A Reuters story travels: the same wire copy appears on many outlets within hours. Our Reuters scraper collects headlines, datelines and timestamps and tracks where stories are picked up.
Plans from €169/month · Free project assessment · Reply within 1 business day
ScrapeIt runs the work as a managed service. We build and run the pipeline over the topics and regions you follow, with the source type and collection timestamp recorded on every row, and hand back CSV, JSON, Excel or a push into your warehouse.
Where the brief is about reach, wire pickup across other outlets runs in the same pipeline and lands in the same schema.
We collect only publicly available data - everything a visitor can see on Reuters - and we collect it legally. Pacing, proxy rotation and rendering are part of the service.
Story records carry the headline, dateline, publication and update timestamps, byline, topics, region and the story identifier.
The canonical URL and story identifier travel with every row, because provenance you can show is worth more than provenance you remember.
Wire pickup records are a separate table and do not touch reuters.com at all. They record where Reuters copy appears in other outlets, identified by the attribution line.
Every row carries the collection timestamp and the source type, so story records and pickup observations are never confused.
Wire pickup tracking is the strongest product here: which Reuters stories are republished, by which outlets, with what delay, and how the headline changes on the way. It runs across the outlets that carry Reuters copy.
Timing analysis around market events is common: publication and update timestamps show when a story appeared and when it changed, and pickup delays show how fast it reached other outlets.
What we never do is plain: log in or go behind a paywall. The dataset holds what any visitor can see; if a brief needs more than that, we are the wrong supplier and say so on the first call.
Reuters is one of the largest news agencies in the world. Most of its journalism reaches readers through other organisations: newspapers, broadcasters, financial terminals and websites that carry its copy. The reuters.com site is one outlet among thousands carrying Reuters stories.
Each story page on reuters.com shows every visitor the story itself: headline, dateline, byline, publication and update timestamps, the topics and region it is filed under, and a story identifier. Those fields are the backbone of a Reuters dataset.
The rest of the picture lives on other sites. Wire copy carries an attribution line wherever it is republished, so the spread of a Reuters story through the media can be read from the outlets that carry it.
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Delivery: CSV, JSON or XLSX files, our API or a JSON feed. MCP server on request.
Full plan details →Briefs that ask for Reuters data usually want one of three things. The first is fast headlines for trading or monitoring: headline, dateline and publication timestamp as soon as a story appears. The second is coverage research: which topics and regions the agency covers, how often and from where, built from topics, regions and datelines. The third is measuring how Reuters stories spread through the media, and that needs no access to reuters.com at all.
The third case is the one clients most often overlook. Wire copy carries an attribution line wherever it is republished, so counting pickups across other outlets shows which stories travelled, how fast and where. That is a legitimate media analysis built entirely from other sources.
All three start from the same fields: headline, dateline, byline, publication and update timestamps, topics and region, with the collection timestamp on every row.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed extraction company, not a tool you have to learn. Our team builds the pipeline, keeps the source type and collection timestamp attached to the data, and repairs the collection when formats change.
You see a sample first, in your format, over the topics or outlets you actually track, so you can judge the value before the project starts.
Yes. We collect headlines, datelines, bylines, publication and update timestamps, topics and story identifiers from the public pages of reuters.com, and track where Reuters copy is picked up across other outlets. We never log in and never go behind a paywall.
Yes. Headlines, datelines, bylines, timestamps and topics are shown to every visitor on reuters.com, and wire pickup is visible on the outlets that carry Reuters copy. We collect only publicly available data - everything a visitor can see on Reuters - and we collect it legally.
Yes. The ScrapeIt Reuters API returns headline, dateline, byline, publication and update timestamps, topics and the story identifier as JSON from an endpoint we host, refreshed on the schedule you set. The same data also comes as CSV or XLSX files or straight into your database.
Yes. Wire copy carries an attribution line wherever it is republished, so pickups can be counted across other outlets. That shows which stories travelled, how fast and where, without touching reuters.com.
Because story records from reuters.com and pickup observations from other outlets answer different questions, and data outlives the people who scoped the project. A source type and collection timestamp on every row keep the two apart when the data is reused later by someone who never saw the brief.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
Reuters data from €169/month. Free project assessment, reply within 1 business day.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
NIP: 5423457175
REGON: 523384582
