Spam prevention using vector-based text similarity comparisons across multiple time windows

Abstract

Aspects of the present disclosure relate to detecting spam messages. Embodiments include creating condensed vector representations of messages; calculating similarity scores for each message relative to other messages using the vector representations; associating messages of the plurality of messages with a grouping based on the calculated similarity score for the messages within the grouping exceeding a threshold; determining that a grouping of messages are spam messages by comparing an amount of messages of the grouping of messages sent within a first time window to an amount of messages of the grouping of messages sent within a second time window, wherein the second time window comprises a time period preceding the first time window; providing the identified spam message to a machine learning model; and training the machine learning model by iteratively adjusting parameters of the model based on tracking the identified spam message through multiple layers of the model.