Use LinkedIn Keyword & Hashtag Post Discovery Scraper with Python

Search LinkedIn posts by keyword or hashtag with filters for date, author type, industry, connection degree, mentions, and minimum reactions. 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.

{
  "authorType": "",
  "contentType": [
    "articles",
    "posts",
    "documents",
    "videos",
    "images"
  ],
  "countryCode": "auto",
  "datePosted": "",
  "emitNoResultsMarker": true,
  "enrichSdui": false,
  "excludeReposts": false,
  "hydratePosts": true,
  "includeDocumentDetails": false,
  "includeRepostCounts": false,
  "includeUnmatched": false,
  "includeVideoTranscript": false,
  "language": "en",
  "maxResults": 3,
  "mentions": [],
  "mentionsCompany": "",
  "mentionsPerson": "",
  "minComments": 0,
  "minReactions": 0,
  "mode": "auto",
  "monitorMode": false,
  "postUrls": [],
  "postedAfter": "",
  "postedBefore": "",
  "searchQueries": [],
  "searchQuery": "artificial intelligence",
  "sortBy": "relevance",
  "talksAbout": []
}

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

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/wuq1JzPEwyJuIWboy/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 matching LinkedIn post, with full text, author, date, engagement metrics, top comments and media where the public post page exposes them. A no-results marker row ({searchQuery, noResults: true}) is added when a clean search finds nothing.

Questions before you run

Generated from source build zL8UURqaufW1YxiHA and its README and schemas. Source changes regenerate this guide; no third-party editorial claims are inferred.