Google Inc.

Andrew Beckmann Sellergren

Xavier Llora

Nandini Kappiah

Morgan Avery Conbere

Katherine Huang

Endorsement abuse detection via social interactions is described, including generating an endorsement log comprising an endorser identifier, a target identifier, endorsement data, and a token indicative of an endorsement event; analyzing the endorsement log with respect to a user aggregator and a target aggregator, to generate an online endorsement abuse candidate; generating an endorsement overlap graph for the online endorsement abuse candidate based on the endorsement log, the user aggregator and the target aggregator, wherein nodes of the endorsement overlap graph represent ones of the online endorsement abuse candidate, and edges represent a number of shared endorsements; and determining whether the endorsement event is authorized based an analysis of the endorsement overlap graph and an orthogonal signal at least one of transmitted and received by the ones of the online endorsement abuse candidate.

Endorsement abuse detection via social interactions

A system for protecting endorsement events against anomalous behaviors is disclosed. The system includes a data collecting module, an activity recording module, an aggregation module, a detection module and an action module. The data collecting module collects event data describing one or more endorsement events. The activity recording module records activity data associated with the one or more endorsement events. The aggregation module aggregates the activity data. The detection module detects, based at least in part on the aggregation of the activity data, one or more anomalies associated with the one or more endorsement events. The action module takes one or more protection actions responsive to the detection of the one or more anomalies. The present disclosure also includes a method for protecting endorsement events against anomalous behaviors.

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San Francisco, CA, United States of America

Andrew Beckmann Sellergren