NEWS & MEDIA DATA

The news, as data.
Fresh enough to act on.

// THE SHORT ANSWER

News and media data feeds deliver structured article data from thousands of public news sources — headlines, full text, publish time, author, entities and sentiment — as real-time streams or historical archives. iWeb Data Scraping collects public news content, structures it consistently, and delivers via API or webhooks, so AI/RAG pipelines can ground answers in current events and finance teams can turn news flow into signal. Freshness is a defined SLA, because for both use cases, stale news is no news.

Get free sample in 48h See delivery specs
99%+field accuracy, QA-verified
48hfree sample turnaround
24/7pipeline monitoring
ISO 27001+ 9001 certified

Key facts

  • Sources: Thousands of public news and media sites; custom source lists supported
  • Freshness: Real-time (minutes from publish) or scheduled/historical
  • Enrichment: Entity recognition, ticker mapping, sentiment scoring
  • Delivery: Streaming API, webhooks, JSONL archives, warehouse-direct

News is simultaneously the freshest and messiest data on the web: thousands of sources, every one with a different layout, publishing continuously, in dozens of languages. Two audiences need it structured — AI teams building RAG systems that must answer with current events, and finance teams turning news flow into tradeable signal.

We normalize the chaos: article text extracted cleanly (no nav, ads or boilerplate), consistent metadata (source, publish time, author, section), entity recognition (companies, tickers, people, places) and sentiment scoring — delivered real-time as articles publish, or as historical archives for backtesting and training. One schema across thousands of sources, so your pipeline ingests news, not HTML.

WHAT'S INCLUDED

Thousands of sources,
one clean schema.

Clean full-text extraction

Article body only — navigation, ads and boilerplate stripped — so downstream models read content, not clutter.

Consistent metadata

Source, publish timestamp, author, section and URL normalized across every source into one schema.

Entity & ticker tagging

Companies, tickers, people and places recognized and tagged — news mapped to the entities you track.

Sentiment scoring

Article and entity-level sentiment, so news flow becomes a quantifiable signal, not a reading list.

Real-time or historical

Stream articles as they publish, or pull historical archives for backtesting and model training.

Multi-language

Major world languages supported, with source-language and translation options on request.

SEE THE DATA FIRST

What you'll actually receive.

Real sample structure from this feed. Your free 48-hour sample comes in your category, in this shape — CSV, JSON or straight to your warehouse.

News article feed — clean text, entities, sentiment, real-time.
published_at source headline_snippet entities sentiment
2026-07-08 08:12 Reuters Retailer expands quick-commerce... ExampleMart; India +0.42
2026-07-08 07:50 Economic Times Q1 margins under pressure as... RivalCo -0.31
2026-07-08 06:20 Bloomberg New entrant raises $200M for... StartupX +0.55
2026-07-07 21:05 Mint Regulator reviews delivery-fee... Sector-wide -0.12
↑ Sample structure — illustrative values. Your data reflects your platforms and category. Get this for your data →
DELIVERY SPECS

The details procurement asks for.

SOURCES Thousands of public news and media sites; custom source lists supported
FRESHNESS Real-time (minutes from publish) or scheduled/historical
ENRICHMENT Entity recognition, ticker mapping, sentiment scoring
DELIVERY Streaming API, webhooks, JSONL archives, warehouse-direct
TIMELINE Free sample in 48h · production 1–2 weeks
PRICING By sources × freshness × enrichment; historical archives priced separately
COMPLIANCE Public content only · provenance documented · ISO 9001/27001 · NDA-first

Every engagement is NDA-first and starts with a free sample — judge the data before any commitment.

THE HONEST COMPARISON

Why a data layer, not a DIY script or a rigid tool

In-house DIY Generic SaaS tool iWeb Data Scraping
Setup & maintenance You build scrapers, fight anti-bot, fix breakages weekly Rigid templates, breaks on site changes, slow support Fully managed — we build, monitor and fix, you never touch a proxy
Data quality Best-effort, no QA layer, silent failures Generic parsers, frequent gaps 99%+ field accuracy, QA-verified, monitored 24/7
Coverage Limited to what you can maintain Only supported sites Any public site or app, at scale
Compliance Your legal risk to manage alone Often opaque about methods ISO-certified, PII-scrubbed, NDA-first, documented
Time to value Weeks to months of engineering Fast but inflexible Free sample in 48h, production in days
WHO USES THIS

Built for the person
who owns the number.

AI / RAG TEAM

Ground answers in current events

Real-time news feeds let your agents and RAG systems cite what happened today, not what training saw last year — pairs with agent feeds.

FINANCE / ALT-DATA

News flow as signal

Entity-tagged, sentiment-scored news mapped to tickers — a standard input alongside our alternative data panels.

BRAND / PR

Monitor coverage at scale

Track mentions, sentiment and share of voice across thousands of outlets — reputation as a measurable trend.

RELATED:AI Agent & RAG Feeds →Alternative Data →Social Media Scraping →
FAQ

Before the first call.

Two main uses: grounding AI systems (RAG pipelines and agents that must answer with current events) and generating financial signal (entity-tagged, sentiment-scored news flow mapped to tickers). Brand monitoring, PR measurement and market research are common secondary uses — anywhere structured, current news beats manually reading sources.

Freshness is an SLA you choose: real-time feeds deliver articles within minutes of publication, suitable for trading signals and live AI grounding; scheduled or historical modes suit backtesting and training. For both live use cases, we treat latency as a measured commitment, not a vague promise.

Full article text, cleaned — the body is extracted with navigation, ads and boilerplate removed, alongside structured metadata (source, timestamp, author, section). For AI training and RAG this matters: models should ingest the content, not the surrounding page furniture.

Yes — entity recognition tags companies, tickers, people and places in each article, and sentiment is scored at article and entity level. This turns raw news into a signal mapped to the exact names you track, which is why finance teams use it alongside point-in-time alternative-data panels.

Get a free sample dataset in 48 hours.