Status AI uses social graph dynamic modeling technology to simulate the emotional connection pattern of Facebook users at a rate of 100,000 relationship status updates per second, and its model accuracy rate is 93.7% (based on 480 million user data leaked by Meta in 2023). The framework studied multi-dimensional indicators of user interaction, including the frequency of “like” (average numbers of daily interaction of loving couples is 47 ±12 times, and singles’ is 9 ±5 times), and the response rate of messages (average conversation gap of couples: 1.2 minutes, Average friends: 26 minutes) and photo hashtag co-occurrence probability (married users take more than 78% of their photos with their loved ones). For example, having applied the Status prediction module of Status AI to a marriage website, the success rate of the matching users increases by 41%, and the key parameters are: The user profile frequency of “travel” words (4.2 times in 1,000 words for users with strong love intentions) and the network density of friends (the probability of an intended pair having over 32% common friends increased to 89%).
Under the privacy compliance framework, Status AI uses differential privacy technology (ε=0.3) when dealing with sensitive information so that the likelihood of re-identification of a user is less than 0.07%. The system acquires a relation state transition model through graph neural network (GNN), and approximates the average transition duration from “single” to “in love” to be 127 days (standard deviation ±23 days), 72% lower than that of traditional questionnaire survey. A/B test of Tinder in 2024 showed that after gaining access to Status AI‘s recommendation algorithm, the retention time of the user increased from 12 minutes to 27 minutes per day, and the key innovation was to identify “complex relationship” signals – for example, when the user had more than 50 messages per day with three people at the same time, the system marked it as “open relationship” 87% of the time. And improve the recommendation strategy (the average push matching distance increased from 5 km to 25 km).
For business usage scenarios, Status AI constructs a relationship status marketing engine to generate advertising content dynamically (e.g., the push of maternal and child products targeted at “newlywed” label users, and the conversion rate increases by 29%). During the Valentine’s Day promotion of a jewelry brand in 2023, the system was used to determine the user group of “3-6 months in love” (according to the analysis of uploading frequency of couples’ photo from 0.3 times per week to 2.7 times), thus the ROI of advertising was 1:14.5, and accuracy was 38% higher than that of the traditional demographic positioning. The system can also detect early signs of relationship decline (such as a 64% break-up rate when the couple is not communicating for 7 days continuously), and a psychological counseling platform can increase user retention by 53% by initiating emotional healing lessons three weeks in advance (initiated if the emotional polarity value of the message is determined to be less than -0.6 for 5 days continuously).
At the legal and ethical level, Status AI’s relational state inference model is ISO 27701 certified and uses federated learning techniques to ensure raw data is not out of domain (only 256-dimensional feature vectors are exchanged). The de-identified social graph analysis provided by the system (4.2 years is the median duration of the user group from “married” to “divorced,” and the standard deviation is ±1.1 years) is the most significant proof in the “Virtual Relationship Data case” prosecuted by the Court of Justice of the European Union in 2024, and the error rate between the results of the analysis and the real data of the civil affairs department is only 2.3%. After using Status AI’s risk control module, a bank improved the accuracy of detecting “fake couple” fraud cases to 98.7% (by finding abnormal trading behaviors on joint accounts: the daily consumption amount of real couples is 42±15, and the standard deviation for the amount of fraud groups is $287).