google-scholar-scrapy-spider

Python Scrapy spider that searches Google Scholar for a particular keyword and extracts all search results from the product page. The spider will iterate through all pages returned by the keyword query. The following are the fields the spider scrapes for the Google Scholar search results page:

• Title
• Link
• Citations
• Related Links
• Number of Verions
• Author
• Publisher
• Snippet

This Google Scholar spider uses Scraper API as the proxy solution. Scraper API has a free plan that allows you to make up to 1,000 requests per month which makes it ideal for the development phase, but can be easily scaled up to millions of pages per month if needs be.

To monitor the scraper, this scraper uses ScrapeOps. Live demo here: ScrapeOps Demo

Using the Google Scholar Spider

Make sure Scrapy is installed:

pip install scrapy

Set the keywords you want to search in Google Scholar.

queries = ['airbnb', 'covid-19']

Setting Up ScraperAPI

Signup to Scraper API and get your free API key that allows you to scrape 1,000 pages per month for free. Enter your API key into the API variable:

API_KEY = '<YOUR_API_KEY>'

def get_url(url):
    payload = {'api_key': API_KEY, 'url': url, 'country_code': 'us'}
    proxy_url = 'http://api.scraperapi.com/?' + urlencode(payload)
    return proxy_url

By default, the spider is set to have a max concurrency of 5 concurrent requests as this the max concurrency allowed on Scraper APIs free plan. If you have a plan with higher concurrency then make sure to increase the max concurrency in the settings.py.

## settings.py

CONCURRENT_REQUESTS = 5
RETRY_TIMES = 5

# DOWNLOAD_DELAY
# RANDOMIZE_DOWNLOAD_DELAY

We should also set RETRY_TIMES to tell Scrapy to retry any failed requests (to 5 for example) and make sure that DOWNLOAD_DELAY and RANDOMIZE_DOWNLOAD_DELAY aren’t enabled as these will lower your concurrency and are not needed with Scraper API.

Integrating ScrapeOps

ScrapeOps is already integrated into the scraper via the settings.py file. However, to use it you must:

Install the ScrapeOps Scrapy SDK on your machine.

pip install scrapeops-scrapy

And sign up for a free ScrapeOps account here so you can insert your API Key into the settings.py file:

    ## settings.py
    
    ## Add Your ScrapeOps API key
    SCRAPEOPS_API_KEY = 'YOUR_API_KEY'
    
    ## Add In The ScrapeOps Extension
    EXTENSIONS = {
     'scrapeops_scrapy.extension.ScrapeOpsMonitor': 500, 
    }
    
    ## Update The Download Middlewares
    DOWNLOADER_MIDDLEWARES = { 
	'scrapeops_scrapy.middleware.retry.RetryMiddleware': 550, 
	'scrapy.downloadermiddlewares.retry.RetryMiddleware': None, 
    }

From there, our scraping stats will be automatically logged and automatically shipped to our dashboard.

Running The Spider

To run the spider, use:

scrapy crawl scholar -o test.csv

Editing the Google Scholar Spider

The spider has 3 parts:

1. start_requests - will construct the Google Scholar URL for the search queries and send the request to Google.
2. parse - will extract all the search results from the Google Scholar search results.
3. get_url - to scrape Google Scholar at scale without getting blocked we need to use a proxy solution. For this project we will use Scraper API so we need to create a function to send the request to their API endpoint.

If you want to scrape more or less fields from the search results page then edit the XPath selectors in the parse function:

def parse(self, response):
       print(response.url)
       position = response.meta['position']
       for res in response.xpath('//*[@data-rp]'):
           link = res.xpath('.//h3/a/@href').extract_first()
           temp = res.xpath('.//h3/a//text()').extract()
           if not temp:
               title = "[C] " + "".join(res.xpath('.//h3/span[@id]//text()').extract())
           else:
               title = "".join(temp)
           snippet = "".join(res.xpath('.//*[@class="gs_rs"]//text()').extract())
           cited = res.xpath('.//a[starts-with(text(),"Cited")]/text()').extract_first()
           temp = res.xpath('.//a[starts-with(text(),"Related")]/@href').extract_first()
           related = "https://scholar.google.com" + temp if temp else ""
           num_versions = res.xpath('.//a[contains(text(),"version")]/text()').extract_first()
           published_data = "".join(res.xpath('.//div[@class="gs_a"]//text()').extract())
           position += 1
           item = {'title': title, 'link': link, 'cited': cited, 'relatedLink': related, 'position': position,
                   'numOfVersions': num_versions, 'publishedData': published_data, 'snippet': snippet}
           yield item
       next_page = response.xpath('//td[@align="left"]/a/@href').extract_first()
       if next_page:
           url = "https://scholar.google.com" + next_page
           yield scrapy.Request(get_url(url), callback=self.parse,meta={'position': position})

If you don't want to scrape every page returned for that keyword then comment out the next_page section of the parse function.