Facebook, Inc.

Meta Platforms, Inc.

Yang Mu

Daniel Olmedilla De La Calle

Emanuel Alexandre Strauss

Enming Luo

Taiyuan Zhang

Giridhar Rajaram

For various content campaigns (or content), an online system generates a score indicating a likelihood of the content item having deceptive information, such as including a picture or name of a celebrity to promote something that the celebrity has not actually endorsed. The online system receives a request to determine whether a content item comprises deceptive information. The online system extracts features from the content item, and provides the extracted features to a machine learning based model configured to generate score indicating whether a content item comprises deceptive information. The online system executes the machine learning based model to generate the score for the content item. Responsive to the generated score indicating that content item comprises deceptive information, the online system verifies whether the content item conforms to content policies.

Machine-learning based detection of policy-violating information in content

A content review system for an online system automatically determines if received content items to be displayed to users violate any policies of the online system. The content review system generates a semantic vector representing the semantic features of a content item, for example, using a neural network. By comparing the semantic vector for the content item with semantic vectors of content items previously determined to violate one or more policies, the content review system determines whether the content item also violates one or more policies. The content review system may also maintain templates corresponding to portions of semantic vectors shared by multiple content items. An analysis of historical content items that conform to the template is performed to determine a probability that received content items that conform to the template violate a policy.

Detecting content items in violation of an online system policy using semantic vectors

Detecting content items in violation of an online system policy using templates based on semantic vectors representing content items

For various content campaigns (or content), an online system predicts a likelihood score of context violations (e.g., account term violations) of a content campaign. The online system derives a plurality of feature vectors of the content campaign. The online system predicts a likelihood score of context violation of the content campaign using a memorization model based on the plurality of feature vectors. The memorization model comprises a plurality of categories and a plurality of items of each category. Each of the plurality of categories has a category weight, and each of the plurality of items of each category has an item weight. The predicted likelihood score is based on a combination of a plurality of category weights and a plurality of item weights associated with the plurality of feature vectors. The online system performs an action affecting the content campaign based in part on the predicted likelihood score.

Memorization model for context violations

An online system distributes content items provided by content providers. The online system determines a likelihood of a content item having deceptive information. The online system stores images showing faces of people in an image database. The online system extracts features from the content item, and provides the extracted features to a machine learning based model configured to generate score indicating whether a content item comprises deceptive information. The machine learning based model uses at least a feature based on matching of faces of users shown in the content item with faces of users shown in the images of the image database. If the online system determines that a content item is deceptive, the online system adds images comprising faces extracted from the content item to the image database to grow the image database.

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Fremont, CA, United States of America

Yang Mu