Snap Inc.

Neil Shah

Hamed Nilforoshan-Dardashti

Leonardo Ribas Machado Das Neves

Vítor Silva Sousa

Nils Murrugarra-Llerena

Yu-Hsin Chen

Mingyi Zhao

A messaging system performs engagement analysis based on labels associated with content items produced by users of the messaging system. The messaging system is configured to process content items comprising images to identify elements in the images and determine labels for the images based on conditions indicating when to associate a label of the labels with an image of the images based on the elements in the image. The messaging system is further configured to associate the label with the content item, in response to determining to associate the label with the image, associating the label with the content item. The messaging system is further configured to determine engagement scores for the label based on interactions of users with the content items associated with label and adjust the engagement scores to determine trends in the labels to generate adjusted engagement scores.

Messaging system for engagement analysis based on labels

Systems, devices, media and methods are presented for detecting anomalous resources and events in social data. The systems and methods receive a plurality of events associated with a plurality of resources, wherein the plurality of events includes a plurality of features. The systems and methods detect a set of anomalous resources from the plurality of resources and identify a set of anomalous events associated with the set of anomalous resources. The systems and methods cause an interface to be displayed on a computing device, wherein the interface includes the set of anomalous resources and the set of anomalous events.

Detecting anomalous resources and events in social data using a trained anomaly detector

Systems, devices, media, and methods are presented for determining a level of abusive network behavior suspicion for groups of entities and for identifying suspicious entity groups. A suspiciousness metric is developed and used to evaluate a multi-view graph across multiple views where entities are associated with nodes of the graph and attributes of the entities are associated with levels of the graph.

Suspicious group detection

Systems and methods are disclosed for automatically predicting a risk score of a user login attempt by receiving a user login attempt and generating a login feature vector associated with the user login attempt. The systems and methods further train a machine learning technique to establish a relationship between the login feature vector and the risk score. The trained machine learning technique is applied to new user login attempts to predict a risk score associated with the login attempt and issue an authentication challenge to the user if the risk score exceeds a predetermined threshold value.

Growing community of inventors

Los Angeles, CA, United States of America

Neil Shah