Social Authenticity Network
  • Introduction
    • What is #SocAuth?
    • AN Social Authenticity Network
    • Vision & Mission
  • PROBLEM AND SOLUTION
    • Problem Statement
    • Solution Statement
  • Markets and Competitors Analysis
    • Markets & Growth
    • AN vs Competitors
  • AN Social Authenticity Solutions
    • AN Neural Network
      • Network Services
      • Network Scaling
      • Network Clusters
    • AN AI Powerhouse
      • Interactions
      • Network Embedding
      • AI Modular Infrastructure
      • Dimensional Analysis
      • Blau Spaces
      • DeepNet
    • Use Cases
  • AN Social Authenticity Network
    • User Data Sovereignty
    • Community Validation
    • Genuine Reviews
    • Social Trust Metric (STM)
      • STM Features
    • AN ID/DID
      • ID/DID Solutions
  • AN SocAuth Applications
    • AN Mobile App
      • AN Social Feeds
      • App Services & Features
    • AN Interactive
      • Wearable & Mobility Devices
  • Tokenomics and Revenues
    • Tokenomics
      • Token Utility
      • User Rewards
      • Buyback & Burn
    • Revenues
  • User Centric Rewards System
    • Rewards System
      • ANr and ANp Points
      • Rewards Rates
      • User Contributions
  • Roadmap
    • 2023 - 2024
  • OUR TEAM
    • Team Info
  • AANN.ai Lab
    • Research
    • Development
  • Authentica Foundation
    • About Authentica
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  1. AN Social Authenticity Network

Genuine Reviews

PreviousCommunity ValidationNextSocial Trust Metric (STM)

Last updated 1 year ago

Genuine Reviews on AN network contribute to establishing a valuable resource for users, communities, advertisers and businesses seeking reliable insights.

AN Neural Networks technologies offer an accurate trust scoring and user authenticity ranking system providing a line of defense against fake reviews and manipulation. Establishing a new benchmark for trust and accountability, enhancing the reliability of peer reviewed data, while ensuring rankings are unbiased and based on provable user data.

AN tracks, evaluates and sorts reviews based on measurable peer trust reactions, stored within the network by categories and topics.

For example, if a person visits a park, restaurant, store, or watches a movie, AN Neural Networks prompts its user to share their experience within the network. Users within the network can then review the shared experience to further establish an authenticity ranking for the shared content.