Abstract: An online system determines a frequency with which its users delete information stored in a browser on client devices associated with each user. When a user accesses the online system, the online system determines a user identifier associated with the user and determines if one or more conditions are satisfied based on information received from a browser used to access the online system. If a condition is satisfied, the online system communicates an instruction to the browser to communicate information associated with a third party and the user identifier to the third party. Information previously stored in the browser and associated with the user identifier is compared to information associated with the user identifier received from the browser. Deletion of information stored in the browser is determined when stored information associated with the user identifier differs from received information associated with the user identifier.

Abstract: An online system determines a frequency with which its users delete information stored in a browser on client devices associated with each user. When a user accesses the online system, the online system determines a user identifier associated with the user and determines if one or more conditions are satisfied based on information received from a browser used to access the online system. If a condition is satisfied, the online system communicates an instruction to the browser to communicate information associated with a third party and the user identifier to the third party. Information previously stored in the browser and associated with the user identifier is compared to information associated with the user identifier received from the browser. Deletion of information stored in the browser is determined when stored information associated with the user identifier differs from received information associated with the user identifier.

Abstract: Machine learning models are used for ranking news feed stories presented to users of a social networking system. The social networking system divides its users into different sets, for example, based on demographic characteristics of the users and generates one model for each set of users. The models are periodically retrained. The news feed ranking model may rank news feeds for a user based on information describing other users connected to the user in the social networking system. Information describing other users connected to the user includes interactions of the other users with objects associated with news feed stories. These interactions include commenting on a news feed story, liking a news feed story, or retrieving information, for example, images, videos associated with a news feed story.

Abstract: An online system determines a frequency with which its users delete information stored in a browser on client devices associated with each user. When a user accesses the online system, the online system determines a user identifier associated with the user and determines if one or more conditions are satisfied based on information received from a browser used to access the online system. If a condition is satisfied, the online system communicates an instruction to the browser to communicate information associated with a third party and the user identifier to the third party. Information previously stored in the browser and associated with the user identifier is compared to information associated with the user identifier received from the browser. Deletion of information stored in the browser is determined when stored information associated with the user identifier differs from received information associated with the user identifier.

Abstract: Machine learning models are used for ranking news feed stories presented to users of a social networking system. The social networking system divides its users into different sets, for example, based on demographic characteristics of the users and generates one model for each set of users. The models are periodically retrained. The news feed ranking model may rank news feeds for a user based on information describing other users connected to the user in the social networking system. Information describing other users connected to the user includes interactions of the other users with objects associated with news feed stories. These interactions include commenting on a news feed story, liking a news feed story, or retrieving information, for example, images, videos associated with a news feed story.

Abstract: Machine learning models are used for ranking news feed stories presented to users of a social networking system. The social networking system divides its users into different sets, for example, based on demographic characteristics of the users and generates one model for each set of users. The models are periodically retrained. The news feed ranking model may rank news feeds for a user based on information describing other users connected to the user in the social networking system. Information describing other users connected to the user includes interactions of the other users with objects associated with news feed stories. These interactions include commenting on a news feed story, liking a news feed story, or retrieving information, for example, images, videos associated with a news feed story.

Abstract: Machine learning models are used for ranking news feed stories presented to users of a social networking system. The social networking system divides its users into different sets, for example, based on demographic characteristics of the users and generates one model for each set of users. The models are periodically retrained. The news feed ranking model may rank news feeds for a user based on information describing other users connected to the user in the social networking system. Information describing other users connected to the user includes interactions of the other users with objects associated with news feed stories. These interactions include commenting on a news feed story, liking a news feed story, or retrieving information, for example, images, videos associated with a news feed story.

Abstract: A method and system for retrieving data from a webpage is described herein. A scheduler organizes, or rather orders, a group of webpage identifiers according to some predetermined criteria. Based upon this ordering, a fetcher may be configured to fetch data from webpages identified by the identifiers. To promote efficiency and reduce the latency between when a webpage is updated and when the fetcher retrieves data from the webpage, the scheduler may be configured to reorder the identifiers in such a manner that it causes an identifier that was less relevant, and would not have been sent to the fetcher, to become more relevant. In this way, the method and system may be particularly useful for retrieving data related to webpages that are updated frequently, such as social media webpages, for example.

Abstract: The behavior of browser applications, such as web browsers, can be controlled in part by script-based instructions present within documents read by those browsers. To analyze such scripts in an efficient manner, a script analyzer can identify the scripts in the document, divide them into script modules, and order the modules to represent an interpretational flow. The script can be interpreted and executed on a line-by-line basis and its behavior analyzed. Prior to interpretation, the scripts can be reviewed for delay conditionals, and such statements can be modified for more efficient interpretation. Additionally, if, during interpretation, the script generates new script, or modifies existing script, such new scripts can be themselves interpreted. External function calls made by the script can be intercepted and responded to in a generic fashion, limiting the need to create a document object model, based on the document's data, solely for script analysis purposes.

Abstract: Machine learning models are used for ranking news feed stories presented to users of a social networking system. The social networking system divides its users into different sets, for example, based on demographic characteristics of the users and generates one model for each set of users. The models are periodically retrained. The news feed ranking model may rank news feeds for a user based on information describing other users connected to the user in the social networking system. Information describing other users connected to the user includes interactions of the other users with objects associated with news feed stories. These interactions include commenting on a news feed story, liking a news feed story, or retrieving information, for example, images, videos associated with a news feed story.

Abstract: Machine learning models are used for ranking news feed stories presented to users of a social networking system. The social networking system divides its users into different sets, for example, based on demographic characteristics of the users and generates one model for each set of users. The models are periodically retrained. The news feed ranking model may rank news feeds for a user based on information describing other users connected to the user in the social networking system. Information describing other users connected to the user includes interactions of the other users with objects associated with news feed stories. These interactions include commenting on a news feed story, liking a news feed story, or retrieving information, for example, images, videos associated with a news feed story.

Abstract: A method and system for retrieving data from a webpage is described herein. A scheduler organizes, or rather orders, a group of webpage identifiers according to some predetermined criteria. Based upon this ordering, a fetcher may be configured to fetch data from webpages identified by the identifiers. To promote efficiency and reduce the latency between when a webpage is updated and when the fetcher retrieves data from the webpage, the scheduler may be configured to reorder the identifiers in such a manner that it causes an identifier that was less relevant, and would not have been sent to the fetcher, to become more relevant. In this way, the method and system may be particularly useful for retrieving data related to webpages that are updated frequently, such as social media webpages, for example.

Abstract: The behavior of browser applications, such as web browsers, can be controlled in part by script-based instructions present within documents read by those browsers. To analyze such scripts in an efficient manner, a script analyzer can identify the scripts in the document, divide them into script modules, and order the modules to represent an interpretational flow. The script can be interpreted and executed on a line-by-line basis and its behavior analyzed. Prior to interpretation, the scripts can be reviewed for delay conditionals, and such statements can be modified for more efficient interpretation. Additionally, if, during interpretation, the script generates new script, or modifies existing script, such new scripts can be themselves interpreted. External function calls made by the script can be intercepted and responded to in a generic fashion, limiting the need to create a document object model, based on the document's data, solely for script analysis purposes.