How to scrape Google News with Python in 2023 (Full Code included)

Google News ranks as the 6th most popular news publishing website in the United States. With a massive user base of over 370 million monthly visitors, it stands as one of the most widely accessed news sources globally.

As well as being hugely popular, the site aggregates data from over 20,000 publishers. Scraping data from Google News means finding out what the world's media is thinking about a given subject at a given time. Thousands of hits with one stone.

In this article, we'll explore how to scrape Google News using Python 🐍. We will walk you through the process of creating a Python script to gather news data from Google News.

Why scrape Google News?

Scraping in Python, we know how to do it. But what is our motive?

There are many reasons to scrape Google News, including:

1. Brand monitoring: Track how your brand is being covered in the media.
2. Competitor analysis: Monitor your competitors' activities and strategies.
3. Market research: Gather data on consumer trends and market sentiment.
4. News aggregation: Create your own news website or aggregator.
5. Data analysis: Perform research on a variety of topics, such as politics, economics, and social issues.

Whether you're a data enthusiast, researcher, or simply someone who loves keeping up with the news, this Python script allows you to extract articles from any language or region while capturing important data like headlines, publication dates, article links, and news sources. Let's get coding! 🧑‍💻

How to scrape Google News with Python?

Before proceeding, we asked ourselves, what do we need exactly?

Which data attributes we want to scrape and which features we need in our scraper.

What data attributes do we need?

We want to keep it simple and straightforward. When we explore news online, we read the headline/title, when it was published, who posted it, and maybe a few words from description to understand the context.

So the attributes are sorted as follows:

1. Title: headline of the news article
2. Published datetime: freshness of the news
3. Source: who posted the article — is it trustworthy?
4. Description: what's the context
5. Link: to access the complete article

What features do we need in our scraper?

Well we don’t need any fancy stuff but the scraper must be powerful enough to scrape data from several URLs and stop once a certain number of items have been collected.

Here are the features of the end scraper:

1. Extract: extract all data from a given Google News Search URL
2. Stop: after a limit amount of data has been collected
3. Export: data to .csv with a timestamped filename
4. Scale: read several Google News search URLs from a csv file

Scrape Google News with API

First we started with exploring the official API. While searching for the official API, we found out Google News had an official API till 2013 but it was deprecated in 2011 and completely discontinued in 2013.

From this support Google thread, it unfortunately cannot be more clear, there is strictly no official Google News API:

Thanks Chris.

We could also have used a third-party API, such as the powerful and widely-used News API. But these commercial APIs are restricted, and substantially expensive: starting from USD 450 per day, and articles available with 24h delay with the free plan.

Do we have any cost-competitive alternative — with fresh data and competitive price?

Scrape Google News through Google SERPs

Intuitively, the first idea is to pick data from Google SERPs, when using the News tab. This is, after all, the easiest way to access Google News.

But when exploring the data source, it does look that the HTML structure is extremely complex, with intricate HTML elements and meaningless class names:

More concerning, the SERPs results do not provide the exact date and time of publication of news articles. Instead, we have rough and unreliable estimates. Annoying if you want to build a live news-based programmatic investment machine.

If only we had another source of data, simply structured and making exhaustive data available.

Does this exist?

In the next part, we will see how to leverage the old-but-gold powerful Google News RSS feed.

Scrape Google News with RSS feed

RSS, invented in 1999, stands for Really Simple Syndication, which means a really simple way to convey information between organizations. Nice, this is exactly what we need.

It is actually similar to an HTML structure, apart from 3 key differences:

1. Flat: the data structure is easy
2. Explicit: each element has a clear explicit name
3. Simple: there is no class name or additional attributes with no functional purpose

Basically, an RSS feed does look like this:

<item>
    <title>The Pros And Cons Of Each Cooking Method For Crab - Tasting Table</title>
    <link>https://news.google.com/rss/articles/CBMiRWh0dHBzOi8vd3d3LnRhc3Rpbmd0YWJsZS5jb20vMTQzOTk5My9wcm9zLWNvbnMtZWFjaC1tZXRob2QtY29vay1jcmFiL9IBAA?oc=5</link>
    <guid isPermaLink="false">CBMiRWh0dHBzOi8vd3d3LnRhc3Rpbmd0YWJsZS5jb20vMTQzOTk5My9wcm9zLWNvbnMtZWFjaC1tZXRob2QtY29vay1jcmFiL9IBAA</guid>
    <pubDate>Tue, 07 Nov 2023 12:00:40 GMT</pubDate>
    <description><a href="https://news.google.com/rss/articles/CBMiRWh0dHBzOi8vd" target="_blank">The Pros And Cons Of Each Cooking Method For Crab</a></description>
    <source url="https://www.tastingtable.com">Tasting Table</source>
</item>

And the good news is that Google News does provide an easy-to-access, highly-structured live RSS feed. Awesome!

Because of a simpler and more structured format, we will be able to develop a parser faster, and more robust in the long run.

Cherry on the cake, unlike Google SERP, the RSS feed provides a reliable exact date and time of publication for each article. To the second.

In the next part, we’ll see how to scrape Google News RSS feed.

Step-by-step tutorial

RSS provides data in a simple, structured and standardized format: it’s easier to pull and parse.

From left to right, XML feed, webpage, and HTML source code from the search Google News URL, https://news.google.com/search?q=crab&hl=en-US&gl=US&ceid=US%3Aen

To scrape data from RSS, the first thing we need is a feed URL. But where to find this Google News RSS URL?

You can access the RSS feed just by simply adding /rss to your Google news URL. Go to news.google.com and search any topic. This will give us a URL like this:

https://news.google.com/search?q=crab

Now after the .com/ add rss and it will take you to the RSS feed of that particular page e.g.

https://news.google.com/rss/search?q=crab

Now we have the feed url, we’ll create a Google News RSS Feed scraper in 5 simple steps:

1. Fetch the RSS feed using a python library
2. Parse the feed and extract our required data attributes
3. Add dynamic attributes for extra comfort
4. Export the data to csv file
5. Make it more functional

1. Fetch & Parse RSS feed

For extracting data from the RSS feed, we’re going to use a ready-made easy-to-use library: feedparser.

What is feedparser?

feedparser is a Python library for parsing RSS feeds and Atom feeds. It simplifies the retrieval and extraction of structured data from syndicated web content, making it easier to access and work with news articles, blog posts, and other feed-based information.

To get started, we need to install it using pip (obviously). Here’s how to do it:

$ pip install feedparser

Now let’s import it to our script. Let's create a new python file and import feedparser.

import feedparser

Now let’s create a scrape_google_news_feed() function and see how feedparser works. We’ll start by generating the RSS feed URL from the query and then fetch and parse the feed.

def scrape_google_news_feed(query):
    rss_url = f'https://news.google.com/rss/search?q={query}&hl=en-US&gl=US&ceid=US:en'
    feed = feedparser.parse(rss_url)

2. Extract data attributes

Now let’s fetch and print the entries of our Google news feed to the console. What entries to scrape? Let’s identify them on our feed page.

So we can fetch title, link, date of publication, description, and source from the RSS feed.

Let’s see how:

if feed.entries:
        for entry in feed.entries:
            title = entry.title
            link = entry.link
            description = entry.description
            pubdate = entry.published
            source = entry.source
            print(f"Title: {title}\nLink: {link}\nDescription: {description}\nPublished: {pubdate}\nSource: {source}")
            print("-+-")
    else:
        print("Nothing Found!")

Easy!

Now let’s give our script a query and execute it.

if __name__ == "__main__":
    query = 'crab'
    scrape_google_news_feed(query)

And here’s the output:

$ python google_news_feed_scraper.py
Title: Virginia ponders reopening long-closed winter crab harvest - The Chesapeake Bay Journal
Link: https://news.google.com/rss/articles/CBMilgFodHRwczovL3d3dy5iYXlqb3VybmFsLmNvbS9uZXdzL2Zpc2hlcmllcy92aXJnaW5pYS1wb25kZXJzLXJlb3BlbmluZy1sb25nLWNsb3NlZC13aW50ZXItY3JhYi1oYXJ2ZXN0L2FydGljbGVfMWZlZTYxNTItNzFhNS0xMWVlLTk3YWEtMWIzMjNiOTRlMjA3Lmh0bWzSAQA?oc=5
Description: <a href="https://news.google.com/rss/articles/CBMilgFodHRwczovL3d3dy5iYXlqb3VybmFsLmNvbS9uZXdzL2Zpc2hlcmllcy92aXJnaW5pYS1wb25kZXJzLXJlb3BlbmluZy1sb25nLWNsb3NlZC13aW50ZXItY3JhYi1oYXJ2ZXN0L2FydGljbGVfMWZlZTYxNTItNzFhNS0xMWVlLTk3YWEtMWIzMjNiOTRlMjA3Lmh0bWzSAQA?oc=5" target="_blank">Virginia ponders reopening long-closed winter crab harvest</a>&nbsp;&nbsp;<font color="#6f6f6f">The Chesapeake Bay Journal</font>
Published: Mon, 23 Oct 2023 17:45:00 GMT
Source: {'href': 'https://www.bayjournal.com', 'title': 'The Chesapeake Bay Journal'}
-+-
Title: Costa Rica Wildlife - Meet the Halloween Crab : - The Tico Times
Link: https://news.google.com/rss/articles/CBMiTGh0dHBzOi8vdGljb3RpbWVzLm5ldC8yMDIzLzEwLzI0L2Nvc3RhLXJpY2Etd2lsZGxpZmUtbWVldC10aGUtaGFsbG93ZWVuLWNyYWLSAQA?oc=5
Description: <a href="https://news.google.com/rss/articles/CBMiTGh0dHBzOi8vdGljb3RpbWVzLm5ldC8yMDIzLzEwLzI0L2Nvc3RhLXJpY2Etd2lsZGxpZmUtbWVldC10aGUtaGFsbG93ZWVuLWNyYWLSAQA?oc=5" target="_blank">Costa Rica Wildlife - Meet the Halloween Crab :</a>&nbsp;&nbsp;<font color="#6f6f6f">The Tico Times</font>
Published: Tue, 24 Oct 2023 21:07:00 GMT
Source: {'href': 'https://ticotimes.net', 'title': 'The Tico Times'}
-+-

It works, awesome!

🎉

We’ve scraped all 5 data attributes from the RSS feed successfully.

Now let’s make this code more organized and maintainable. How do we do that? We encapsulate it in a class first.

Here’s how our updated code looks like:

import feedparser

class googleNewsFeedScraper:
    def __init__(self, query):
        self.query = query

    def scrape_google_news_feed(self):
        rss_url = f'https://news.google.com/rss/search?q={self.query}&hl=en-US&gl=US&ceid=US:en'
        feed = feedparser.parse(rss_url)

        if feed.entries:
            for entry in feed.entries:
                title = entry.title
                link = entry.link
                description = entry.description
                pubdate = entry.published
                source = entry.source
                print(f"Title: {title}\nLink: {link}\nDescription: {description}\nPublished: {pubdate}\nSource: {source}")
                print("-+-")
        else:
            print("Nothing Found!")

if __name__ == "__main__":
    query = 'crab'
    scraper = googleNewsFeedScraper(query)
    scraper.scrape_google_news_feed()

3. Add dynamic attributes

Now we don’t want to edit the query variable in main every time we need to run the script. How about using a Google News URL instead of a query?

Let’s modify the script to take a Google News URL and convert it to RSS URL.

We also want the user to enter the URL in the command line using an argument like -u or url. We have the argparse module to handle this for us. argparse is a Python module for easily parsing command-line arguments and creating user-friendly command-line interfaces for your scripts. It's available in the standard library, so we don’t need to install it.

Let’s import it first:

import argparse

Now first, we need to define a method to convert our Google News URL to RSS url.

We can do this using:

def convert_to_rss_url(self):
        if "https://news.google.com/search?" in self.url:
            self.url = self.url.replace("https://news.google.com/search?", "https://news.google.com/rss/search?")
        else:
            raise ValueError("Invalid URL.")

Now we’ll define the arguments using argparse in our main() function. We’ll create an ArgumentParser object and use it with add_argument to define a command line argument.

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("-u", "--url", type=str, required=True, help="Google News URL")

    args = parser.parse_args()
    scraper = GoogleNewsFeedScraper(args.url)
    scraper.scrape_google_news_feed()

Now to execute this updated code, add the final touch:

if __name__ == "__main__":
    main()

Let’s execute our code now:

$ python google_news_feed_scraper.py -u "https://news.google.com/search?q=crab&hl=en-US&gl=US&ceid=US%3Aen"
Title: Why 10 Billion Crabs Suddenly Vanished From the Bering Sea - Popular Mechanics
Link: https://news.google.com/rss/articles/CBMiaWh0dHA6Ly93d3cucG9wdWxhcm1lY2hhbmljcy5jb20vc2NpZW5jZS9hbmltYWxzL2E0NTYxNjUxOC93aHktYmlsbGlvbnMtb2YtY3JhYnMtdmFuaXNoZWQtZnJvbS1iZXJpbmctc2VhL9IBAA?oc=5
Description: <a href="https://news.google.com/rss/articles/CBMiaWh0dHA6Ly93d3cucG9wdWxhcm1lY2hhbmljcy5jb20vc2NpZW5jZS9hbmltYWxzL2E0NTYxNjUxOC93aHktYmlsbGlvbnMtb2YtY3JhYnMtdmFuaXNoZWQtZnJvbS1iZXJpbmctc2VhL9IBAA?oc=5" target="_blank">Why 10 Billion Crabs Suddenly Vanished From the Bering Sea</a>&nbsp;&nbsp;<font color="#6f6f6f">Popular Mechanics</font>
Published: Wed, 25 Oct 2023 15:45:07 GMT
Source: {'href': 'http://www.popularmechanics.com', 'title': 'Popular Mechanics'}
-+-
Title: Virginia ponders reopening long-closed winter crab harvest - The Chesapeake Bay Journal
Link: https://news.google.com/rss/articles/CBMilgFodHRwczovL3d3dy5iYXlqb3VybmFsLmNvbS9uZXdzL2Zpc2hlcmllcy92aXJnaW5pYS1wb25kZXJzLXJlb3BlbmluZy1sb25nLWNsb3NlZC13aW50ZXItY3JhYi1oYXJ2ZXN0L2FydGljbGVfMWZlZTYxNTItNzFhNS0xMWVlLTk3YWEtMWIzMjNiOTRlMjA3Lmh0bWzSAQA?oc=5
Description: <a href="https://news.google.com/rss/articles/CBMilgFodHRwczovL3d3dy5iYXlqb3VybmFsLmNvbS9uZXdzL2Zpc2hlcmllcy92aXJnaW5pYS1wb25kZXJzLXJlb3BlbmluZy1sb25nLWNsb3NlZC13aW50ZXItY3JhYi1oYXJ2ZXN0L2FydGljbGVfMWZlZTYxNTItNzFhNS0xMWVlLTk3YWEtMWIzMjNiOTRlMjA3Lmh0bWzSAQA?oc=5" target="_blank">Virginia ponders reopening long-closed winter crab harvest</a>&nbsp;&nbsp;<font color="#6f6f6f">The Chesapeake Bay Journal</font>
Published: Mon, 23 Oct 2023 17:45:00 GMT
Source: {'href': 'https://www.bayjournal.com', 'title': 'The Chesapeake Bay Journal'}
-+-

Our boi works smoothly.

Bravo ✨

4. Export data to csv

But we can’t keep printing the output to the console. It’ll vanish when you close your terminal or command prompt. We want to save the output sustainably.

Why not save the collected data within a .csv file?

4.1. Save data in a csv

We can do it by importing csv from the python standard library.

import csv

Now we’ll initialize an empty list to store the scraped data.

def __init__(self, url):
        self.url = url
        self.data = []

Now we’ll add a line of code to feed.entries to append data to the list:

self.data.append([self.url, title, link, pubdate, description, source])

Next, in main(), let’s define an output csv file and write data to it:

output_file = "google_news_output.csv"
    with open(output_file, "w", newline="") as csvfile:
        csv_writer = csv.writer(csvfile)
        csv_writer.writerow(["Input URL", "Title", "Link", "Published", "Description", "Source"])
        for row in scraper.data:
            csv_writer.writerow(row)

Now let’s check if our code saves the data to the csv file. Run the same command again:

$ python google_news_feed_scraper.py -u "https://news.google.com/search?q=crab&hl=en-US&gl=US&ceid=US%3Aen"

And it works!

But this csv will be rewritten every time we run this script. 🤔 To avoid this, we can either prompt the user to enter a csv filename or we can generate a unique csv filename each time we run the script.

The second option is good, because it’s automated and does not required any manual commitment. Automation wins.

Let’s explore it.

4.2. Make file name unique

We’ll use the time module to create a csv filename with current time. This will not only make the file unique but also help you track files by time and date.

import time

Now let’s create a new variable to generate a timestamp and modify the output_file variable to include the timestamp in name:

current_time = time.strftime("%Y-%m-%d-%H-%M-%S")
output_file = f"google_news_{current_time}.csv"

OK working clean!

5. Make it more functional

Let’s make our script perfect by adding additional functionality. What if we have 100 URLs to scrape? What if we need to scrape only top 10 news from each URL? Let’s solve this problem too.

We’ll thus solve both problems:

1. handle multiple URLs
2. add limit

Time for functionalities enhancement.

5.1. Handling multiple URLs

To make our powerful parser handle multiple URLs without a sweat, we’ll make the changes to our code.

Our __init__ constructor changes to the following, with an empty list to store data from each URL.

def __init__(self):
        self.data = []

To convert URLs from the list, used the converted_url variable in scrape_google_news_feed() :

converted_url = self.convert_to_rss_url(url)
feed = feedparser.parse(converted_url)

And finally, in main(), to give users the choice to choose between url or file, we changed the arguments to mutually exclusive groups and added a new argument.

group = parser.add_mutually_exclusive_group(required=True)
group.add_argument("-u", "--url", type=str, help="Google News URL")
group.add_argument("-f", "--file", type=str, help="File containing Google News URLs")
args = parser.parse_args()

And to read from urls file, we added this simple condition:

if args.file:
        with open(args.file, "r") as url_file:
            urls = url_file.read().splitlines()

Here’s how the full code looks like now:

class GoogleNewsFeedScraper:
    def __init__(self):
        self.data = []

    def convert_to_rss_url(self, url):
        if url.startswith("https://news.google.com/search?"):
            return url.replace("https://news.google.com/search?", "https://news.google.com/rss/search?")
        else:
            raise ValueError("Invalid URL.")

    def scrape_google_news_feed(self, url):
        converted_url = self.convert_to_rss_url(url)
        feed = feedparser.parse(converted_url)

        if feed.entries:
            for entry in feed.entries:
                title = entry.title
                link = entry.link
                description = entry.description
                pubdate = entry.published
                source = entry.source

                self.data.append([url, title, link, pubdate, description, source])
        else:
            print("Nothing Found!")

def main():
    parser = argparse.ArgumentParser()
    group = parser.add_mutually_exclusive_group(required=True)
    group.add_argument("-u", "--url", type=str, help="Google News URL")
    group.add_argument("-f", "--file", type=str, help="File containing Google News URLs")

    args = parser.parse_args()
    scraper = GoogleNewsFeedScraper()

    current_time = time.strftime("%Y-%m-%d-%H-%M-%S")
    output_file = f"google_news_{current_time}.csv"

    if args.file:
        with open(args.file, "r") as url_file:
            urls = url_file.read().splitlines()
        with open(output_file, "w", newline="") as csvfile:
            csv_writer = csv.writer(csvfile)
            csv_writer.writerow(["Input URL", "Title", "Link", "Published", "Description", "Source"])
            for url in urls:
                scraper.scrape_google_news_feed(url)
                for row in scraper.data:
                    csv_writer.writerow(row)
    else:
        scraper.scrape_google_news_feed(args.url)

if __name__ == "__main__":
    main()

5.2. Add limit

Now let’s add the limit.

First add a limit parameter to __init__ and we’ll also add a self.limit attribute to store the data within the limit.

def __init__(self, limit=None):
        self.limit = limit
        self.data = []

Next, in scrape_google_news_feed, apply the limit to the entries list by slicing it using self.limit if a limit is specified.

if self.limit:
            entries = feed.entries[:self.limit]
        else:
            entries = feed.entries

Finally in the main() function, add a -l or --limit argument and create a scraper object with args.limit parameter to pass the limit to the scraper.

parser.add_argument("-l", "--limit", type=int, help="Limit the number of results per URL")

    args = parser.parse_args()
    scraper = GoogleNewsFeedScraper(args.limit)

    current_time = time.strftime("%Y-%m-%d-%H-%M-%S")
    output_file = f"google_news_{current_time}.csv"

    if args.file:
        with open(args.file, "r") as url_file:
            urls = url_file.read().splitlines()
        with open(output_file, "w", newline="") as csvfile:
            csv_writer = csv.writer(csvfile)
            csv_writer.writerow(["Input URL", "Title", "Link", "Published", "Description", "Source"])
            for url in urls:
                scraper.scrape_google_news_feed(url)
                for row in scraper.data:
                    csv_writer.writerow(row)
    else:
        scraper.scrape_google_news_feed(args.url)

Let’s test the limit and file feature.

Run the following command:

$ python google_news_feed_scraper.py -f urls.txt -l 10

Here we go:

And with that, we’ve created a powerful multi-functionalities Google News scraper.

Here’s the full code.

import feedparser
import argparse
import csv
import time

class GoogleNewsFeedScraper:
    def __init__(self, limit=None):
        self.limit = limit
        self.data = []

    def convert_to_rss_url(self, url):
        if url.startswith("https://news.google.com/search?"):
            return url.replace("https://news.google.com/search?", "https://news.google.com/rss/search?")
        else:
            raise ValueError("Invalid URL.")

    def scrape_google_news_feed(self, url):
        converted_url = self.convert_to_rss_url(url)
        feed = feedparser.parse(converted_url)

        if self.limit:
            entries = feed.entries[:self.limit]
        else:
            entries = feed.entries

        if feed.entries:
            for entry in entries:
                title = entry.title
                link = entry.link
                description = entry.description
                pubdate = entry.published
                source = entry.source

                self.data.append([url, title, link, pubdate, description, source])
        else:
            print("Nothing Found!")

def main():
    parser = argparse.ArgumentParser()
    group = parser.add_mutually_exclusive_group(required=True)
    group.add_argument("-u", "--url", type=str, help="Google News URL")
    group.add_argument("-f", "--file", type=str, help="File containing Google News URLs")
    parser.add_argument("-l", "--limit", type=int, help="Limit the number of results per URL")

    args = parser.parse_args()
    scraper = GoogleNewsFeedScraper(args.limit)

    current_time = time.strftime("%Y-%m-%d-%H-%M-%S")
    output_file = f"google_news_{current_time}.csv"

    if args.file:
        with open(args.file, "r") as url_file:
            urls = url_file.read().splitlines()
        with open(output_file, "w", newline="") as csvfile:
            csv_writer = csv.writer(csvfile)
            csv_writer.writerow(["Input URL", "Title", "Link", "Published", "Description", "Source"])
            for url in urls:
                scraper.scrape_google_news_feed(url)
                for row in scraper.data:
                    csv_writer.writerow(row)
    else:
        scraper.scrape_google_news_feed(args.url)

if __name__ == "__main__":
    main()

But it looks extremely nerdy, with no documentation, nor helpful documentation. In order to ensure long-term maintainability, and ease readability, let’s tackle this now.

5.3. Garnish it with exceptions

Well, it’s functionally powerful but… Let’s make it sexy! Documenting code though is an extremely… boring activity, we never doubted of it:

How can we fix it?

Let’s take some help from ChatGPT. If ChatGPT sometimes is inventing entire areas of knowledge, GPT is doing an exceptional job at commenting on ready-made content. Let’s use it to add exceptions and print statements to the code to make it more readable and user-friendly.

So first we asked our AI coding partner to add exceptions to our code:

Now let’s see what it generated for us:

Now let’s add some print statements too. We’ll ask ChatGPT to add print statements to print the url we’re scraping, elapsed time, and output file to the console.

Now let’s run the code to check if GPT did a good job or it messed up (like it notoriously does sometimes)).

5.4. Add unforgettable chef-like signature

Last thing, like an aspiring grand chef (let’s hope so 🥹) let’s add our hot signature before running the code.

First, let’s visit this old-school stylish signature making: https://patorjk.com/.

Then, let’s simply type the name of the company, and picked the font as Isometric 1:

https://patorjk.com/software/taag/#p=display&f=Isometric1&t=lobstr

Endly, let’s just insert it at the end of the code:

    print(f"Total Elapsed Time: {total_elapsed_time} seconds")
    print('''~~ success
     ___       ___           ___           ___           ___           ___     
     /\__\     /\  \         /\  \         /\  \         /\  \         /\  \    
    /:/  /    /::\  \       /::\  \       /::\  \        \:\  \       /::\  \   
   /:/  /    /:/\:\  \     /:/\:\  \     /:/\ \  \        \:\  \     /:/\:\  \  
  /:/  /    /:/  \:\  \   /::\~\:\__\   _\:\~\ \  \       /::\  \   /::\~\:\  \ 
 /:/__/    /:/__/ \:\__\ /:/\:\ \:|__| /\ \:\ \ \__\     /:/\:\__\ /:/\:\ \:\__\
 \:\  \    \:\  \ /:/  / \:\~\:\/:/  / \:\ \:\ \/__/    /:/  \/__/ \/_|::\/:/  /
  \:\  \    \:\  /:/  /   \:\ \::/  /   \:\ \:\__\     /:/  /         |:|::/  / 
   \:\  \    \:\/:/  /     \:\/:/  /     \:\/:/  /     \/__/          |:|\/__/  
    \:\__\    \::/  /       \::/__/       \::/  /                     |:|  |    
     \/__/     \/__/         ~~            \/__/                       \|__|   
''')

Unforgettable.

Full Code

So here’s how our complete code will look like:

import feedparser
import argparse
import csv
import time
import requests
import sys

class GoogleNewsFeedScraper:
    def __init__(self, limit=None):
        self.limit = limit
        self.data = []

    def convert_to_rss_url(self, url):
        if url.startswith("https://news.google.com/search?"):
            return url.replace("https://news.google.com/search?", "https://news.google.com/rss/search?")
        else:
            return None

    def scrape_google_news_feed(self, url):
        try:
            converted_url = self.convert_to_rss_url(url)
            if converted_url is None:
                raise ValueError("Invalid URL.")

            feed = feedparser.parse(converted_url)

            if self.limit:
                entries = feed.entries[:self.limit]
            else:
                entries = feed.entries

            if feed.entries:
                for entry in entries:
                    title = entry.title
                    link = entry.link
                    description = entry.description
                    pubdate = entry.published
                    source = entry.source

                    self.data.append([url, title, link, pubdate, description, source])
            else:
                print("Nothing Found!")
        except feedparser.FeedParserDict as e:
            print(f"Error parsing the feed: {e}")
        except requests.exceptions.RequestException as e:
            print(f"Network error: {e}")

def main():
    start_time = time.time()  # Start tracking time

    parser = argparse.ArgumentParser()
    group = parser.add_mutually_exclusive_group(required=True)
    group.add_argument("-u", "--url", type=str, help="Google News URL")
    group.add_argument("-f", "--file", type=str, help="File containing Google News URLs")
    parser.add_argument("-l", "--limit", type=int, help="Limit the number of results per URL")

    args = parser.parse_args()
    scraper = GoogleNewsFeedScraper(args.limit)

    current_time = time.strftime("%Y-%m-%d-%H-%M-%S")
    output_file = f"google_news_{current_time}.csv"

    try:
        if args.file:
            with open(args.file, "r") as url_file:
                urls = url_file.read().splitlines()
            with open(output_file, "w", newline="") as csvfile:
                csv_writer = csv.writer(csvfile)
                csv_writer.writerow(["Input URL", "Title", "Link", "Published", "Description", "Source"])
                for url in urls:
                    print(f"Scraping URL: {url}")
                    start_time_url = time.time()
                    scraper.scrape_google_news_feed(url)
                    for row in scraper.data:
                        csv_writer.writerow(row)
                    elapsed_time_url = time.time() - start_time_url
                    print(f"Elapsed Time for {url}: {elapsed_time_url} seconds")
            print(f"Data saved to file: {output_file}")
        else:
            print(f"Scraping URL: {args.url}")
            start_time_url = time.time()
            scraper.scrape_google_news_feed(args.url)
            elapsed_time_url = time.time() - start_time_url
            with open(output_file, "w", newline="") as csvfile:
                csv_writer = csv.writer(csvfile)
                csv_writer.writerow(["Input URL", "Title", "Link", "Published", "Description", "Source"])
                for row in scraper.data:
                    csv_writer.writerow(row)
            print(f"Elapsed Time for {args.url}: {elapsed_time_url} seconds")
            print(f"Data saved to file: {output_file}")
    except KeyboardInterrupt:
        print("Operation interrupted by the user.")
        sys.exit(1)
    except ValueError as e:
        print(f"Invalid URL: {e}")

    total_elapsed_time = time.time() - start_time  # Calculate total elapsed time
    print(f"Total Elapsed Time: {total_elapsed_time} seconds")
    print('''~~ success
     ___       ___           ___           ___           ___           ___     
     /\__\     /\  \         /\  \         /\  \         /\  \         /\  \    
    /:/  /    /::\  \       /::\  \       /::\  \        \:\  \       /::\  \   
   /:/  /    /:/\:\  \     /:/\:\  \     /:/\ \  \        \:\  \     /:/\:\  \  
  /:/  /    /:/  \:\  \   /::\~\:\__\   _\:\~\ \  \       /::\  \   /::\~\:\  \ 
 /:/__/    /:/__/ \:\__\ /:/\:\ \:|__| /\ \:\ \ \__\     /:/\:\__\ /:/\:\ \:\__\
 \:\  \    \:\  \ /:/  / \:\~\:\/:/  / \:\ \:\ \/__/    /:/  \/__/ \/_|::\/:/  /
  \:\  \    \:\  /:/  /   \:\ \::/  /   \:\ \:\__\     /:/  /         |:|::/  / 
   \:\  \    \:\/:/  /     \:\/:/  /     \:\/:/  /     \/__/          |:|\/__/  
    \:\__\    \::/  /       \::/__/       \::/  /                     |:|  |    
     \/__/     \/__/         ~~            \/__/                       \|__|   
''')

if __name__ == "__main__":
    main()

And here’s our output:

$ python google_news_feed_scraper.py -f urls.txt -l 5
Scraping URL: https://news.google.com/search?q=crab&hl=en-US&gl=US&ceid=US%3Aen
Elapsed Time for https://news.google.com/search?q=crab&hl=en-US&gl=US&ceid=US%3Aen: 4.065908432006836 seconds
Scraping URL: https://news.google.com/search?q=lobster&hl=en-US&gl=US&ceid=US%3Aen
Elapsed Time for https://news.google.com/search?q=lobster&hl=en-US&gl=US&ceid=US%3Aen: 2.0368733406066895 seconds
Data saved to file: google_news_2023-10-25-23-13-51.csv
Total Elapsed Time: 6.106725215911865 seconds
~~ ~~ success
     ___       ___           ___           ___           ___           ___     
     /\__\     /\  \         /\  \         /\  \         /\  \         /\  \    
    /:/  /    /::\  \       /::\  \       /::\  \        \:\  \       /::\  \   
   /:/  /    /:/\:\  \     /:/\:\  \     /:/\ \  \        \:\  \     /:/\:\  \  
  /:/  /    /:/  \:\  \   /::\~\:\__\   _\:\~\ \  \       /::\  \   /::\~\:\  \ 
 /:/__/    /:/__/ \:\__\ /:/\:\ \:|__| /\ \:\ \ \__\     /:/\:\__\ /:/\:\ \:\__\
 \:\  \    \:\  \ /:/  / \:\~\:\/:/  / \:\ \:\ \/__/    /:/  \/__/ \/_|::\/:/  /
  \:\  \    \:\  /:/  /   \:\ \::/  /   \:\ \:\__\     /:/  /         |:|::/  / 
   \:\  \    \:\/:/  /     \:\/:/  /     \:\/:/  /     \/__/          |:|\/__/  
    \:\__\    \::/  /       \::/__/       \::/  /                     |:|  |    
     \/__/     \/__/         ~~            \/__/                       \|__|

And here’s the output csv file:

Complete success.

Is it legal to scrape Google News?

Google News functions as a news aggregator and doesn't own the content it displays. The intellectual property belongs to the content creators. But Google's terms of service do clarify that publicly available factual information falls under common knowledge that everyone’s free to use. Furthermore, a recent update to Google's privacy policy has expanded the use of publicly shared information for AI model training and product development.

So all you need to take care of is not to reproduce or distribute the data that is protected by copyright. As long as you don't violate that, it's fully legal to scrape Google News.

However, reproducing and distributing scraped content without permission is a clear violation of copyright laws, and it could result in DMCA takedown penalties and legal action. You can learn more about blocking scraped content here.

Limitations

The RSS feed scraper has its own limitations. RSS feed only shows 100 articles per query, which means you can scrape up to maximum 100 articles.

Also the script depends on your internet connection speed. Slow internet can make them slow or abort the run.

Endly, be careful, because if pulling data too fast, your IP might be blocked. While developing our Google News Feed Parser, we scraped 15 URLs and thus did not face any ban attempt from Google.

Though, while exploring possible limits, we found this insight on trustworthy StackOverflow:

If you want collect as many news as needed with no limitation, as well as prevent being banned at any cost, you can test our powerful no-code Google News scraper.

Conclusion

So wrapping up. This was my complete journey of creating a Google News Scraper without any API. If you're a python newbie (like me) you can play around with this code. For awesome Python and web scraping tutorials, check out our blog.

You’ll find plenty of rich programming resources. Also, try our no-code scraper to scrape Google News without a sweat.

Happy Scraping. 🦞