Use LinkedIn Activity Feed Scraper with Engagement Filters with Python

Pull posts, articles, and reposts from LinkedIn profile activity feeds, with reaction/comment/date filters and a per-reaction-type breakdown. Requires a LinkedIn session cookie.

1. Choose the data you need

Review the published README for supported collection modes, examples, and limits. The recipe below uses the current published input definition.

2. Prepare a source-based input

This JSON contains published prefills and required-field defaults. Review every value for your task before running it; it is not a promise of successful collection.

{
  "includeReposts": true,
  "mode": "postUrls",
  "postUrls": [
    "https://www.linkedin.com/posts/williamhgates_how-better-data-helped-us-cut-child-mortality-activity-7329207003942125568-_gfJ"
  ]
}

Download input.example.json and save your reviewed copy as input.json.

Field	Type	Required	Description
`mode`	string	optional	Choose what to scrape: specific public post URLs, a profile's recent posts, or a company's recent posts.
`postUrls`	array	optional	Public LinkedIn post URLs, feed-update URLs, activity URNs or numeric activity IDs. (mode=postUrls)
`profileUrls`	array	optional	LinkedIn profile URLs or public identifiers, e.g. https://www.linkedin.com/in/williamhgates/ or williamhgates. (mode=profiles)
`companyUrls`	array	optional	LinkedIn company URLs or slugs, e.g. https://www.linkedin.com/company/google/ or google. (mode=companies)
`maxPostsPerProfile`	integer	optional	Maximum posts to keep per profile or company. Public profile pages expose about 6-8 recent posts and company home pages 2-10, so larger values do not unlock more history.
`mediaType`	array	optional	Only keep posts of these media types. Public pages reliably expose text, image, video and article; repost is a pure reshare. Leave empty to keep all.
`postTypes`	array	optional	Legacy type filter. Left at its default of six types it changes nothing; any other selection is merged with mediaType.
`datePosted`	string	optional	Keep posts published within this window, measured from the run time.
`minReactions`	integer	optional	Only keep posts with at least this many reactions. Posts without a readable reaction count are treated as zero.
`minComments`	integer	optional	Only keep posts with at least this many comments. Posts without a readable comment count are treated as zero.
`includeReposts`	boolean	optional	When enabled, reposts and quote posts are kept with their postType and repostOf fields. When disabled, they are dropped.
`maxItems`	integer	optional	Global cap on post records in the dataset. Diagnostic error rows are not counted toward this cap.
`proxyConfiguration`	object	optional	Optional. The actor works without a proxy. Supply one only if your runs are consistently blocked.

3. Start an Actor run

Set APIFY_TOKEN in your environment. Python 3.10 or newer is sufficient; this recipe uses no third-party packages.

Execution may incur charges. Review current pricing on Apify. The request asks for a 120-second timeout and a charge limit; these settings are subject to Apify’s pricing and do not guarantee an all-inclusive spending cap.

import json
import os
import urllib.request

# Save the schema-derived example as input.json; review and edit it first.
with open("input.json", encoding="utf-8") as stream:
    actor_input = json.load(stream)

token = os.environ["APIFY_TOKEN"]
request = urllib.request.Request(
    "https://api.apify.com/v2/acts/CIxdgqOHBfl4nTwzB/runs?timeout=120&maxTotalChargeUsd=0.50",
    data=json.dumps(actor_input).encode("utf-8"),
    headers={"Authorization": "Bearer " + token, "Content-Type": "application/json"},
    method="POST",
)
# This starts a potentially billable run. Do not automatically retry this POST.
with urllib.request.urlopen(request, timeout=40) as response:
    run = json.load(response)["data"]
print("Run ID:", run["id"])
print("Status:", run["status"])
# Inspect and download results in Apify Console once the run succeeds.

4. Inspect the actual results

Open the run in Apify Console and wait for a successful completion. Use the published output schema to locate results and the dataset schema to understand declared views. Missing or optional record fields must not be treated as guaranteed values.

One record per LinkedIn post, with full text, author, exact date, reaction and comment totals, top comments, media, post type and repost/quote details.

Questions before you run

Which inputs are required?

No top-level fields are marked required in the published schema. Conditional requirements can still apply; check the README.

What does this guide verify?

The fields, prefills, and schema links are rendered from the published build. This is a usage recipe, not a paid live extraction test. Source availability and collection results can vary.

What if the run request times out?

Check Apify Console before retrying. The first request may already have created a run. Do not automatically repeat a run-creation POST.

Open on Apify Full Actor reference

Generated from source build 2J31IX5LPEdINxWgA and its README and schemas. Source changes regenerate this guide; no third-party editorial claims are inferred.