Facebook, Inc.

Tobias Henry Wooldridge

Aleksey Sergeyevich Fadeev

Christine Jinha Hwang

Chaochao Cai

Luxi Guo

Lian He

Amlesh Jayakumar

Minghao Wang

Michael Scott Sulak

An online system receives information describing events corresponding to actions associated with a third party system performed by an individual. The received information describes event types and times at which the events occurred. The online system generates nodes of a directed graph associated with the third party system, in which each node corresponds to an event type. For each event, a node count associated with a node corresponding to the event's type is incremented by the online system. Pairs of consecutively occurring events are identified based on times at which the events occurred and an edge describing each transition from one event to another is generated by the online system. The online system determines an edge count for each transition indicating a number of edges describing the transition as well as a sequential order of event types based on one or more node counts and one or more edge counts.

Determining a sequential order of types of events based on user actions associated with a third party system

An online system determines the likelihood of an interaction between a user and a content item being an invalid interaction. The online system receives an indication of an interaction of a client device with a content item. The online system identifies a device ID for the client device and determines whether the device ID is associated with one or more browser IDs. If the device ID is not associated with any browser ID, the received interaction is likely an invalid interaction. The online system may further determine the likelihood of an online publisher manufacturing interactions. The online system determines a number of invalid interactions and a number of valid interactions associated with the online publisher. The online system determines a ratio between the number of invalid and valid interactions. If the ratio is larger than a threshold value, the online system determines that the online publisher is likely manufacturing interactions.

Browsing identity

An online system receives tracking requests from client devices interacting with a website. The online system analyzes user interactions with websites using the tracking requests. The online system identifies missing parameters in the tracking requests and predicts values of the missing parameters. The online system may also identify parameters that are populated incorrectly and predicts their correct values. The online system uses the predicted parameter values for generating reports describing user interactions with the website. The online system predicts the values of the missing parameters based on metadata extracted from previous tracking requests received from client devices and also via web crawling of websites. The online system generates accurate reports based on the predicted parameters values.

Analyzing tracking requests generated by client devices based on metadata describing web page of a third party website

An online system determines the likelihood of an interaction between a user and a content item being an invalid interaction. The online system receives an indication of an interaction of a client device with a content item. The online system identifies a device ID for the client device and determines whether the device ID is associated with one or more browser IDs. If the device ID is not associated with any browser ID, the received interaction is likely an invalid interaction. The online system may further determines the likelihood of an online publisher manufacturing interactions. The online system determines a number of invalid interactions and a number of valid interactions associated with the online publisher. The online system determines a ratio between the number of invalid and valid interactions. If the ratio is larger than a threshold value, the online system determines that the online publisher is likely manufacturing interactions.

Disclosed is a method for identifying an action performed by a user in a third party system. Information associated with a form is received by an online system. For instance, hashed values of a plurality of form fields provided by a user and a description of the plurality of form fields are received by an online system. A form is identified based on the received information. Additionally, a determination whether one or more of the received hashed values correspond to stored values by the online system is made. If the received hashed values correspond to stored values in the online system, a user of the online system is identified based on the stored values corresponding to the one or more received hashed values. An identification of an action associated with the identified form and performed by the user in the third party system is stored.

User activity tracking in third-party online systems

An online system receives tracking requests from client devices interacting with a website. The online system analyzes user interactions with websites using the tracking requests. The online system predicts an accurate label for the web page that caused the tracking request to be generated. The online system uses the accurate label for generating reports describing user interactions with the website. The online system predicts the label of a web page received by the client device based on metadata extracted from markup language documents by the client device and provided to the online system via tracking requests. Examples of metadata extracted from markup language documents include labels and description of widgets in the web page that triggered the tracking request from the client device. The online system generates reports describing the quality of the tracking requests.

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Seattle, WA, United States of America

Tobias Henry Wooldridge