Ethics-based multi-modal user post monitoring

One or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed, cause an electronic device to perform operations, the operations comprising:

• receiving a set of user posts and metadata associated with the received set of user posts;
• fitting a parameterized logistic function on the metadata associated with the received set of user posts;
• clustering each of the received set of user posts associated with corresponding metadata, based on the fitted parameterized logistic function;
• determining a category-prior associated with each of the received set of user posts, based on the clustered set of user posts;
• determining a set of multi-modal features associated with each of the received set of user posts;
• determining a feature likelihood for each of the received set of user posts, based on the determined set of multi-modal features;
• determining a final category associated with each of the received set of user posts, based on the determined category-prior and on the determined feature likelihood;
• and
• rendering post categorization information based on the determined final category associated with each of the received set of user posts,
• the received set of user posts is filtered based on the determined final category.

An electronic device, comprising:

• a memory storing instructions;
• and
• a processor, coupled to the memory, that executes the stored instructions to perform a process comprising:
• receiving a set of user posts and metadata associated with the received set of user posts;
• fitting a parameterized logistic function on the metadata associated with the received set of user posts;
• clustering each of the received set of user posts associated with corresponding metadata, based on the fitted parameterized logistic function;
• determining a category-prior associated with each of the received set of user posts, based on the clustered set of user posts;
• determining a set of multi-modal features associated with each of the received set of user posts;
• determining a feature likelihood for each of the received set of user posts, based on the determined set of multi-modal features;
• determining a final category associated with each of the received set of user posts, based on the determined category-prior and on the determined feature likelihood;
• and
• rendering post categorization information based on the determined final category associated with each of the received set of user posts,
• the received set of user posts is filtered based on the determined final category.