social-analyzer: Username and Social Profile Search Across 1000+ Networks

You're using Sherlock for username enumeration. Now, you're eyeing social-analyzer. The question is, does it do anything Sherlock doesn't, and is it worth the extra hassle?

If you're drowning in false positives, social-analyzer might be your lifeline. Its confidence scoring helps you skip manual checks on weak leads.

Coverage gaps on niche platforms are a problem social-analyzer addresses, with over 1000 sites in its list.

You can get machine-readable output for a pipeline with social-analyzer's JSON output and API mode.

If a simple yes/no on 40 major platforms is all you need, Sherlock still wins for speed. That's it.

What social-analyzer Does

social-analyzer takes a username and searches it across 1000+ social networks at once. It uses multiple detection methods, including HTTP response analysis, metadata inspection, page content pattern matching, and custom regex patterns for each site.

When a match is found, the output isn't just a URL. You get a confidence score showing how many detection signals confirmed the profile's existence. Extracted metadata is included where available.

The tool has three operational modes. Direct search mode is for interactive CLI use; run a username, and results populate in real time. Background scanning mode queues searches for processing and retrieves results when complete; this is best for running lists of username variants. API mode exposes the search functionality as a REST endpoint for programmatic integration.

The output is structured for triage. A 1000-site search might return 847 negative results, 120 Low confidence hits, 28 Medium hits, and 12 High confidence hits. A basic tool would return 140 claimed positives, all of which would require manual verification.

Operators save time by focusing on high-confidence hits, while the tool handles the rest.

Detection Methods and Confidence Scoring

Username enumeration has a significant issue: false positives. Many social platforms return a 200 status code for any username path, regardless of whether the account exists. Tools that rely solely on status codes will flag every one of these as existing, generating numerous false hits. On a large-scale search across 1000 sites, this translates to dozens of misleading leads that consume valuable triage time.

social-analyzer addresses this problem with advanced detection logic. It examines metadata for profile-specific indicators like structured data, meta tags, canonical URLs. It also inspects page content for user-generated text, follower counts, bio fields. Custom patterns are applied for platforms with unusual URL structures. All these signals are aggregated into a confidence score.

A High confidence hit means multiple indicators confirm the profile's existence. This requires finding a populated bio field, a profile image URL, and a correctly formatted canonical URL. Low confidence results might only have a 200 status code without supporting content. These are worth noting but not a priority. A result with None confidence means there weren't enough signals to confirm existence.

By making these distinctions, social-analyzer significantly cuts down on triage work. Focusing on High and Medium confidence results at the outset allows for more efficient verification. The approach targets the most promising leads first.

Operational Modes and Output

Most investigators start with CLI mode. Run the tool, enter a username, and optionally filter by site category or confidence threshold. Output streams in as it's collected.

The tool also offers a web interface with a dashboard for the same data, useful for sharing results with colleagues who prefer visuals or reviewing large datasets that scroll off the terminal screen.

API mode allows for automation. The tool exposes a REST endpoint that accepts username queries and returns JSON. Confidence scores, profile URLs, and metadata are structured for easy processing. API mode is a game-changer for OSINT pipelines, investigation databases, or workflows that need to process usernames automatically.

JSON output is available everywhere, essential for programmatic filtering by confidence threshold. You can extract high-confidence results, feed URLs into a scraper, or write hits to a database. If your workflow goes beyond reading terminal output, JSON is the way to go. It works.

social-analyzer vs Sherlock

When comparing social-analyzer and Sherlock, it's clear they serve similar but distinct needs.

Coverage: social-analyzer checks over 1,000 sites, while Sherlock queries more than 400. Both tools have extensive lists of sites, with major platforms well-covered. The difference lies in niche and regional platforms; social-analyzer covers an extra 600 sites, which may be crucial or not, depending on your case.

Detection quality is where social-analyzer excels. Sherlock simply reports "found" or "not found". social-analyzer provides a confidence level, based on multiple signals. This scoring system is a significant advantage for investigators who are tired of verifying false positives. You can now triage 12 high and 28 medium confidence results, instead of 140 blind hits.

Integration: social-analyzer's API and JSON output make it more suitable for automated workflows. Sherlock's simple output is sufficient for one-off manual checks, as it's quick and easy. However, for handling multiple usernames or integrating into a pipeline, social-analyzer is a better fit. Its architecture supports this type of work. Sherlock's speed is suitable for simple tasks.

Practical Investigation Workflow

Effective Operational Pattern

Start with confidence filtering. Filter results to High and Medium confidence hits, review those first. Low confidence hits are a second-pass list; check them if the investigation warrants it, but don't prioritize them over confirmed results.

For username variant investigations, use background scanning mode. It is more efficient than sequential direct searches. Submit the full variant list and let it run, then retrieve the complete result set when finished. Searching multiple username variants one by one, such as five variants, is slower than submitting them all at once.

High confidence results with metadata provide direct pivot material. No need to visit platforms manually. Look for bios with distinct phrases, check display name variants that differ from the search username, and reverse-search profile image URLs. These metadata points let you pivot without extra manual steps. Work the metadata first, then open browser tabs if needed.

It saves time. Operators often miss things. Prioritizing confirmed results, High and Medium confidence hits, pays off.

Limitations and Honest Assessment

The 1000+ site count isn't a hard limit. It's more like a ceiling that slopes downward. Top-tier platforms, such as Twitter, Instagram, Reddit, GitHub, LinkedIn, TikTok, have solid detection logic in social-analyzer. The further down the list you go, the spottier the maintenance gets.

Niche forums and regional platforms have detection accuracy that can range from solid to stale. If you don't recognize the platform, take the results with a healthy dose of skepticism, no matter what the confidence score says.

Searching all 1000+ sites takes time, as you're querying over a thousand platforms, with multiple checks per platform. This is slower than Sherlock's 400-site search. If you need quick results, social-analyzer's full search isn't the right tool; broader coverage with confidence scores takes longer than narrower coverage with simple yes/no results.

Metadata extraction isn't always a given. A High confidence result means the profile exists, but it doesn't mean you got any extra data. Some platforms return rich profile info, while others just confirm the URL. Plan your investigation around URL confirmation, and treat any extra metadata as a bonus.

Verdict

social-analyzer has racked up 22k+ GitHub stars. It is not just another username checker. It scans over 1000 networks. It gives you a confidence score to prioritize. It provides output in JSON that you can use. It has an API for automation.

For investigators already using Sherlock, social-analyzer is not a replacement. It is an upgrade for when you need to cover more ground, more hits, and better output. Sherlock still works for quick binary checks.

To get the most out of social-analyzer, run it widely, then focus on High and Medium hits. Use profile data to dig deeper. Switch to Sherlock for fast yes/no checks on short lists.

The tool is best suited for OSINT investigators needing broad platform coverage with confidence-rated triage output and API integration for automated username investigation workflows. You can find social-analyzer on GitHub at qeeqbox/social-analyzer.