Bing News Scraper

Scrape Bing News search results for any query. Extract article titles, URLs, sources, snippets, and publication dates from Bing News. Filter results by freshness (past day, week, or month). Fast HTTP-only scraper -- no browser or proxy needed.

What does Bing News Scraper do?

Bing News Scraper extracts news articles from Bing News search results. For each article, it returns the headline, direct article URL, source name, snippet text, position rank, and publication time.

The scraper paginates through multiple pages of results to collect as many articles as you need (up to 500 per query). It uses direct HTTP requests with no browser overhead, making it fast and cost-effective.

Key capabilities:

• Extract news articles with full metadata (title, URL, source, snippet, date)
• Filter results by freshness: past day, past week, or past month
• Paginate through multiple pages automatically
• Support multiple search queries in a single run
• Direct article URLs (no redirect decoding needed)
• HTTP-only -- no browser, no proxy, minimal compute cost

Who is Bing News Scraper for?

• PR and communications teams monitoring brand mentions and media coverage
• Market researchers tracking industry news and trends across topics
• Content creators researching trending news stories for articles and social media
• Competitive intelligence analysts monitoring competitor mentions in the press
• Data analysts building news datasets for sentiment analysis or NLP research
• Academic researchers studying media coverage patterns and news cycles
• Financial analysts tracking company and market news for investment decisions
• Journalists finding sources and related coverage for stories

Why use Bing News Scraper over alternatives?

1. No proxy costs -- works from datacenter IPs without residential proxies, keeping per-result costs minimal.
2. HTTP-only -- no browser (Playwright/Puppeteer) overhead. Runs in 256 MB memory with fast execution.
3. Pay-per-result pricing -- you only pay for results actually extracted, not for compute time or failed requests.
4. Freshness filtering -- built-in support for filtering by day, week, or month without custom URL hacking.
5. Multiple queries -- batch multiple search queries in one run to save on start fees.
6. Clean data -- direct article URLs, clean source names, and structured output ready for analysis.

How much does Bing News Scraper cost?

Bing News Scraper uses pay-per-event pricing:

Event	Price
Run started (one-time)	$0.01
Per result scraped	$0.003

Volume discounts are available through Apify's tiered pricing:

Tier	Per result	Discount
Free	$0.003	--
Bronze	$0.0027	10% off
Silver	$0.0024	20% off
Gold	$0.00195	35% off
Platinum	$0.0015	50% off
Diamond	$0.0012	60% off

Example costs

• 10 articles (1 query): $0.01 + 10 x $0.003 = $0.04
• 100 articles (1 query): $0.01 + 100 x $0.003 = $0.31
• 100 articles (10 queries, 10 each): $0.01 + 100 x $0.003 = $0.31
• 500 articles (5 queries, 100 each): $0.01 + 500 x $0.003 = $1.51

Input

Field	Type	Description	Default
queries	array	List of news search queries	(required)
maxResults	integer	Maximum articles to extract per query (1-500)	30
freshness	string	Filter by recency: Day, Week, Month, or empty for all	""

Example input

{
    "queries": ["artificial intelligence", "climate change"],
    "maxResults": 20,
    "freshness": "Week"
}

Freshness filter values

• Empty string (""): No freshness filter, returns all available results
• Day: Articles published in the last 24 hours
• Week: Articles published in the last 7 days
• Month: Articles published in the last 30 days

Output

Each result contains the following fields:

Field	Type	Description
query	string	The search query that produced this result
position	number	Position rank within the query results (1-based)
title	string	Article headline
url	string	Direct URL to the article
source	string	Name of the publishing source (e.g., "Reuters", "BBC")
snippet	string	Article description/summary
publishedAt	string	Publication time as reported by Bing (e.g., "3 hours ago")
scrapedAt	string	ISO timestamp when the result was scraped

Example output

[
    {
        "query": "artificial intelligence",
        "position": 1,
        "title": "New AI Model Breaks Performance Records",
        "url": "https://example.com/ai-model-records",
        "source": "TechCrunch",
        "snippet": "Researchers unveiled a new artificial intelligence model that sets new benchmarks across multiple tasks...",
        "publishedAt": "3 hours ago",
        "scrapedAt": "2026-03-27T12:00:00.000Z"
    },
    {
        "query": "artificial intelligence",
        "position": 2,
        "title": "AI Regulation Bill Advances in Congress",
        "url": "https://example.com/ai-regulation",
        "source": "Reuters",
        "snippet": "A bipartisan bill aimed at regulating artificial intelligence applications passed a key committee vote...",
        "publishedAt": "5 hours ago",
        "scrapedAt": "2026-03-27T12:00:00.000Z"
    }
]

How to use Bing News Scraper

Run from the Apify Console

1. Go to the Bing News Scraper page on Apify Store.
2. Click "Start" to open the input configuration.
3. Enter your search queries in the "Search queries" field.
4. Set the maximum number of results and freshness filter as needed.
5. Click "Start" to run the scraper.
6. View results in the "Dataset" tab when the run completes.

Run via API

curl -X POST "https://api.apify.com/v2/acts/automation-lab~bing-news-scraper/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "queries": ["artificial intelligence"],
    "maxResults": 20,
    "freshness": "Week"
  }'

Run with the Apify JavaScript client

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_TOKEN' });

const run = await client.actor('automation-lab/bing-news-scraper').call({
    queries: ['artificial intelligence', 'machine learning'],
    maxResults: 30,
    freshness: 'Day',
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Run with the Apify Python client

from apify_client import ApifyClient

client = ApifyClient('YOUR_TOKEN')

run = client.actor('automation-lab/bing-news-scraper').call(run_input={
    'queries': ['artificial intelligence', 'machine learning'],
    'maxResults': 30,
    'freshness': 'Day',
})

items = client.dataset(run['defaultDatasetId']).list_items().items
for item in items:
    print(f"{item['position']}. {item['title']} ({item['source']})")

Integrations

Bing News Scraper works with all standard Apify integrations:

• Google Sheets -- export news results directly to a spreadsheet for tracking and analysis
• Slack / Microsoft Teams -- get notified when new articles match your queries
• Webhooks -- trigger downstream workflows when the scraper finishes
• Zapier / Make -- connect to 5,000+ apps for automated news monitoring pipelines
• Amazon S3 / Google Cloud Storage -- store results in cloud storage for archival
• API -- fetch results programmatically for custom applications

Tips for best results

1. Use specific queries -- "Tesla stock price" works better than just "Tesla" for targeted news.
2. Combine with freshness -- use Day freshness for breaking news monitoring, Month for broader research.
3. Batch queries -- put multiple related queries in one run to save on the start fee.
4. Set appropriate limits -- Bing News typically returns up to ~100 unique results per query. Setting maxResults higher than that may just extend runtime without more results.
5. Monitor regularly -- schedule daily runs with Day freshness for continuous news monitoring.

Limitations

• Results are limited to what Bing News indexes and returns for a given query.
• Publication dates are returned as relative strings ("3 hours ago") as reported by Bing, not as absolute timestamps.
• Some news sources may not be indexed by Bing News.
• Very broad queries may return duplicate articles from syndicated sources.
• Maximum 500 results per query (Bing pagination limit).

Changelog

v0.1 (2026-03-27)

• Initial release
• News article extraction with title, URL, source, snippet, and publication time
• Freshness filtering (day/week/month)
• Multi-query support with pagination
• Pay-per-result pricing with volume discounts