Facebook, Inc.

Matthew Kai-Shing Choi

Yigal Dan Rubinstein

Aditya Koolwal

Joshua Wiseman

Eric Faller

Sumeet Vaidya

Josh Wiseman

A social networking system detects spam in comments posted by users of the social networking system. A spam prevention module of the social networking system retrieves content signals associated with a posted comment and analyzes the content signals to determine whether the posted comment contains spam content. The spam prevention module augments the content signal analysis by analyzing social signals of the posted comment. Based on the content signal analysis and the social signal analysis, the spam prevention module determines whether the posted comment is spam and takes remedial actions on detected spam, including blocking the comment and educating the user who posted the comment.

Spam detection and prevention in a social networking system

When a social networking system receives a request from a requesting user for a content item associated with one or more comments, the social networking system determines an interest score for each comment. The interest score for a comment indicates a measure of the user's likelihood of being interested in the comment. Based on the calculated interest scores, the social networking system selects one or more comments for presentation to the viewing user along with the content item. The social networking system may specify an order in which the selected comments are presented based on the interest scores of the selected comments.

Selection and ranking of comments for presentation to social networking system users

A social networking system determines whether a user is a real public figure or is the correct 'public persona' of an individual who is famous within a particular domain. The system selects one or more trusted agents. A trusted agent is a user who has been confirmed or is otherwise trusted within the system. The system traverses the social graph of the trusted agent to score and identify additional users who have a sufficient connection to the trusted agent to authenticate them as other confirmed public personas/figures. The system computes confidence scores of the connections of the trusted agent, considering the strength of the connections and other relevant factors. The system provides confidence scores for one or more target users in the social network based on the confidence scores of the friends or connections connecting the trusted agent and the target user.

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Menlo Park, CA, United States of America

Matthew Kai-Shing Choi