Abstract: In various example embodiments, a system and method for generating a filtering spam content using machine learning are presented. One or more electronic content is received. The one or more electronic content is labeled as spam or not spam by a current spam filtering system. An associated accuracy score for each of the one or more labeled content is calculated. Potential errors in the one or more labeled content is identified based on the label of the one or more labeled content being inconsistent with information associated with the source of the one or more labeled content. The one or more labeled content with identified potential errors is sent for assessment. The one or more electronic content labeled as spam with an associated accuracy score within a predetermined range is filtered, excluding labeled content with identified potential errors.