Ghost Job Detector

Detect fake, abandoned or "ghost" job postings before you waste your time applying.

This actor tracks job listings over time, captures changes (reposts, title/location modifications, disappearance), extracts meaningful signals and calculates a Hiring Likelihood Score — helping you quickly understand which jobs are actually worth applying to.

Free to use until February 16, 2026
(Apify platform usage costs — compute, storage, proxies — still apply)

✨ Features

• One-time check or continuous monitoring of job postings
• Works with LinkedIn and many other job boards (that allow public access)
• Detects common ghost-job signals:
  • No changes for many weeks/months
  • Reposts with near-identical content
  • Sudden disappearance
  • Very short or unchanged description
  • Title/location changes without real updates
• Outputs clear Hiring Likelihood Score (0–100) + human-readable classification
• Built-in residential proxy support (strongly recommended for LinkedIn)

⚡ How It Works

flowchart LR
    A[Job URLs] --> B[Periodic Snapshots]
    B --> C[Signal Extraction]
    C --> D[Hiring Likelihood Score & Classification]
    D --> E[RESULTS Dataset]

```markdown
| Field                 | Type           | Required?     | Default | Description                                                                |
|-----------------------|----------------|---------------|---------|----------------------------------------------------------------------------|
| `job_urls`            | string / array | **Yes**       | —       | Job posting URLs (comma, semicolon, newline separated or array)           |
| `run_mode`            | string         | **Yes**       | —       | `"single"` – one-time check<br>`"monitor"` – continuous tracking          |
| `monitor_days`        | integer        | If `monitor`  | —       | How many days to keep monitoring (ignored in single mode)                 |
| `use_residential_proxy` | boolean      | No            | `false` | **Strongly recommended = true** for LinkedIn (helps avoid blocks)         |

Input Examples

Continuous monitoring (recommended for best ghost detection):

{
  "job_urls": [
    "https://www.linkedin.com/jobs/view/4350325108",
    "https://company.com/careers/software-engineer-xyz-12345"
  ],
  "run_mode": "monitor",
  "monitor_days": 14,
  "use_residential_proxy": true
}

Quick single check (fast scan):

{
  "job_urls": "https://www.linkedin.com/jobs/view/3987654321",
  "run_mode": "single",
  "use_residential_proxy": true
}

📤 Output (RESULTS dataset)

One clean object per job URL.

Field	Type	Description / Example values
url	string	Original job posting URL
score	number	0–100 (higher = higher probability it's a ghost job)
classification	string	"Actively Hiring", "Possible Hire", "Low Intent", "Likely Ghost Job"
signals	array[object]	List of detected signals (type, weight, value, message)
checkedAt	string (ISO)	Timestamp of the last meaningful snapshot used for scoring
error	string | null	Error message if processing failed (page not found, blocked, parsing issue…)

💡 Tips for Best Results

Always enable residential proxies when checking LinkedIn jobs Monitor for at least 7–14 days for significantly more reliable ghost detection Use the score as a strong filtering signal — not as a 100% final verdict Start with "single" mode to quickly scan many jobs, then monitor only the promising ones