In the fast-moving worlds of PR, finance, and brand monitoring, “yesterday’s news” is worthless. You need to know what is being published about your brand or your stock ticker right now.
For years, developers relied on NewsAPI.org. It became the standard, but it also became a bottleneck. With enterprise plans starting at $449/month and a significant delay in indexing smaller publications, engineers are looking for alternatives.
In this post, we explore why scraping Google News directly is the superior strategy for coverage and cost—and how to do it via API.
The Limitations of Legacy News APIs
1. The “Headline” Problem
Traditional APIs like NewsAPI often rely on RSS feeds from major publishers (CNN, BBC, NYT). They often miss:
- Niche industry blogs.
- Local news stations.
- Press releases on smaller wires.
If you are monitoring a crisis or a small-cap stock, these “long-tail” sources are where the story breaks first.
2. The Cost Barrier
NewsAPI.org
The “Business” plan costs $449/mo. Even the basic plan is limited.
SerpApi
Charging $0.02 - $0.03 per search for Google News results makes high-frequency monitoring expensive.
For a detailed cost comparison, see our pricing analysis.
The Superior Alternative: Google News via SearchCans
Instead of relying on a curated list of publishers, why not query the world’s largest news aggregator directly?
Google News aggregates content from thousands of sources instantly. By using SearchCans to scrape Google News, you get:
- Global Coverage: If it’s on Google, you get it.
- Real-Time Data: No waiting for an RSS refresh.
- Structured JSON: We parse the title, source, date, and thumbnail for you.
Code Tutorial: Building a “Brand Watchdog” in Python
Let’s build a simple script that checks for negative news about a brand (e.g., “Tesla”) every 10 minutes.
The Request
To get news specifically, we use the dedicated news engine or tbm parameter.
import requests
import time
API_KEY = "YOUR_SEARCHCANS_KEY"
BRAND = "Tesla"
def check_news():
print(f"🔍 Scanning news for {BRAND}...")
response = requests.post(
"https://www.searchcans.com/api/search",
json={
"s": f"{BRAND} crash OR lawsuit OR recall", # Advanced search operators
"t": "google",
"tbm": "nws", # Trigger Google News tab
"d": 10
},
headers={"Authorization": f"Bearer {API_KEY}"}
)
data = response.json()
if data.get("code") == 0:
articles = data.get("data", [])
for article in articles:
# Simple sentiment keywords check
print(f"📰 {article['title']} - {article['source']}")
print(f"🔗 {article['url']}")
print("-" * 20)
else:
print("Error fetching news")
# Run continuously
while True:
check_news()
time.sleep(600) # Check every 10 minutes
Advanced: Sentiment Analysis Integration
Combine news monitoring with sentiment analysis:
from textblob import TextBlob
def analyze_sentiment(article_title):
blob = TextBlob(article_title)
polarity = blob.sentiment.polarity
if polarity < -0.3:
return "negative"
elif polarity > 0.3:
return "positive"
else:
return "neutral"
def check_news_with_sentiment():
response = requests.post(
"https://www.searchcans.com/api/search",
json={
"s": "Tesla",
"t": "google",
"tbm": "nws",
"d": 20
},
headers={"Authorization": f"Bearer {API_KEY}"}
)
data = response.json()
negative_count = 0
for article in data.get("data", []):
sentiment = analyze_sentiment(article['title'])
if sentiment == "negative":
negative_count += 1
send_alert(article) # Send to Slack/Email
return negative_count
Use Cases for News Monitoring
1. Crisis Management
Detect negative coverage early:
CRISIS_KEYWORDS = [
"lawsuit",
"recall",
"scandal",
"investigation",
"controversy"
]
def crisis_monitor(company):
for keyword in CRISIS_KEYWORDS:
query = f"{company} {keyword}"
results = check_news(query)
if len(results) > 0:
alert_pr_team(results)
2. Competitive Intelligence
Track competitor mentions:
def competitor_monitor(your_company, competitors):
all_companies = [your_company] + competitors
mentions = {}
for company in all_companies:
results = check_news(company)
mentions[company] = len(results)
return mentions
3. Financial Market Intelligence
Monitor stock-moving news:
def stock_news_monitor(ticker):
keywords = [
f"{ticker} earnings",
f"{ticker} acquisition",
f"{ticker} CEO",
f"{ticker} revenue"
]
all_news = []
for keyword in keywords:
news = check_news(keyword)
all_news.extend(news)
return deduplicate_news(all_news)
For more on building market intelligence platforms, see our dedicated guide.
Integration with Alerting Systems
Slack Integration
import requests
def send_slack_alert(article):
webhook_url = "YOUR_SLACK_WEBHOOK"
message = {
"text": f"🚨 News Alert: {article['title']}",
"attachments": [{
"color": "danger",
"fields": [
{"title": "Source", "value": article['source']},
{"title": "URL", "value": article['url']}
]
}]
}
requests.post(webhook_url, json=message)
Email Notifications
import smtplib
from email.mime.text import MIMEText
def send_email_alert(articles):
msg = MIMEText(f"Found {len(articles)} new articles")
msg['Subject'] = 'News Alert'
msg['From'] = 'alerts@yourcompany.com'
msg['To'] = 'team@yourcompany.com'
with smtplib.SMTP('smtp.gmail.com', 587) as server:
server.starttls()
server.login('user', 'password')
server.send_message(msg)
Building a News Archive
Store news data for historical analysis:
import sqlite3
from datetime import datetime
def store_article(article):
conn = sqlite3.connect('news_archive.db')
c = conn.cursor()
c.execute('''
INSERT INTO articles (title, source, url, date, sentiment)
VALUES (?, ?, ?, ?, ?)
''', (
article['title'],
article['source'],
article['url'],
datetime.now(),
analyze_sentiment(article['title'])
))
conn.commit()
conn.close()
def get_trend_analysis(company, days=30):
# Query historical data
# Calculate sentiment trends
# Return visualization data
pass
Comparison: NewsAPI vs SearchCans
| Service | NewsAPI.org | SerpApi | SearchCans |
|---|---|---|---|
| Source | Curated Publishers | Google News | Google News |
| Price | $449/mo (Business) | ~$150/mo (Production) | $0.56 / 1k requests |
| History | 1 month (Basic) | Real-time | Real-time |
| Commitment | Monthly Sub | Monthly Sub | Pay-As-You-Go |
| Coverage | Major outlets only | Comprehensive | Comprehensive |
Advanced Features
Multi-Language Monitoring
def multi_language_news(company, languages):
results = {}
for lang in languages:
results[lang] = check_news(
company,
language=lang
)
return results
# Monitor in English, Spanish, Chinese
news = multi_language_news("Tesla", ["en", "es", "zh"])
Geographic Filtering
def regional_news(company, location):
results = check_news(
f"{company} {location}",
geo_location=location
)
return results
# Get Tesla news specifically from California
ca_news = regional_news("Tesla", "California")
For more automation patterns, see our LangChain integration guide.
Best Practices
- Deduplication: News appears across multiple sources
- Rate Limiting: Be respectful even with unlimited concurrency
- Error Handling: Network failures happen
- Data Retention: Archive important alerts
- Alert Fatigue: Set appropriate thresholds
Conclusion
For building financial terminals, PR dashboards, or AI news summarizers, direct access to Google News data is the gold standard. With SearchCans, you get that data at a fraction of the cost of legacy news APIs.
Start monitoring the world’s news today. Check out our complete documentation or explore our pricing options.