Systems and methods for classifying media according to user negative propensities

A method for classifying media according to user negative propensities, the method comprising:

• obtaining, by a computing device, a physiological state data as a function of a user input;
• identifying, by the computing device, a user propensity for problematic behavior associated with a human subject as a function of the physiological state data, wherein the user propensity for problematic behavior identifies a problematic behavior from a predetermined plurality of problematic behaviors, wherein identifying the user propensity for problematic behavior further comprises:
• training a behavior machine-learning model using a feature learning algorithm and a behavioral training set, wherein the behavioral training set includes a plurality of physiological state data correlated to a plurality of problematic behavior propensities;
• and
• generating the user propensity for problematic behavior using the trained behavior machine-learning model as a function of the physiological state data;
• receiving, by the computing device, a media item to be transmitted to a device operated by the human subject;
• identifying, by the computing device, a principal theme associated with the media item, wherein identifying the principal theme further comprises:
• training a media theme machine-learning classifier using a classification algorithm and media training data, wherein the media training data includes a plurality of media items correlated with a plurality of principal themes;
• and
• generating the principal theme using the trained media theme machine-learning classifier as a function of the media element;
• and
• blocking, by the computing device, transmission of the media item to the device operated by the human subject as a function of the principal theme and the user propensity for problematic behavior.