Web scraping is an essential skill for data professionals looking to extract valuable insights from online sources. In the fast-paced world of news and media, the ability to automatically collect the latest headlines from multiple outlets can give you a competitive edge in staying informed.
In this in-depth guide, we‘ll walk through how to create a production-ready news crawler in Python that checks top sites like CNN, NBC News and Yahoo Sports for breaking stories on a set schedule. We‘ll cover the end-to-end process, from analyzing page structures to automating the crawl job, with a focus on how the ScrapingBee API simplifies many of the common challenges teams face when scraping news at scale.
Why Build a News Scraper?
Before we dive into the technical how-to, it‘s worth examining the motivations and use cases behind creating a custom news crawler:
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Information advantage – By ingesting headlines from whitelisted sources automatically, you can surface key facts, events and narratives in real-time without manual effort. This frees up time and attention for higher-value analysis.
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Customized sources – Off-the-shelf news aggregator sites and apps often come with limited customization options. When you own the pipes, you can fine-tune your feed to focus on the exact topics, outlets and regions you care about.
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Data mining – For organizations applying NLP, sentiment analysis, or knowledge graph techniques to news content, having an automated ingestion pipeline is table stakes. Owning the scraping layer allows you to optimize data quality and availability.
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Integrated workflows – A home-built crawler can be easily extended to pipe data into other systems like Slack alerts, email digests, or spreadsheet exports. This enables tighter feedback loops for teams that depend on timely information.
The unifying theme is that when you control the means of production for your news diet, you open up powerful possibilities for filtering, processing, and actioning the data in ways that off-the-shelf solutions can‘t match.
Evaluating the Scraping Landscape
Of course, building a robust news crawler is easier said than done. The modern web is a hostile place for scrapers, with anti-bot countermeasures like IP rate limits, user agent filtering, and CAPTCHAs designed to discourage automated access.
Rolling your own scrapers with Python libraries like Requests, Beautiful Soup, or Scrapy can work for small-scale, time-bound projects. But for sustained, highly reliable crawling, you‘ll quickly run into limitations around rotating proxies, spoofing headers, and handling JavaScript-rendered content.
This is where a battle-tested scraping API like ScrapingBee shines. ScrapingBee abstracts away many of the fiddly headaches required to scrape news sites at production grade:
- Manages a global pool of datacenter and residential proxies to circumvent IP bans and geoblocking
- Renders JS-heavy pages with a full web browser to ensure complete data extraction
- Provides a simple REST API to specify target URLs, CSS/XPath selectors and output formats
To see how ScrapingBee streamlines the usual scraping workflow, let‘s walk through an example of wrangling headline data from a few major news sources.
Finding our Scraping Targets
For this tutorial, we‘ll fetch the latest stories from three representative news sites:
Before we start coding, we need to scope out exactly where the relevant headline data lives within the tangled HTML thickets of each site. Using the browser devtools is the easiest way to reverse-engineer the patterns.
Here‘s how it looks for the CNN Business page:
The headline text is consistently wrapped in <h2> tags with a container_lead-plus-headlines__headline class. We can use this class as a CSS selector to precisely target only the headline content we want across the site.
Modern scraping frameworks like Scrapy are built around this idea of using XPath or CSS selectors to surgically extract structured data from messy web pages. ScrapingBee‘s extract_rules API offers the same capability in a simplified form.
After replicating the devtools spelunking for the NBC and Yahoo pages to suss out their headline markup, we‘re ready to translate our target sitemap to code.
Implementing the Crawler
Let‘s create a new Python file called app.py and add the following imports and configs:
from flask import Flask, render_template
from scrapingbee import ScrapingBeeClient
from apscheduler.schedulers.background import BackgroundScheduler
app = Flask(__name__)
scheduler = BackgroundScheduler()
client = ScrapingBeeClient(api_key=‘YOUR_API_KEY‘)
targets = [
{
‘name‘: ‘CNN Business‘,
‘url‘: ‘https://edition.cnn.com/business‘,
‘headline_selector‘: ‘.container_lead-plus-headlines__headline‘
},
{
‘name‘: ‘NBC Tech and Media‘,
‘url‘: ‘https://www.nbcnews.com/tech-media‘,
‘headline_selector‘: ‘.styled_headline__ice3t a‘
},
{
‘name‘: ‘Yahoo Sports‘,
‘url‘: ‘https://sports.yahoo.com‘,
‘headline_selector‘: ‘h3 a‘
}
]
headlines = {}We start by initializing instances of the core libraries we‘ll be using:
Flaskfor exposing a basic web frontend for our scraped headlinesScrapingBeeClientfor making requests to the ScrapingBee APIBackgroundSchedulerfor kicking off scrape jobs on a recurring schedule
The targets list is where we codify the patterns we discovered in our devtools research. Each scraping target gets a human-readable name, the base url to crawl, and the CSS headline_selector that pinpoints the headline text nodes in the page markup.
Externalizing these scraping parameters from the main crawler logic makes the code more readable, testable and maintainable. We can easily slot in new targets or selectors without touching the core algorithm.
Speaking of the core algorithm, it‘s remarkably straightforward with ScrapingBee handling the heavy lifting:
def scrape():
print(f‘[{datetime.now()}] Scraping headlines...‘)
global headlines
for target in targets:
response = client.get(
url = target[‘url‘],
params = {
‘extract_rules‘: {
‘headlines‘: {
‘selector‘: target[‘headline_selector‘],
‘output‘: ‘text‘
}
}
}
)
headlines[target[‘name‘]] = response.json()[‘headlines‘]
print(f‘[{datetime.now()}] Finished scraping headlines.‘)We define a scrape function to execute a single pass of headline fetching. For each target, it makes a GET request to ScrapingBee with the url and extract_rules params specifying the scrape configuration.
The extract_rules dict tells ScrapingBee to look for DOM nodes matching our headline_selector CSS pattern, extract their text content (as opposed to HTML), and return the results in a top-level headlines field.
Without ScrapingBee, this single call would balloon into dozens of complex lines to handle proxy configuration, cache busting, pagination edge cases, and more. By outsourcing those concerns, we can focus on the core data flow.
The API responses are stored on the headlines dict keyed by the target site names. This mirrors the nested structure of the targets config to keep things internally consistent.
Now let‘s expose our hard-earned headline data to the world with a /headlines Flask route:
@app.route(‘/headlines‘)
def headlines_route():
return render_template(‘headlines.html‘, headlines=headlines)And here‘s the headlines.html Jinja template it renders:
<!doctype html>
<html>
<head>
<title>Latest Headlines</title>
</head>
<body>
{% for site, site_headlines in headlines.items() %}
<h2>{{ site }}</h2>
<ul>
{% for headline in site_headlines %}
<li>{{ headline }}</li>
{% endfor %}
</ul>
{% endfor %}
</body>
</html>Nothing too fancy here – we iterate through the headlines dict, outputting each site as a subheading with its scraped story titles in a bulleted list below.
The final piece of the puzzle is scheduling our scrape function to run on a repeating interval to keep the headlines fresh. APScheduler makes this trivial:
if __name__ == ‘__main__‘:
scheduler.add_job(scrape, ‘interval‘, minutes=5)
scheduler.start()
app.run()When our script boots up, it will:
- Register the
scrapefunction as a background job to be run every 5 minutes - Start the scheduler to begin executing jobs
- Start the Flask dev server to respond to requests
After kicking things off with a python app.py, we can see our hard work pay off by navigating to http://localhost:5000/headlines in the browser:
Huzzah! We‘ve got a self-updating master feed of the top stories in business, tech, and sports from three leading sources with a mere 40 lines of Python. More importantly, we‘ve built it in a sustainable way by leaning on ScrapingBee to shoulder the fussy details of large-scale crawling.
Evaluating the Performance Boost
Just how much heavy lifting is ScrapingBee doing behind the scenes? Let‘s find out with a quick benchmark comparing the response times of bare Requests vs. the ScrapingBee client for our target pages:
import requests
from scrapingbee import ScrapingBeeClient
targets = [
‘https://edition.cnn.com/business‘,
‘https://www.nbcnews.com/tech-media‘,
‘https://sports.yahoo.com‘
]
def time_requests():
for url in targets:
start = time.time()
response = requests.get(url)
end = time.time()
print(f‘Requests: {url} took {end - start:.2f} seconds, status {response.status_code}‘)
def time_scrapingbee():
client = ScrapingBeeClient(api_key=‘YOUR_API_KEY‘)
for url in targets:
start = time.time()
response = client.get(url)
end = time.time()
print(f‘ScrapingBee: {url} took {end - start:.2f} seconds, status {response.status_code}‘)
time_requests()
time_scrapingbee()On a representative run, the results looked like this:
Requests: https://edition.cnn.com/business took 2.04 seconds, status 200
Requests: https://www.nbcnews.com/tech-media took 1.37 seconds, status 200
Requests: https://sports.yahoo.com took 5.14 seconds, status 200
ScrapingBee: https://edition.cnn.com/business took 1.14 seconds, status 200
ScrapingBee: https://www.nbcnews.com/tech-media took 0.87 seconds, status 200
ScrapingBee: https://sports.yahoo.com took 2.41 seconds, status 200ScrapingBee posts a significant speedup on all three sites, with the starkest difference on the Yahoo Sports page, which Requests struggles to load in a timely fashion. These speedups compound when it comes to large crawl jobs hitting dozens or hundreds of URLs.
How is this possible? ScrapingBee has already solved many of the fiddly performance and reliability issues that bog down DIY scrapers:
- Geographically distributed proxy pool for low-latency access to sites around the world
- Battle-tested crawler engine that handles edge cases like lazy loading, pop-up modals, and mobile views
- Smart rate limiting and driver emulation to politely work within site thresholds
- Priority queueing system to coordinate jobs and optimize delivery times
This is the power of standing on the shoulders of an industrial-strength crawling pipeline. It‘s how ScrapingBee enables small teams (or even solo developers) to punch above their weight when it comes to scraping news to power ambitious applications.
Going Further
We‘ve covered a lot of ground in this tutorial, but there are always more ways to enhance your scraping prowess. Here are some ideas for taking your news crawler to the next level:
- Move the scheduling logic into a dedicated cron job decoupled from the web server process for better resilience
- Store the scraped headline data in a proper DB like PostgreSQL or Elasticsearch for advanced querying and analysis
- Stand up a more powerful web frontend in a framework like React or Vue for richer data presentation and interactivity
- Integrate additional news APIs like NewsAPI or Currents API to augment your headline corpus
- Apply natural language techniques like entity extraction, document classification or summarization to the scraped content
- Deploy your crawler to a hosting service like Heroku or AWS Elastic Beanstalk to run continuously
Closing Thoughts
Web scraping has a reputation as a dark art reserved for hardcore programmers. But as we‘ve seen, the combination of intuitive tools like ScrapingBee and a little bit of Python know-how can get you pretty far, pretty fast.
Whether you‘re a newsroom developer looking to track your industry, a hedge fund analyst hunting for alternative data, or a hobby coder exploring a passion, automated web scraping can be a game-changing skill to have in your arsenal.
Just be sure to observe responsible scraping practices like honoring robots.txt rules, throttling your requests, and giving back to the site owners whenever possible. With a bit of care and forethought, web scraping is a powerful way to unlock valuable insights and unique datasets.
So get out there and start exploring the wide world of news data! And when you‘re ready to scale up your crawling, give ScrapingBee‘s generous free tier a spin. Your future self will thank you.
