Facebook, Inc.

Meta Platforms, Inc.

Christopher Alexander Stein

Nickolay Vladimirov Tchervenski

Gil Shklarski

Bhalchandra Suresh Agashe

Lev Timourovich Popov

Zhimin Chen

Evan Ashton Stratford

Karan Mangla

Erdong Chen

Yigal Dan Rubinstein

Travis Bright

Some embodiments include tracking events and classifying assets within a computer system. A time series of occurrences of an event type associated with at least one asset is generated. A first signal value and a second signal value is determined based on the time series. The at least one asset can be classified based on comparison of the first signal value and the second signal value. The time series can be based on at least one time window including time intervals. Counters to determine a number of occurrences of an event type can be associated with the time intervals. Each of the counters can be incremented upon occurrence of the event type associated with the at least one asset during an associated time interval.

Systems and methods for event tracking using time-windowed counters

To determine and quantify the reputations of users within a social network. In an embodiment, a social networking system determines at least one category associated with a first account of a social networking system. The social networking system determines a first reputation score for the first account in the at least one category. The category may represent subject matter in which a user of the first account has an interest. The first reputation score may be based on content posted by the first account. The first reputation score may be adjusted based on an activity and the scores of others.

Systems and methods for managing user reputations in social networking systems

A system differentiates good content from bad content in a user-provided content system. Messages are analyzed for features that characterize messages. A feature may occur in one or more messages. A feature that has more than a threshold number of occurrences in messages in a time interval is identified for further analysis. Enhanced authentication is requested from senders of the messages with occurrences of the identified feature. Based on the rate at which senders of the messages pass authentication, the content associated with the message is determined to be good content or bad content. Subsequent messages are blocked or successfully delivered based on whether features occurring in the messages are indicative of good content or bad content.

Differentiating between good and bad content in a user-provided content system

Behavior of an online system is modified dynamically using feature expressions and feature loops. A feature expression can be expressed as a combination of other features or feature expressions, thereby allowing specification of complex features. The sets of feature expressions and policies of an online system can be modified while the online system is running. Feature loops aggregate values of a feature expression across a plurality of actions, for example, number of occurrences of an event over a time interval. The online system evaluates a set of feature expressions in response to actions performed by users. Feature expressions are used to specify policies that determine how the online system reacts to certain types of user actions. The ability to dynamically modify the feature expressions and policies of the online system allows the online system to adapt to attacks by malicious users in a timely manner.

Updating system behavior dynamically using feature expressions and feature loops

To allow for tracking events and classifying assets within a social networking system. A time series of occurrences of an event type associated with at least one asset is generated. A first signal value and a second signal value is determined based on the time series. The at least one asset is classified based on comparison of the first signal value and the second signal value. In an embodiment, the time series is based on at least one time window including time intervals. In an embodiment, counters to determine a number of occurrences of an event type are associated with the time intervals. In an embodiment, each of the counters are incremented upon occurrence of the event type associated with the at least one asset during an associated time interval.

A mechanism for efficiently determining similarity between documents. A set of reference data items is generated by processing a reference document. A similarity index representing similarity between a candidate document and the reference documents is obtained by counting segments of the candidate document matching the reference data items. The candidate document is a message transmitted in a communication system where the message is compared against one or more reference documents representing unwanted messages to filter and block unwanted messages from being transmittal or propagated.

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Los Angeles, CA, United States of America

Christopher Alexander Stein