What does Ghost Job Detector do?

Ghost Job Detector helps you identify ghost, fake, or reposted job listings on LinkedIn and company career pages. Instead of crawling all job boards, this Actor monitors specific job URLs over time, extracts key signals, and calculates a Hiring Likelihood Score (0–100) to help job seekers distinguish genuine opportunities from fraudulent or outdated postings.

Ghost Job Detector can analyze:

• Job title, company name, and posting URL

• Job description and requirements

• Posting date and last activity timestamp

• Repost detection and frequency analysis

• Ghosting signals (e.g., inactive hiring, repeated postings)

• Hiring Likelihood Score (0–100)

• Red flags and authenticity indicators

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Why monitor LinkedIn and company job listings?

LinkedIn and company career pages host millions of job listings, but not all of them represent genuine hiring opportunities. Many positions are ghost jobs—postings that remain online even when companies aren’t actively hiring. These listings can waste time and distort recruitment insights for researchers and job seekers alike.

By monitoring job URLs over time, Ghost Job Detector helps you:

• Track whether a specific job is still actively hiring

• Identify positions that are repeatedly reposted

• Build a personal job search filter to focus only on legitimate opportunities

• Research hiring patterns and company recruitment strategies

• Analyze labor market trends and see which companies are actively hiring

• Detect fraudulent or misleading job postings before applying

• Create datasets for machine learning models that predict hiring likelihood

This approach allows you to focus on the jobs you care about, rather than crawling entire job boards.

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How to analyze LinkedIn and company job listings

Using Ghost Job Detector on Apify is simple. Instead of scraping entire job boards, you provide specific job URLs that you want to monitor and analyze.

Follow these steps:

1. Go to the Actor page:
   Ghost Job Detector and click Try for free.

2. Enter one or more job posting URLs (LinkedIn or company career pages) in the input field.

3. (Optional) Enable monitoring settings if you want to run the Actor regularly to track changes over time.

4. Click Run to start the analysis.

5. When the run finishes, open the Dataset tab to preview or download your results.

You can export the dataset in formats such as JSON, CSV, Excel, or HTML.

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Input

The Ghost Job Detector Actor accepts the following input:

• job_urls (array or string): One or more LinkedIn or company job posting URLs to analyze.
• testMode (boolean, optional): Set true to skip crawling and use placeholder data for testing.
• use_residential_proxy (boolean, optional): Set true to route requests through residential proxies (useful for LinkedIn).

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Output

Each run produces structured JSON objects with the following fields:

• url: Original job posting URL
• score: Hiring Likelihood Score (0–100)
• classification: Human-readable risk classification
• checkedAt: Timestamp of analysis
• signals: Array of detected ghost-job signals (type, weight, value)
• signalsDisplay: Human-readable summary of signals
• error: Error message if analysis failed

Example for one job:

{
  "url": "https://jobcenter.mv/en/jobs/application-training-specialist",
  "score": 21,
  "classification": "Low Intent",
  "checkedAt": "2026-02-12T20:14:35.412Z",
  "signals": [
    {
      "type": "missing_location",
      "weight": 18,
      "value": null
    },
    {
      "type": "missing_posted_date",
      "weight": 12,
      "value": null
    }
  ],
  "signalsDisplay": [
    "missing_location (18)",
    "missing_posted_date (12)"
  ],
  "error": null
}

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Dataset

The full dataset tab shows results for all analyzed job URLs. Each row corresponds to a single job listing.

Example dataset output for multiple jobs:

[
  {
    "url": "https://jobcenter.mv/en/jobs/application-training-specialist",
    "score": 21,
    "classification": "Low Intent",
    "checkedAt": "2026-02-12T20:14:35.412Z",
    "signals": [
      {
        "type": "missing_location",
        "weight": 18,
        "value": null
      },
      {
        "type": "missing_posted_date",
        "weight": 12,
        "value": null
      }
    ],
    "signalsDisplay": [
      "missing_location (18)",
      "missing_posted_date (12)"
    ],
    "error": null
  },
  {
    "url": "https://jobcenter.mv/en/jobs/aviation-executive-7",
    "score": 21,
    "classification": "Low Intent",
    "checkedAt": "2026-02-12T20:14:35.240Z",
    "signals": [
      {
        "type": "missing_location",
        "weight": 18,
        "value": null
      },
      {
        "type": "missing_posted_date",
        "weight": 12,
        "value": null
      }
    ],
    "signalsDisplay": [
      "missing_location (18)",
      "missing_posted_date (12)"
    ],
    "error": null
  },
  {
    "url": "https://jobcenter.mv/en/jobs/academic-support-officer-1",
    "score": 0,
    "classification": "Low Intent",
    "checkedAt": "2026-02-12T20:14:35.191Z",
    "signals": [
      {
        "type": "placeholder",
        "weight": 0,
        "value": ""
      }
    ],
    "signalsDisplay": [
      "placeholder (0)"
    ],
    "error": null
  }
]

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How much will it cost to scrape job listings?

Apify gives you $5 in free usage credits every month on the Apify Free plan. Depending on the scope of your job search, you may be able to analyze dozens of listings completely free!

But if you need to monitor job listings regularly or analyze large volumes of data, you should grab an Apify subscription. We recommend our $49/month Starter plan—ideal for job seekers and researchers tracking hiring trends.

For large-scale hiring market analysis, get up to 10,000+ job analyses per month with the $499 Scale plan.

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Results

Ghost Job Detector outputs a structured dataset with the following information for each job listing:

[
  {
    "url": "https://jobcenter.mv/en/jobs/application-training-specialist",
    "score": 21,
    "classification": "Low Intent",
    "checkedAt": "2026-02-12T20:14:35.412Z",
    "signalsDisplay": [
      "missing_location (18)",
      "missing_posted_date (12)"
    ]
  },
  {
    "url": "https://jobcenter.mv/en/jobs/aviation-executive-7",
    "score": 21,
    "classification": "Low Intent",
    "checkedAt": "2026-02-12T20:14:35.240Z",
    "signalsDisplay": [
      "missing_location (18)",
      "missing_posted_date (12)"
    ]
  },
  {
    "url": "https://jobcenter.mv/en/jobs/academic-support-officer-1",
    "score": 0,
    "classification": "Low Intent",
    "checked

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Notes:

• url – the original job posting URL.

• score – Ghost-job risk score (0–100, higher means more likely ghost job).

• classification – human-readable hiring likelihood classification.

• checkedAt – timestamp of when the analysis was performed.

• signalsDisplay – list of detected ghost-job signals in a human-readable format.

You can preview this dataset in the Output tab or download it in JSON, CSV, Excel, or HTML formats.

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Tips for interpreting Ghost Job Detector results

• Track patterns over time: Monitor the same company's job listings to see if the same positions are reposted frequently, which may indicate ghost jobs.
• Pay attention to signals: Check the signalsDisplay field for indicators such as missing location, missing posted date, or repeated postings.
• Focus on the Hiring Likelihood Score: Scores closer to 100 indicate a higher risk of ghost jobs; lower scores suggest active hiring.
• Compare multiple sources: If the same job appears on LinkedIn and the company career page, check if the signals differ.
• Validate critical details: Missing job location or posting date can indicate incomplete or outdated listings.
• Use classifications for quick filtering: The classification field provides a human-readable summary (Low Intent, Possible Hire, Actively Hiring, Likely Ghost Job) to help prioritize which jobs to investigate further.

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Is it legal to scrape job listings?

Note that personal data is protected by GDPR in the European Union and by other regulations around the world. You should not scrape personal data (such as individual applicant information or recruiter details) unless you have a legitimate reason to do so.

Job listing data itself is generally considered public information, but you should review the terms of service for LinkedIn and company career pages to ensure compliance. If you're unsure whether your use case is legitimate, consult your lawyers.

We also recommend that you read Apify blog post: is web scraping legal?.