Spam is identified by computing sender reputation derived from historical activity data across counts for various categories. A spam filter or machine learning system can be trained utilizing pre-categorized data in conjunction with activity data associated with a sender aggregated across at least one time period. This sender activity filter can be employed alone or in combination with other filters to facilitate classification of messages as spam or non-spam.