How to Scrape Google News Results Data Using Python?

At the edge of real-time information, Google News is a great aggregated news content platform. Google News combines news from global and local sources and presents the most accurate and diverse content. It shows important news as it happens, tailored to your interests. Google News is recognized for delivering worldwide and local news.

Google News Result is a major contributor in providing data points on a large scale for tracking trends, monitoring brands, sentiment analysis, crisis detection, and more. These insights can be used by a wide range of individuals and organizations, such as journalists, media outlets, academic researchers, communication professionals, and more.

This is a detailed and thorough blog post in which we will go through a step-by-step approach to use to scrape Google News results using Python.

Why Scrape Google News Results Data?

Scraping data from Google News results serves multiple objectives. Extracting this platform provides actionable insights that enable you to spot emerging news topics, track mentions and reputation shifts, and gauge public opinion tone. Additionally, it can empower you to identify early warning signals to get alerted to a crisis.

Extracting data from Google News results is beneficial for journalists to analyze rivals’ media coverage. It can provide market intelligence that can link news to business signals. Many times, customers face difficulties in finding relevant content. Extracting data from Google News results can power content aggregation for feeding dashboards and newsletters.

Understanding Google News Structure

In this section, we will explore Google News Structure. The platform layout is as follows:

• Headline: This is the article title text.
• Snippet: It is the summary or excerpt of the news.
• Source: This is a publisher or outlet name.
• Article Link: Google News contains a direct news URL.
• Timestamp: This is the news publication time/date.
• Thumbnail Image: The platform contains a preview image (optional).
• Topic Tag: Google News displays category or keywords.
• Author (optional): Contains the name of the journalist or contributor.

Google News Homepage, Topic Pages, & Search Results

The Google News site is divided into three page types called Homepage, Topic Pages, and Search Results. The following table presents the difference between these three page types.

The Importance of Targeting The Right URL Structure

Targeting the right URL structure is indispensable for:

• Query Relevance: It is for Accurate topic filtering.
• Data Consistency: This is for a predictable HTML layout.
• Anti-bot Resilience: It will be used to avoid unnecessary redirects.
• Result Freshness: This is to get timely article updates.
• Scraping Efficiency: The right URL structure will empower the development of faster parsing logic.
• Pagination Control: This is for easier result navigation.

Tools and Libraries Required

• Selenium or Playwright: These Python libraries are optional. They are needed to render JavaScript content.
• BeautifulSoup: It is required to parse static HTML.
• Python Programming Language: In this technical blog post, we will write a Python script to extract Google News results data.
• Pandas: It is essential for structuring data.
• Uandom_user_agent or Fake_useragent: These libraries are required for spoofing headers.
• ChromeDriver: This will be the interface for the Selenium browser.
• Installation commands: (pip install ...).

Step-by-Step Scraping Workflow

Now we will understand step-by-step how you can scrape Google News search results data.

Step 1: Define your Search Query

First, we define a search query and choose a topic or a keyword.

import urllib.parse
query = "AI regulation"
encoded_query = urllib.parse.quote_plus(query)  # Converts to 'AI+regulation'
search_url = f"https://news.google.com/search?q={encoded_query}"
print("Search URL:", search_url)

In the above code, you can see that we have used the function urllib.parse.quote_plus() to ensure the query is URL-safe. Search_url is needed to target the search result page.

Step 2: Set Up Headers and Session

In the second step, we will set up headers and sessions to mimic a real browser and avoid blocks.

import requests
from fake_useragent import UserAgent

ua = UserAgent()
headers = {
    "User-Agent": ua.random,
    "Accept-Language": "en-US,en;q=0.9",
    "Referer": "https://news.google.com/"
}
session = requests.Session()

In the above code, we have used User-Agent. It is needed to randomize to simulate different browsers. Further, the use of Accept-Language in the code will return English content. Referer will add legitimacy to our requests. Here requests.Session() function reuses TCP connections for efficiency.

Step 3: Send the HTTP Request

In this step, we will fetch the content of the search result page.

response = session.get(search_url, headers=headers, timeout=10)
if response.status_code == 200:
    html_content = response.text
else:
    print("Request failed with status:", response.status_code)

In the above code, timeout=10 will help you to prevent hanging requests. Status_code will handle errors.

Step 4: Parse the HTML Response

In this step, we will parse HTML using the Python library BeautifulSoup.

from bs4 import BeautifulSoup

soup = BeautifulSoup(html_content, "html.parser")
articles = soup.select("article")
print("Found", len(articles), "articles")

Here, the function soup.select("article") targets each news card on the page.

Step 5: Extract Article Components

The fifth step is to loop through each article and extract data.

news_data = []

for article in articles:

headline_tag = article.find("h3")
    headline = headline_tag.text if headline_tag else None

    link_tag = article.find("a", href=True)
    raw_link = link_tag["href"] if link_tag else None
    full_link = f"https://news.google.com{raw_link[1:]}" if raw_link else None

    source_tag = article.find("div", class_="SVJrMe")
    source = source_tag.text if source_tag else None

    time_tag = article.find("time")
    timestamp = time_tag["datetime"] if time_tag and time_tag.has_attr("datetime") else None

    snippet_tag = article.find("span")
    snippet = snippet_tag.text if snippet_tag else None

    news_data.append({
        "headline": headline,
        "link": full_link,
        "source": source,
        "timestamp": timestamp,
        "snippet": snippet
    })

Step 6: Normalize and Store the Data

This is the 6th and last step, in which we will clean, normalise the data. After this, we will store data in a CSV file.

import pandas as pd

df = pd.DataFrame(news_data)
df.dropna(subset=["headline", "link"], inplace=True)
df.to_csv("google_news_results.csv", index=False)
print("Saved to google_news_results.csv")

How to handle Scroll?

To handle browser scroll, you have to perform the following steps.

• Step 1: Use topic pages like https://news.google.com/topics/…
• Step 2: After this, go for the RSS feed.
• Step 3: Use either Selenium or Playwright to simulate scrolling.

Use Cases of Extracting Google News Results Data

This section will provide information on the use cases of extracting Google News result data.

Competitor Media Analysis

Product managers can leverage extracted Google News results data for assessing the effectiveness of positive or negative framing using a qualitative user research method. This data helps businesses to successfully compare volume to measure media visibility.

Publisher Bias Detection

Scraping Google News results data enables businesses to analyze source tone to detect positive or negative bias. By interpreting this data, researchers and analysts can source headline sentiment and evaluate the framing of coverage.

Retail Trend Detection

Retailers can use scraped Google News result data to track seasonal demand by identifying peak shopping periods. In essence, organizations can leverage this data to effectively monitor trending interests. Enterprises can rely on scraped data to map regional trends and detect location-specific buying habits.

Brand Sentiment Tracking

By extracting Google News result data, businesses and researchers can effectively monitor public perception of brands. It helps to measure PR effectiveness and benchmark media tone. Businesses can monitor brand trust to track long-term reputation trends.

Investor Sentiment Analysis

Google News results data enables firms to detect M&A rumors by spotting acquisition speculation. It empowers investors to identify volatility triggers to receive market fear signals. Traders can leverage scraped Google News results data to gauge brand-level investor tone.

Anti-Bot Strategies and Resilience

IP Rotation

If you wish to extract data from Google News results, then always switch your IP address. This will prevent you from getting blocked. It will bypass rate limiting by distributing the request load. IP rotation also helps you avoid CAPTCHA to reduce bot detection.

Add Random Delays

You need to introduce a sleep interval between your data scraping requests to mimic the timing of real user interactions.

Monitor Response Codes

Always monitor response codes such as HTTP 429 or 403 to detect blocking early.

Spoof Request Headers

You need to include realistic headers Accept-Language and User-Agent. This will help you to simulate legitimate traffic.

Conclusion

Now, it’s time to conclude this blog. This is a detailed blog in which we grasp the knowledge about Google News, the Importance of scraping Google News results data. Furthermore, we understood Google News' structure in depth. We also saw Google News homepage, topic pages, & search results in detail. This blog showed the importance of targeting the right URL structure, tools, and libraries required. We wrote a Python code to scrape data from Google News results. At last, we learnt about the use cases of extracting Google News results data and anti-bot strategies, and resilience. Want to develop an automated scraper that can extract data from Google News? Reach Scraping Intelligence today.

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About the author

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Zoltan Bettenbuk

Zoltan Bettenbuk is the CTO of ScraperAPI - helping thousands of companies get access to the data they need. He’s a well-known expert in data processing and web scraping. With more than 15 years of experience in software development, product management, and leadership, Zoltan frequently publishes his insights on our blog as well as on Twitter and LinkedIn.