Use LinkedIn Hashtag Feed Engagement Scraper with Python

Pull posts published under one or more LinkedIn hashtags, with author name, headline, content, media type and reaction/comment/repost counts. Works without login; a session cookie unlocks more results.

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.

{
  "authorType": "",
  "datePosted": "",
  "dedupeAcrossHashtags": false,
  "excludeReposts": false,
  "hashtags": [
    "AI"
  ],
  "includeComments": true,
  "includeVideoTranscript": false,
  "maxPostsPerHashtag": 3,
  "mediaTypeFilter": "",
  "minComments": 0,
  "minReactions": 0,
  "postedAfter": "",
  "postedBefore": "",
  "sortBy": "relevance"
}

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

Field	Type	Required	Description
`hashtags`	array	required	Hashtags to collect. Accepts #AI, AI, artificial-intelligence, https://www.linkedin.com/feed/hashtag/ai/ or .../feed/hashtag/?keywords=ai. Duplicates are removed.
`maxPostsPerHashtag`	integer	optional	Maximum posts to keep for each hashtag after the hashtag check and filters are applied.
`datePosted`	string	optional	Restrict posts to a recent window. Applied by the search index and re-checked against each post's published date.
`postedAfter`	string	optional	Keep posts published on or after this date (YYYY-MM-DD, UTC day). Leave empty to disable.
`postedBefore`	string	optional	Keep posts published on or before this date (YYYY-MM-DD, whole UTC day). Leave empty to disable.
`sortBy`	string	optional	Order the posts found in this run. Relevance keeps the search index order; date and engagement sort only the posts that were found, not all of LinkedIn.
`mediaTypeFilter`	string	optional	Keep only one post type. Text, image, video, article and repost are the types public posts expose most often.
`authorType`	string	optional	Keep only posts written by a person or by a company page. Author type is read from the public author profile link.
`minReactions`	integer	optional	Drop posts with fewer than this many reactions. A post with no visible reaction count is treated as 0.
`minComments`	integer	optional	Drop posts with fewer than this many comments. A post with no visible comment count is treated as 0.
`includeComments`	boolean	optional	Include the first public page of comments (up to 10) with each post. No extra request is made.
`includeVideoTranscript`	boolean	optional	Add the public transcript and caption URL when a video post publishes them.
`excludeReposts`	boolean	optional	Drop posts that reshare another post.
`dedupeAcrossHashtags`	boolean	optional	Return each post once even when it appears under several requested hashtags. By default a post is returned once per hashtag.

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/bTHVIwGpTDUckiRZf/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 public LinkedIn post found for a hashtag, with author, followers, text, media, engagement and first-page comments.

Questions before you run

Which inputs are required?

The published required fields are hashtags

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 NnpM3ONrdHeX2rfXi and its README and schemas. Source changes regenerate this guide; no third-party editorial claims are inferred.