The Scenario
You're the PR analyst at a mid-size SaaS company. Brand mention alerts came in overnight — 60 news article URLs, a mix of trades, tech blogs, and general business press. Your monthly coverage report is due at end of week and the executive summary is expected to include headline, author, publication, and date for each mention.
Someone set up Google Alerts. The alerts work. What doesn't work is the part where 60 URLs become a structured sheet.
The bad version:
- Open each URL. Find the headline — sometimes it's the page title, sometimes it's an H1, sometimes they differ.
- Scroll to find the byline. Some publications list it at the top, some at the bottom, some not at all.
- Find the publication name. Find the date. Paste all four into the sheet. Next row.
Sixty articles at three minutes each is three hours of work that produces exactly zero insights. The insights come after — from sorting by date, filtering by publication tier, looking at which topics cluster. But you have to collect the data before you can analyze it, and right now the collection is eating the week.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads the sheet and through its built-in ScrapingBee integration it fetches each article URL and extracts the structured metadata — headline, author, publication, date — writing the fields directly into your columns.
For each URL in column A, use ScrapingBee to fetch the page and extract the article headline, author name, publication, and publish date, writing them into columns B, C, D, and E.
What You Get
- Column B: article headline as extracted from the page — H1 or title tag, whichever is the editorial title.
- Column C: author name as listed in the byline.
- Column D: publication name from the site's masthead or meta tags.
- Column E: publish date in the format the site uses — a separate prompt can normalize these if needed.
- Rows where any field is missing flagged with "MISSING" so you know which ones need a manual check.
What If the Data Is Not Quite Ready
Dates come back in inconsistent formats — some as "April 11, 2026," some as "2026-04-11," some as "3 hours ago"
After extracting publish dates into column E using ScrapingBee, normalize all values in column F to ISO format (YYYY-MM-DD) — convert relative dates like "3 hours ago" using today's date as the reference, and flag any date that can't be parsed with "REVIEW."
Some publications don't list a byline — the author field comes back empty for 12 of the 60 rows
For each row where column C (Author) is blank after the initial ScrapingBee scrape, check the page again and look for the author in the meta tags (author, article:author), the schema.org markup, or any contributor credit line — write the found value into column C or "UNLISTED" if genuinely absent.
You need to tag each article by publication tier based on a lookup table in the Tiers tab
After extracting publication names into column D using ScrapingBee, check each value against the publication list in column A of the Tiers tab — write the corresponding tier (Tier 1, Tier 2, Tier 3) into column F, and "UNLISTED" for any publication not found in the lookup.
Kill chain: scrape all 60 URLs, extract metadata, normalize dates, tag by tier, and output a coverage summary sorted by tier and date
For each URL in column A, use ScrapingBee to extract headline, author, publication, and date into columns B through E — normalize dates to ISO format, match publications against the Tiers tab and write tier into column F, then output a Coverage Summary sheet with all six columns sorted by Tier ascending and Date descending.
The coverage report that used to take half the week now starts from a complete dataset.
Try It
Get the 7-day free trial of SheetXAI and open any Google Sheet with news article URLs in column A, then ask it to extract coverage metadata using ScrapingBee. See also: Scrape Competitor Homepages Into a Google Sheet for Messaging Analysis and the full ScrapingBee integration overview.