Truth Score shows live reputation signals beside detected social usernames while the extension is active. It records deepfake, bully/spam, hate, and other incidents in the ledger.
Runs on supported social pages while protection is enabled. The live session timer starts in the panel.
STEP 02
Username Detection
Finds visible usernames as the page changes, including feeds, profiles, posts, and comment sections.
STEP 03
Score Lookup
Checks each detected social ID against the ledger. New clean users show a safe score until incidents are recorded.
STEP 04
AI Analysis
Truth Score AI checks comments for abuse or spam and checks media for deepfake signals.
STEP 05
Pill Injected
A compact score pill appears beside the social ID. Tap or click to expand the current score breakdown.
STEP 06
Ledger Updated
Evidence is stored as a secure hash. Duplicate reports are automatically rejected.
Score Bands
What each score means
Score
Label
Meaning
85–100
Clean ✅
No incidents recorded. Verified trustworthy.
65–84
Mostly Safe 🙂
Minor or older incidents. Generally safe.
45–64
Caution 👀
Some concerning patterns. Stay alert.
30–44
At Risk ⚠️
Multiple incidents. Proceed carefully.
0–29
Dangerous 💀
Serious and repeated violations detected.
Flag Categories
Four types of violations tracked
🤖
Deepfake
Truth Score AI detects AI-generated or manipulated images and supported media frames that show synthetic-content signals.
−12 pts per incident💬
Bully / Spam
Truth Score AI detects harassment, threats, abusive language, and spam patterns in multilingual comments.
−4 to −14 pts per incident??
Hate
Records hate speech, targeted slurs, and identity-based abusive comments detected in social conversations.
?12 pts per incident?
Other
Records supported safety signals that do not fit deepfake, bully/spam, or hate categories.
?4 pts per incident
Future Improvements
Roadmap categories we may add later
These are planned improvements, not active scoring categories today. If released, the product, privacy policy, and Chrome Web Store disclosures will be updated before collection begins.
🕵️
Fake Profile Signals
Identify suspicious public profiles that appear empty, coordinated, impersonating, or repeatedly used for harassment or propaganda-like behavior.
Future category🔬
Scientific / Unscientific Claims
Classify public posts that make science, medical, or health claims as scientific, unscientific, ayurvedic, or not conclusive when evidence is limited.
Future content label📰
News & Allegation Context
Tag online allegations and news items as conclusive or non-conclusive based on credible public outcomes such as court judgments or official records.
Future context label⚖️
Judgment-Based Evidence
Use public legal outcomes to separate proven findings from allegations, rumours, and unresolved claims so users do not confuse signals with conclusions.
Future verification layer
Network Pulse
0Users tracked0Incidents logged8PlatformsLiveWhile active
Privacy by Design
What we do — and don't — store
🔒 No raw text stored
Comments are hashed with SHA256. The original text is never saved to any database.
🖼️ Media minimized
Media is used only for analysis. The ledger keeps the evidence hash, category, score impact, and result.
🧮 No double counting
The same evidence cannot be submitted twice. Hash deduplication prevents score inflation.
✅ Clean users protected
Clean users show a safe score, and duplicate evidence is blocked so the same incident cannot inflate penalties.
Truth Score helps people and teams make better decisions before trust is given. These examples follow the current ledger categories: deepfake, bully/spam, hate, and other incidents.
Personal Safety
Dating & Relationships
Before meeting someone from a dating app, check their social ID. Deepfake or abuse history gives you context before you decide to meet.
Parents & Children
If your teenager is talking to someone online, a clean score can reassure you; a dangerous score can start the conversation that matters.
Friendships & Trust
When someone new enters your circle, Truth Score can reveal patterns of targeted harassment before they get close.
Professional & Business
Hiring
Before an offer letter, teams can review public social behavior signals such as threats, abuse, or repeated harassment patterns.
Brand Collaborations
Brands can check whether a creator has repeated misinformation or unscientific content flags before collaboration becomes liability.
Influencer Marketing
Agencies can evaluate creator trust alongside reach and engagement, using behavior history instead of vanity metrics alone.
Vendor & Partner Vetting
Before working with a freelancer, consultant, or partner, teams can check professional social IDs for relevant red flags.
Community & Social
Comment Sections
Readers can see who has a history of harassment and who appears to be engaging in good faith.
Online Communities
Admins can review incoming members' social IDs before granting access to private communities.
Event Organizers
Conference, workshop, and meetup teams can check social IDs during registration review to spot known abuse patterns.
Health & Safety
Medical Misinformation
Health workers and caregivers can check whether an account has a track record of unsafe or misleading health claims.
Science Communication
Science communicators can show audiences when an account has repeated unscientific flags instead of arguing from one post alone.
Journalism & Research
Source Verification
Journalists can check whether a social source has a history of misinformation before relying on a tip.
Fact-Checking Teams
Investigators can use account history to spot repeated patterns around viral claims and coordinated abuse.
Academic Research
Researchers studying online abuse, misinformation, or harassment can work with structured, hashed incident data.
Legal & Institutional
Evidence for Complaints
Victims of online harassment can point to timestamped, hash-protected incidents instead of relying only on scattered screenshots.
Platform Policy Teams
Trust and safety teams can use Truth Score as an external signal when reviewing accounts already flagged by users.
Cybercrime Investigations
Teams investigating harassment, deepfake abuse, or coordinated campaigns can review a structured ledger of incidents.
Emerging Use Cases
AI Agent Trust
As online agents interact for people, reputation signals can help decide whether an account is safe to engage with.
Web3 & DAO Governance
Communities can review account behavior patterns when assessing manipulation risk around proposals and votes.
Insurance & Risk
Risk teams can consider public social behavior signals as part of broader digital risk reviews.
Marriage & Background Checks
Families doing due diligence can add public digital behavior history alongside existing background checks.
The core insight
You cannot currently verify the behavioral record of most people you meet online.
Truth Score makes that record visible through live score pills, structured categories, hash-protected evidence, and duplicate-safe reporting. The internet gave everyone a voice. Truth Score gives everyone a reputation.
Check Your Score
Enter your social media ID
Search a social username already seen by the ledger to view its latest Truth Score.
How to Improve
Understanding your Truth Score
Your score starts at 100 and changes when supported evidence is recorded. Here is what moves it and how to recover.