides a powerful tool for researchers to access and analyze this data at scale, enabling new types of bibliometric and scientometric studies.
However, scraping Google Scholar is not without its technical and ethical challenges. By using appropriate tools like Python libraries and APIs, following best practices around politeness and reproducibility, and engaging with your scholarly community around the responsible use of scraped data, you can unlock valuable insights while minimizing risk.
I hope this guide has given you a solid foundation to start your own Google Scholar scraping projects. Here are a few additional resources to dive deeper:
- Scholarly: A Python package that simplifies accessing author and publication data from Google Scholar
- SAGE Ocean Guide to Web Scraping: A comprehensive tutorial on scraping for social science research
- Connected Papers: A visual tool for exploring citation graphs powered by data from Google Scholar and Microsoft Academic Graph
As always, feel free to reach out if you have any questions or want to share your own experiences scraping Google Scholar. Happy researching!