> For the complete documentation index, see [llms.txt](https://docs.fusion.vectra.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.fusion.vectra.ai/detection-models/library/operational-governance/social_tinder_detection.md). # social\_tinder\_detection Explanation The social\_tinder\_detection NDM is designed to detect usage of the social media app Tinder on network endpoints. What to Look For If the social\_tinder\_detection NDM is triggered, customers **Explanation** The social\_tinder\_detection NDM is designed to detect usage of the social media app Tinder on network endpoints. **What to Look For** If the social\_tinder\_detection NDM is triggered, customers should examine their networks and endpoints for signs of Tinder usage. This might include communicating with questionable individuals on the app or using the app during business hours on work devices. To remediate the problem, customers should take steps to limit or prohibit the use of social media apps like Tinder on their networks and endpoints. Tinder is a popular location-based dating app that allows users to find and connect with potential romantic partners within a specific geographic area. Launched in 2012, Tinder has gained widespread popularity due to its simple and engaging user interface, which focuses on the concept of swiping through user profiles to express interest or disinterest. Here's how Tinder is used: 1. Profile creation: To use Tinder, users must create a profile by signing up with their Facebook account, Google account, or phone number. They can then add photos, a brief bio, and other information, such as age, gender, and sexual orientation. 2. Location-based matching: Tinder uses the user's location to find potential matches within a specified distance range. Users can adjust the search radius and other preferences, such as the age range and gender of potential matches. 3. Swiping and matching: Tinder presents users with a series of profiles based on their search preferences. Users can swipe right on a profile to express interest ("like") or swipe left to dismiss it ("dislike"). If two users both swipe right on each other's profiles, they become a "match" and can then start messaging each other within the app. 4. Messaging and communication: Once matched, users can exchange text messages, photos, and even voice or video calls within the app. The conversation can continue on Tinder or move to other platforms, depending on the users' preferences. 5. Meet-ups and dating: Tinder is primarily used as a platform for users to find potential romantic partners and arrange in-person dates or casual meet-ups. The app facilitates initial connections, but the success of any relationship that may develop depends on the users' interactions and chemistry. Tinder's popularity can be attributed to its simple and gamified approach to online dating, as well as its focus on mobile devices and location-based matching. However, users should be cautious when using Tinder or any other dating app, as it can also be a platform for scams, catfishing, or other malicious activities. It is essential to verify the identity of potential matches and prioritize personal safety when meeting someone in person for the first time. --- # 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 by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.fusion.vectra.ai/detection-models/library/operational-governance/social_tinder_detection.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.