Abstract: A system and method for generating automatic false positive estimations for website matching is described. Several sets of assets and Uniform Resource Locators (URLs) are aggregated. Each of the several sets of assets is searched across webpage content corresponding to the several URLs to determine matches between the sets of assets and webpage content. One or more false positive estimations is determined, where each of the one or more false positive estimations corresponds to the one or more matches. A combined score is generated based on the one or more false positive estimations.

Abstract: A request is received from a first user of a social interaction platform. The request is a request to acquire a status. In response to the receiving of the request, a database is accessed. The database contains first electronic data pertaining to previous interactions between the first user and other entities of the social interaction platform. The first electronic data is analyzed via one or more Natural Language Processing (NLP) techniques. A first result is obtained based on the analyzing. A machine learning process is executed based at least in part on the first result. Based on the executing of the machine learning process, a determination is made whether to grant or deny the request received from the first user.

Abstract: A system and method for generating automatic false positive estimations for website matching is described. Several sets of assets and Uniform Resource Locators (URLs) are aggregated. Each of the several sets of assets is searched across webpage content corresponding to the several URLs to determine matches between the sets of assets and webpage content. One or more false positive estimations is determined, where each of the one or more false positive estimations corresponds to the one or more matches. A combined score is generated based on the one or more false positive estimations.