Compare Facebook Posts Scraper Side by Side
Compare Facebook Posts Scraper Side by Side — a practical guide with a ready-to-run configuration and structured output.

Need to compare facebook posts scraper across targets? Thirdwatch's Facebook Posts Scraper turns it into structured rows — the fields you need, ready to export.
Skip the setup: Run this as a ready-to-go task on Apify — pre-loaded with the configuration from this guide.
Why Compare side by side works
Put the same fields side by side for every target — the deltas are where decisions happen. This guide shows the configuration that pulls facebook posts scraper data from Facebook Posts Scraper.
How does this compare to the alternatives?
Manual copy-paste works for a handful of rows; dedicated per-site scrapers on other platforms either gate the same data behind sign-in or charge flat monthly fees regardless of usage.
Why this Actor
- Purpose-built for Facebook Posts Scraper — no generic crawling config to tune
- Pay-per-result pricing — you pay only for rows that come back
- Structured output ready for JSON/CSV export or API consumption
- No login, cookies, or API keys needed for the public surfaces it reads
How to do it in 3 steps
Step 1: Configure the input
Set the inputs as shown below — mode takes the targets, and the optional fields bound the run size.
Step 2: Run the Actor
Run it from the console, the API, or the linked saved task. One dataset row is written per record.
Step 3: Use the output
Every row carries the fields shown in the sample — export as JSON, CSV, or feed it straight into your pipeline.
{
"pageUrls": [
"https://www.facebook.com/nasa"
],
"maxResults": 10
}Each dataset row looks like:
{
"record_type": "post",
"post_id": "1639114777583892",
"post_url": "https://www.facebook.com/NASA/posts/pfbid02FDWE6z2u4MfbHnirq8BdWUYtYi1cdDNEiayPTquFJDjCKi92ULJoWM5Q3U7e94Ddl",
"story_id": "UzpfSTEwMDA0NDU2MTU1MDgzMToxNjM5MTE0Nzc3NTgzODkyOjE2MzkxMTQ3Nzc1ODM4OTI=",
"text": "Welcome to the Artemis Accords, San Marino \ud83c\uddf8\ud83c\uddf2\n\nSan Marino has joined 75 other nations by committing to the safe and sustainable exploration of space. https://go.nasa.gov/4iKdyyL",
"created_at": "2026-09-25T20:31:03+00:00",
"timestamp": 1790368263,
"author_name": "NASA - National Aeronautics and Space Administration",
"author_id": "100044561550831",
"author_url": "https://www.facebook.com/NASA",
"media_type": "photo",
"image_urls": [
"https://scontent.fkkc4-2.fna.fbcdn.net/v/t39.99422-6/825340597_3467943383376834_2458584074081235097_n.png?stp=dst-jpg_tt6&cstp=mx2048x1670&ctp=s640x640&_nc_cat=107&_nc_map=urlgen_bucketless&ccb=1-7&_nc_sid=127cfc&_nc_ohc=vd3znTnyBN4Q7kNvwGTQ9uk&_nc_oc=AdrVH0W4QjZ56jneUUREYwE6B7Pbr9Z9qRZQ5jakMZCL7kwjF9Mvbu680M-C3sKL-3E&_nc_zt=14&_nc_ht=scontent.fkkc4-2.fna&_nc_gid=b0u6SiDmQeXci8asG_6XzQ&_nc_ss=7d289&oh=00_AQKp6nnNsPU7xkQba8CM96UbOtIX7ak-4J5EXm9823MRWA&oe=6ABD5CD6"
]
}What to watch for
Rate the targets reasonably — large batches benefit from modest concurrency. Source sites may throttle heavy bursts; retry once and widen filters if a target surface is thin.
Related use cases
- Feed Facebook Posts Scraper Data Into Your Pipeline
- Extract Facebook Posts Scraper at Scale
- Monitor Facebook Posts Scraper on a Schedule
- Export Facebook Posts Scraper to CSV
- All Thirdwatch use-case guides
Run the Facebook Posts Scraper on Apify Store — pay per result, free to try, no credit card to test.
Frequently asked questions
What does Facebook Posts Scraper return?
One dataset row per result — with the fields shown in the sample output. Export as JSON, CSV, or Excel.
How do I control run size?
`mode` selects the targets and the max/limit fields bound how many rows come back. Start small, then scale.
Can I run this on a schedule?
Yes — save it as a task and attach a schedule in Apify Console for daily/weekly pulls.
Is this data public?
The Actor collects publicly visible data only — the same information a logged-out visitor sees.
What formats can I export?
JSON, CSV, Excel, XML, or direct API access to the dataset — plus webhooks and integrations.
What if a run returns fewer rows than expected?
The source limits some results; retry once, and widen the query or filters if the target surface is genuinely thin.