Abstract: A social networking system includes pages created by users for providing content related to topics of interest. An interaction engine captures data and maintains analytical information about how users interact with pages and posts. When a user takes an action such as liking a page or a post, the interaction engine updates the page's activity history to reflect the activity and information about the user who took the action. The interaction engine also measures the reach of pages and posts, by determining how and from where each user viewed the page and its contents. The interaction engine further tracks the community's interest in pages and posts by determining how many unique users create stories that include a page or its content. An insights module uses the data obtained by the interaction engine to synthesize graphical reports about page interactions and present the reports to page administrators.

Abstract: A social networking system infers a sentiment polarity of a user toward content of a page. The sentiment polarity of the user is inferred based on received information about an interaction between the user and the page (e.g., like, report, etc.), and may be based on analysis of a topic extracted from text on the page. The system infers a positive or negative sentiment polarity of the user toward the content of the page, and that sentiment polarity then may be associated with any second or subsequent interaction from the user related to the page content. The system may identify a set of trusted users with strong sentiment polarities toward the content of a page or topic, and may use the trusted user data as training data for a machine learning model, which can be used to more accurately infer sentiment polarity of users as new data is received.

Abstract: A social networking system infers a sentiment polarity of a user toward content of a page. The sentiment polarity of the user is inferred based on received information about an interaction between the user and the page (e.g., like, report, etc.), and may be based on analysis of a topic extracted from text on the page. The system infers a positive or negative sentiment polarity of the user toward the content of the page, and that sentiment polarity then may be associated with any second or subsequent interaction from the user related to the page content. The system may identify a set of trusted users with strong sentiment polarities toward the content of a page or topic, and may use the trusted user data as training data for a machine learning model, which can be used to more accurately infer sentiment polarity of users as new data is received.

Abstract: A social networking system infers a sentiment polarity of a user toward content of a page. The sentiment polarity of the user is inferred based on received information about an interaction between the user and the page (e.g., like, report, etc.), and may be based on analysis of a topic extracted from text on the page. The system infers a positive or negative sentiment polarity of the user toward the content of the page, and that sentiment polarity then may be associated with any second or subsequent interaction from the user related to the page content. The system may identify a set of trusted users with strong sentiment polarities toward the content of a page or topic, and may use the trusted user data as training data for a machine learning model, which can be used to more accurately infer sentiment polarity of users as new data is received.

Abstract: An online system maintains information describing interactions by its users with various applications. To allow evaluation of an application against other applications, the online system identifies additional applications having a threshold measure of similarity to the application and with which at least at threshold number of users interacted during a time interval. Based on a number of users who interacted with various additional applications and amounts of revenue obtained by additional applications, the online system selects a group of additional applications. The online system selects additional applications from the group based on scores for the additional applications determined from user interaction and revenue obtained by the additional applications and provides information about the additional applications selected from the group to an entity associated with the application.

Abstract: A social networking system includes pages created by users for providing content related to topics of interest. An interaction engine captures data and maintains analytical information about how users interact with pages and posts. When a user takes an action such as liking a page or a post, the interaction engine updates the page's activity history to reflect the activity and information about the user who took the action. The interaction engine also measures the reach of pages and posts, by determining how and from where each user viewed the page and its contents. The interaction engine further tracks the community's interest in pages and posts by determining how many unique users create stories that include a page or its content. An insights module uses the data obtained by the interaction engine to synthesize graphical reports about page interactions and present the reports to page administrators.

Abstract: A social networking system infers a sentiment polarity of a user toward content of a page. The sentiment polarity of the user is inferred based on received information about an interaction between the user and the page (e.g., like, report, etc.), and may be based on analysis of a topic extracted from text on the page. The system infers a positive or negative sentiment polarity of the user toward the content of the page, and that sentiment polarity then may be associated with any second or subsequent interaction from the user related to the page content. The system may identify a set of trusted users with strong sentiment polarities toward the content of a page or topic, and may use the trusted user data as training data for a machine learning model, which can be used to more accurately infer sentiment polarity of users as new data is received.

Abstract: An online system maintains information describing interactions by its users with various applications. To allow evaluation of an application against other applications, the online system identifies additional applications having a threshold measure of similarity to the application and with which at least at threshold number of users interacted during a time interval. Based on a number of users who interacted with various additional applications and amounts of revenue obtained by additional applications, the online system selects a group of additional applications. The online system selects additional applications from the group based on scores for the additional applications determined from user interaction and revenue obtained by the additional applications and provides information about the additional applications selected from the group to an entity associated with the application.

Abstract: A social networking system receives a selection of user characteristics defining a benchmark audience and a target audience, and generates audience metrics that compare the audiences across a set of user characteristics. These user characteristics include demographics, interests, purchasing activity, and actions on the social networking system. The audience metrics are provided to an advertiser who may select additional user characteristics to refine the benchmark or target audiences. The audience metrics may include an affinity score that compares the audience metrics for a particular type of interaction, and may normalize the frequency of interactions relative to interactions of the audience as a whole. Advertisers may use the defined audiences to establish targeting criteria for an advertisement, and may use existing targeting criteria to seed the selection of an audience.

Abstract: A social networking system receives a selection of user characteristics defining a benchmark audience and a target audience, and generates audience metrics that compare the audiences across a set of user characteristics. These user characteristics include demographics, interests, purchasing activity, and actions on the social networking system. The audience metrics are provided to an advertiser who may select additional user characteristics to refine the benchmark or target audiences. The audience metrics may include an affinity score that compares the audience metrics for a particular type of interaction, and may normalize the frequency of interactions relative to interactions of the audience as a whole. Advertisers may use the defined audiences to establish targeting criteria for an advertisement, and may use existing targeting criteria to seed the selection of an audience.

Abstract: A social networking system receives a selection of user characteristics defining a benchmark audience and a target audience, and generates audience metrics that compare the audiences across a set of user characteristics. These user characteristics include demographics, interests, purchasing activity, and actions on the social networking system. The audience metrics are provided to an advertiser who may select additional user characteristics to refine the benchmark or target audiences. The audience metrics may include an affinity score that compares the audience metrics for a particular type of interaction, and may normalize the frequency of interactions relative to interactions of the audience as a whole. Advertisers may use the defined audiences to establish targeting criteria for an advertisement, and may use existing targeting criteria to seed the selection of an audience.

Abstract: A social networking system infers a sentiment polarity of a user toward content of a page. The sentiment polarity of the user is inferred based on received information about an interaction between the user and the page (e.g., like, report, etc.), and may be based on analysis of a topic extracted from text on the page. The system infers a positive or negative sentiment polarity of the user toward the content of the page, and that sentiment polarity then may be associated with any second or subsequent interaction from the user related to the page content. The system may identify a set of trusted users with strong sentiment polarities toward the content of a page or topic, and may use the trusted user data as training data for a machine learning model, which can be used to more accurately infer sentiment polarity of users as new data is received.

Abstract: A social networking system includes pages created by users for providing content related to topics of interest. An interaction engine captures data and maintains analytical information about how users interact with pages and posts. When a user takes an action such as liking a page or a post, the interaction engine updates the page's activity history to reflect the activity and information about the user who took the action. The interaction engine also measures the reach of pages and posts, by determining how and from where each user viewed the page and its contents. The interaction engine further tracks the community's interest in pages and posts by determining how many unique users create stories that include a page or its content. An insights module uses the data obtained by the interaction engine to synthesize graphical reports about page interactions and present the reports to page administrators.

Abstract: A social networking system includes pages created by users for providing content related to topics of interest. An interaction engine captures data and maintains analytical information about how users interact with pages and posts. When a user takes an action such as liking a page or a post, the interaction engine updates the page's activity history to reflect the activity and information about the user who took the action. The interaction engine also measures the reach of pages and posts, by determining how and from where each user viewed the page and its contents. The interaction engine further tracks the community's interest in pages and posts by determining how many unique users create stories that include a page or its content. An insights module uses the data obtained by the interaction engine to synthesize graphical reports about page interactions and present the reports to page administrators.

Abstract: A social networking system includes pages created by users for providing content related to topics of interest. An interaction engine captures data and maintains analytical information about how users interact with pages and posts. When a user takes an action such as liking a page or a post, the interaction engine updates the page's activity history to reflect the activity and information about the user who took the action. The interaction engine also measures the reach of pages and posts, by determining how and from where each user viewed the page and its contents. The interaction engine further tracks the community's interest in pages and posts by determining how many unique users create stories that include a page or its content. An insights module uses the data obtained by the interaction engine to synthesize graphical reports about page interactions and present the reports to page administrators.