Daily News & Article Data Collection from 24 Websites

At a Glance

Industry Media intelligence / research

Market United States

Sources 24 news & forum websites

Cadence Daily

Focus Article metadata, publication data, deduplication

Delivery Structured daily dataset (JSON / CSV)

Who is this for? (ICP)

Best fit: A media-monitoring, research, analytics, or brand-intelligence team that needs a consistent, structured stream of published content from many sources — where reading or manually collecting from each site is not viable.

Core pain points this solves:

• 24 sources means 24 different HTML structures and publishing patterns.
• Sites change layouts without warning, breaking in-house scrapers.
• The same story appears on multiple sites — duplicates pollute the dataset.
• The data is only useful if it arrives reliably, every single morning.

Success looks like: One clean, deduplicated file every day covering all 24 sources — arriving without fail, in a stable schema.

Why is multi-source news collection harder than it looks?

Because each additional source multiplies the failure surface. One scraper is easy; 24 scrapers running daily is an operations problem:

• Any of the 24 sites can change layout on any day, silently breaking extraction.
• A source that returns zero articles looks like a successful run unless you check for it.
• Different sites publish at different times, so "daily" needs a sensible cut-off.
• Stories syndicate across sites, so raw collection produces heavy duplication.

The hard part isn't scraping a news site. It's 24 of them, every day, without silent gaps.

What was the challenge?

The client needed comprehensive daily coverage across a defined set of 24 news and forum sites. Building this in-house had three failure modes they wanted to avoid:

• Maintenance load — 24 scrapers is a permanent engineering commitment.
• Silent breakage — a site redesign returns partial or empty data, and nobody notices for days.
• Dirty data — duplicates, inconsistent date formats, missing authors, mixed schemas.

They needed the dataset to be a dependable input, not a daily firefight.

How was it solved?

Actowiz built a managed multi-source news pipeline:

• 24-source coverage — each site handled with its own extraction logic, output into one unified schema.
• Daily scheduled runs with a consistent cut-off, so each day's file is comparable.
• Full metadata capture — headline, author, publication date/time, section/category, URL, and body text where applicable.
• Deduplication — syndicated and near-duplicate stories collapsed, so the dataset reflects unique content.
• Coverage validation — this is the critical part: the pipeline checks that every source returned a plausible volume. A source returning zero articles triggers an alert instead of quietly shipping a gap.
• Managed maintenance — when a site redesigns, Actowiz fixes the extractor; the client's schema never changes.

Key takeaways

• With multi-source collection, the difficulty scales with source count, not complexity.
• Coverage validation is non-negotiable: a source returning zero must alert, not ship silently.
• Deduplication is essential when stories syndicate across sites.
• One unified schema across many sources is what makes the data actually usable.
• Only public, published article metadata and content are collected — respecting each site's terms.

Frequently asked questions

What news data can be collected?

Article headline, author, publication date and time, section or category, URL, and body content where permitted — normalized across all sources into one schema.

How many news sources can be covered?

This engagement covered 24 sites; the approach scales to more. The challenge is reliability across sources, not any single source.

How are duplicate and syndicated stories handled?

Near-duplicate and syndicated articles are detected and collapsed, so the dataset reflects unique content rather than the same story repeated.

What happens if one source breaks or returns nothing?

A coverage check compares each source's volume against its recent baseline and raises an alert — so a silent gap never ships as a "successful" file.

Is this compliant?

The pipeline collects publicly published content and metadata, respecting site terms — not personal data or content behind access controls.