Facebook, Inc.

Meta Platforms, Inc.

Halil Bayrak

Shashikant Khandelwal

Shuo Li

Mridul Malpani

Cassidy Jake Beeve-Morris

Vinay Ramesh Jain

Eric Gaudet

Jonathan Mooser

Matthew Feldman

Aishwarya Rajagopal

Vandana Mohan

Leqiang Li

Zachary Zhang

A method is disclosed which includes: receiving, from a third-party content provider, a link to a structured document on an external server, where the link is associated with a post of the online social network by the third-party content provider, and retrieving the structured document from the external server, where the structured document includes content and input fields. This step is followed by extracting a predefined set of information from the structured document about the content and input fields of the structured document. The extracted set of information from the structured document are then evaluated to determine one or more feature-values of the structured document. A machine learning model is used to classify the structured document as an external lead-generation type page based on the one or more feature-values of the structured document and a notification is sent to the third-party content provider if the structured document is classified as an external lead-generation type page.

Classifying post types on online social networks

An online system applies content policies regulating presentation of sponsored content to its users. For example, content policies may prevent the presentation of sponsored content items in certain positions content feeds. The online system may relax a content policy when generating a content feed for a user based on characteristics of a user. For example, the online system generates a model determining a tolerance of the user for sponsored content, and relaxes one or more content policies if the tolerance of the user for sponsored content equals or exceeds a threshold. As another example, the online system determines whether to relax one or more content policies based on a comparison of a historical amount of compensation received from the user and an expected amount of compensation from presenting content items violating a content policy.

Relaxing policy rules for regulating the presentation of sponsored content to a user of an online system based on characteristics of the user

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San Jose, CA, United States of America

Halil Bayrak