> For the complete documentation index, see [llms.txt](https://whitepaper.werate.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://whitepaper.werate.io/authentic-reviews/authenticity-framework/proof-of-authenticity-system.md). # Proof-of-Authenticity system ### 💡 A new foundation for trust Traditional review platforms assume authenticity. weRate measures it. Every contribution (a review, photo, or check-in) is evaluated through a set of authenticity signals. These signals form a **Trust Score**, a dynamic grading system that reflects how connected a review is to a real human experience. Users aren’t forced into any specific action. They simply choose how much authenticity they want to provide, and the system recognises it. Trust is not treated as binary. It is progressive, transparent, and earned. *** ### 📍 Proof of location Presence is one of the strongest authenticity signals. weRate can validate it through several optional inputs, such as: * A verified check-in at the venue * GPS confirmation * A photo with embedded location metadata * Time-based triangulation (check-in, photo, receipt) * AI-powered consistency checks to detect spoofed locations or synthetic images These signals are optional. If users provide them, the review becomes more trustworthy. If not, the review simply carries fewer trust points. *** ### 🪪 Proof of personhood Authentic reviews come from real people, not bots or farms. weRate lightly validates identity without invading privacy, using signals like: * Device and behaviour consistency * Phone number confirmation * Optional wallet-based identity linking (e.g., Solana address) This ensures reviews come from genuine, unique contributors while keeping personal data private. *** ### 🧾 Proof of purchase For venues where it matters users can optionally add: * A receipt photo * A bill snippet * A payment confirmation Receipts dramatically increase trust, but remain fully optional.\ weRate rewards authenticity; it does not require it. *** ### 🧠 Behavioural authenticity Real behaviour leaves natural patterns. weRate strengthens trust using signals like: * Frequency of visits * Review consistency * Time gaps between check-in and review * Realistic behaviour signatures These patterns help surface genuine activity while reducing synthetic or coordinated behaviour. *** ### 🗂️ Context-rich contributions Reviews carry context such as: * Date and time of visit * How often the user goes * Their discovery style * Whether they checked in This transforms a simple comment into a meaningful insight rooted in real experiences. *** ### 🧩 The Trust Score: authenticity made visible All authenticity signals feed into a transparent **Trust Score** that: * Grades authenticity * Is visible to readers * Unlocks better rewards for contributors * Elevates reliable recommendations * Reduces the impact of fake or low-effort content Users are never punished for sharing fewer signals. They simply earn more when they provide more proof. *** ### 🌟 Why this matters The internet is drowning in synthetic reviews, bots, and noise. People want to know what’s real — without becoming auditors. weRate’s authenticity framework: * Gives readers confidence * Rewards contributors for real experiences * Helps businesses access credible feedback * Creates a transparent, human-verified discovery layer weRate doesn’t force authenticity. It *reveals* it. This is the foundation for trust in the real world. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://whitepaper.werate.io/authentic-reviews/authenticity-framework/proof-of-authenticity-system.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.