1. A computer-implemented method, comprising: receiving data associated with an electronic communication, the data comprising text data of text content from the body of at least one of a plurality of messages and a plurality of advertisements; determining semantic meaning of the text content based at least in part on contextual usage of words in the text content; detecting, from the received data, and using an unsupervised machine learning model, at least one indicator of a potential violation condition, wherein a violation condition is associated with a human activity that is a violation of a predetermined standard, wherein the detecting of the at least one indicator is based at least in part on the determined semantic meaning and comprises performing entity resolution on the text data, across the at least one of the plurality of messages and plurality of advertisements, to resolve coreferent mentions to entities, wherein the coreferent mentions comprise a plurality of aliases corresponding to the same person and resolving the coreferent mentions comprises determining that the plurality of aliases refer to the same person, and wherein the at least one indicator is comprised of features defining behavioral patterns of one or more particular individuals, and the behavioral patterns comprise a plurality of actions by the one or more individuals; responsive to detecting the at least one indicator of the potential violation condition, marking the electronic communication as being associated with a potential violation condition; presenting the potential violation condition to a reviewing user for review, wherein the reviewing user is the first user or a second user; receiving a decision from the reviewing user, based on the review, on whether the electronic communication is associated with a violation condition; and based on the decision, adjusting at least one of feature selection, pattern selection, feature weighting, pattern weighting, and alerting thresholds, to improve the accuracy of the unsupervised machine learning model used for detecting potential violation conditions.