Abstract: Systems, methods, and non-transitory computer readable media can generate one or more first machine learning models, where each of the one or more first machine learning models is associated with a respective portion of a first period of time. A second machine learning model incorporating the one or more first machine learning models as features can be generated, where the second machine learning model is associated with a second period of time. A respective weight associated with each of the one or more first machine learning models can be determined. It can be determined whether a content item is associated with a category based on the second machine learning model.

Abstract: Systems, methods, and non-transitory computer-readable media can generate a node graph comprising a plurality of user account nodes and a plurality of edge nodes connecting the plurality of user account nodes. A distance score is calculated for each user account node of the plurality of user account nodes. It is determined that a transaction is an illegitimate transaction based on the distance scores.

Abstract: Systems, methods, and non-transitory computer-readable media can generate a node graph comprising a plurality of user account nodes and a plurality of edge nodes connecting the plurality of user account nodes. A distance score is calculated for each user account node of the plurality of user account nodes. It is determined that a transaction is an illegitimate transaction based on the distance scores.

Abstract: Systems, methods, and non-transitory computer-readable media can generate a node graph comprising a plurality of user account nodes and a plurality of edge nodes connecting the plurality of user account nodes. A distance score is calculated for each user account node of the plurality of user account nodes. It is determined that a transaction is an illegitimate transaction based on the distance scores.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire a plurality of accounts associated with a set of features. Each account in the plurality of accounts can be associated with a respective set of feature values for the set of features. A selection for a subset of features out of the set of features can be received. A group of clusters can be generated based on the selection for the subset of features. Each cluster in the group of clusters can include a respective collection of nodes representing at least some of the plurality of accounts. It can be determined whether a particular collection of nodes, included in at least one cluster out of the group of clusters, represents illegitimate accounts or legitimate accounts.

Abstract: Systems, methods, and non-transitory computer readable media can generate one or more first machine learning models, where each of the one or more first machine learning models is associated with a respective portion of a first period of time. A second machine learning model incorporating the one or more first machine learning models as features can be generated, where the second machine learning model is associated with a second period of time. A respective weight associated with each of the one or more first machine learning models can be determined. It can be determined whether a content item is associated with a category based on the second machine learning model.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire historical data including a plurality of features associated with known legitimate activities and with known illegitimate activities. A machine learning technique can be applied to the historical data to gain information about the plurality of features associated with the known legitimate activities and with the known illegitimate activities. A decision tree can be generated based on at least a portion of the information about the plurality of features. A node in the decision tree that satisfies specified precision criteria can be identified. A rule can be created based on the node. One or more illegitimate activities can be identified based on the rule.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire information associated with a set of rules and a set of activities identified by the set of rules as being potentially illegitimate. A priority order for a set of properties associated with the set of rules can be acquired. The set of rules can be ranked based on the priority order. A highest ranked rule can be stored into a record. The highest ranked rule can be removed from consideration out of the set of rules. At least one activity, identified by the highest ranked rule, can be removed from consideration out of the set of activities. In some instances, the record can be provided.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire a plurality of accounts associated with a set of features. Each account in the plurality of accounts can be associated with a respective set of feature values for the set of features. A selection for a subset of features out of the set of features can be received. A group of clusters can be generated based on the selection for the subset of features. Each cluster in the group of clusters can include a respective collection of nodes representing at least some of the plurality of accounts. It can be determined whether a particular collection of nodes, included in at least one cluster out of the group of clusters, represents illegitimate accounts or legitimate accounts.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire historical data including a plurality of features associated with known legitimate activities and with known illegitimate activities. A machine learning technique can be applied to the historical data to gain information about the plurality of features associated with the known legitimate activities and with the known illegitimate activities. A decision tree can be generated based on at least a portion of the information about the plurality of features. A node in the decision tree that satisfies specified precision criteria can be identified. A rule can be created based on the node. One or more illegitimate activities can be identified based on the rule.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire historical data including a plurality of features associated with known legitimate activities and with known illegitimate activities. A machine learning technique can be applied to the historical data to gain information about the plurality of features associated with the known legitimate activities and with the known illegitimate activities. A decision tree can be generated based on at least a portion of the information about the plurality of features. A node in the decision tree that satisfies specified precision criteria can be identified. A rule can be created based on the node. One or more illegitimate activities can be identified based on the rule.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire information associated with a set of rules and a set of activities identified by the set of rules as being potentially illegitimate. A priority order for a set of properties associated with the set of rules can be acquired. The set of rules can be ranked based on the priority order. A highest ranked rule can be stored into a record. The highest ranked rule can be removed from consideration out of the set of rules. At least one activity, identified by the highest ranked rule, can be removed from consideration out of the set of activities. In some instances, the record can be provided.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire historical data including a plurality of features associated with known legitimate activities and with known illegitimate activities. A machine learning technique can be applied to the historical data to gain information about the plurality of features associated with the known legitimate activities and with the known illegitimate activities. A decision tree can be generated based on at least a portion of the information about the plurality of features. A node in the decision tree that satisfies specified precision criteria can be identified. A rule can be created based on the node. One or more illegitimate activities can be identified based on the rule.