Facebook, Inc.

Meta Platforms, Inc.

Jiun-Ren Lin

Daniel Olmedilla De La Calle

Emanuel Alexandre Strauss

Shengbo Guo

James Li

Jiayi Wen

Sijian Tang

Chenzhang He

An online system receives a content item including a link to a landing page and determines a likelihood the landing page violates an online system policy based on a structural similarity between the landing page and a web page violating the policy. To determine the likelihood, the online system determines a hierarchical structure associated with the web page violating the policy and an additional hierarchical structure associated with the landing page. The hierarchical structure represents a structure of at least a portion of the web page and the additional hierarchical structure represents a structure of a corresponding portion of the landing page. The online system compares the hierarchical structure and additional hierarchical structure. Based on the comparison, the online system computes a measure of dissimilarity between the hierarchical structure and additional hierarchical structure and determines a likelihood the landing page violates the policy based on the measure of dissimilarity.

Detecting a landing page that violates an online system policy based on a structural similarity between the landing page and a web page violating the policy

An online system determines a quality of content provided by third party systems for distribution to users. The online system analyzes URL's posted within the online system by content providers to determine the quality of content of the webpages obtained by accessing the URLs. For each URL, the online system receives an original markup language document and a copy of the markup document obtained by applying a content filter. The online system extracts features from both markup language documents. The online system provides the extracted features to a machine learning based model to generate a content quality score. The online system categorizes the URL as having high quality content or low quality content. The online system restricts distribution of content items including URLs to websites with low quality content.

Managing distribution of content items including URLs to external websites

The disclosed computer-implemented method may include (1) sampling links from an online system, (2) receiving, from a human labeler for each of the links, a label indicating whether the human labeler considers a landing page of the link to be a low-quality webpage, (3) generating a link graph from a crawl of the links, (4) using the link graph to derive a graph-based feature for each of the links, (5) using the label and the graph-based feature of each of the links to train a model configured to predict a likelihood that a link is to a low-quality webpage, (6) identifying content items that are candidates for a content feed of a user, (7) applying the model to the content items to determine a ranking, and (8) displaying the content items in the content feed based on the ranking. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for using link graphs to demote links to low-quality webpages

An online system reviews various user profiles for compliance with policies enforced by the online system. However, users may attempt to subvert action by the online system by creating additional user profiles for presenting content. Accordingly, the online system generates a graph identifying connections user profiles, content items associated with the user profiles, and objects identified by the content items. User profiles, content items, or objects previously identified to have violated one or more policies enforced by the online system are identified via the graph. The online system computes a profile score for various user profiles based on a probability of reaching an object, user profile, or content item identified as violating a policy through a random walk in the graph. Based on the profile scores, the online system trains to identify user profiles for review against one or more enforced policies.

Identifying user profiles to evaluate against policies enforced by an online system based on connections between content items, user profiles, and objects maintained by the online system

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Cupertino, CA, United States of America

Jiun-Ren Lin