Abstract: Structured information about nodes may be generated and shared using sub-nodes. A node in a social networking system may be associated with sub-nodes that are definable by the node owner, such as menu items for a restaurant or songs in playlists for an artist. Users of the system may interact with the sub-nodes, and the interactions may be presented back on the page to a user, aggregated according to the user's connections in the social networking system (e.g., which songs your friends listened to the most by the artist, which menu items were consumed the most). Users may associate other sub-nodes to the node, such as identifying other menu items served by a restaurant, and the node owner may confirm these associations. Location awareness functionalities may be used to inform a user of highly recommended sub-nodes nearby as indicated by other users of the social networking system.

Abstract: A social networking system displays information about a user via a timeline included in a profile page. Narrative data about a user stored in a social networking system, including data about the user and social network activities related to the user is accessed. Timeline units summarizing information about the user are generated from the narrative data. When a viewing user requests a profile page, timeline units are displayed based information related to the viewing user and interactions with the profile page by other users connected to the viewing user. Users that administer the profile page are presented with additional information when viewing the profile page, allowing the administering users to modify appearance of the timeline or to identify actions for an administering user to take to increase interaction with the profile page.

Abstract: An online system provides a social networking environment for a set of users, such as members of an organization. The online system allows generation of groups of users within the set to allow users to more easily communicate content with other users. When a user creates a new group, the online system identifies multiple categories to the user, and the user selects a category for association with the group. Categories are associated with access rights regulating user access to content associated with the group or users capable of performing various actions with the group. Additionally, a member of the group may invite entities outside of the online system, or outside of the set of users, to join the group. If the entity joins the group, the online system provides content associated with the group to the entity, allowing the entity to receive content from the group.

Abstract: Structured information about nodes may be generated and shared using sub-nodes. A node in a social networking system may be associated with sub-nodes that are definable by the node owner, such as menu items for a restaurant or songs in playlists for an artist. Users of the system may interact with the sub-nodes, and the interactions may be presented back on the page to a user, aggregated according to the user's connections in the social networking system (e.g., which songs your friends listened to the most by the artist, which menu items were consumed the most). Users may associate other sub-nodes to the node, such as identifying other menu items served by a restaurant, and the node owner may confirm these associations. Location awareness functionalities may be used to inform a user of highly recommended sub-nodes nearby as indicated by other users of the social networking system.

Abstract: Users in public forums often mention certain topics in the course of their discussions. Member's comments in messages to other members are analyzed to obtain terms that co-occur with topics. Frequencies of co-occurrence of a term with topics are normalized based on frequency of the term in a random sample of message. The terms are ranked by their normalized frequency of co-occurrence with a topic in messages. The top terms are selected based on their rank. Analysis of demographic information associated with members that mentioned top terms associated with a topic is displayed in graphical format that highlights the relationship between the age, gender, and usage of the top terms over time. The demographic information presented includes average age of members that mentioned a top term or their gender information within a selected time interval.

Abstract: An online system provides a social networking environment for a set of users, such as members of an organization. The online system allows generation of groups of users within the set to allow users to more easily communicate content with other users. When a user creates a new group, the online system identifies multiple categories to the user, and the user selects a category for association with the group. Categories are associated with access rights regulating user access to content associated with the group or users capable of performing various actions with the group. Additionally, a member of the group may invite entities outside of the online system, or outside of the set of users, to join the group. If the entity joins the group, the online system provides content associated with the group to the entity, allowing the entity to receive content from the group.

Abstract: Users in public forums often mention certain topics in the course of their discussions. Member's comments in messages to other members are analyzed to obtain terms that co-occur with topics. Frequencies of co-occurrence of a term with topics are normalized based on frequency of the term in a random sample of message. The terms are ranked by their normalized frequency of co-occurrence with a topic in messages. The top terms are selected based on their rank. Analysis of demographic information associated with members that mentioned top terms associated with a topic is displayed in graphical format that highlights the relationship between the age, gender, and usage of the top terms over time. The demographic information presented includes average age of members that mentioned a top term or their gender information within a selected time interval.

Abstract: Users in public forums often mention certain topics in the course of their discussions. Member's comments in messages to other members are analyzed to obtain terms that co-occur with topics. Frequencies of co-occurrence of a term with topics are normalized based on frequency of the term in a random sample of message. The terms are ranked by their normalized frequency of co-occurrence with a topic in messages. The top terms are selected based on their rank. Analysis of demographic information associated with members that mentioned top terms associated with a topic is displayed in graphical format that highlights the relationship between the age, gender, and usage of the top terms over time. The demographic information presented includes average age of members that mentioned a top term or their gender information within a selected time interval.

Abstract: Users in public forums often mention certain topics in the course of their discussions. Member's comments in messages to other members are analyzed to obtain terms that co-occur with topics. Frequencies of co-occurrence of a term with topics are normalized based on frequency of the term in a random sample of message. The terms are ranked by their normalized frequency of co-occurrence with a topic in messages. The top terms are selected based on their rank. Analysis of demographic information associated with members that mentioned top terms associated with a topic is displayed in graphical format that highlights the relationship between the age, gender, and usage of the top terms over time. The demographic information presented includes average age of members that mentioned a top term or their gender information within a selected time interval.

Abstract: Structured information about nodes may be generated and shared using sub-nodes. A node in a social networking system may be associated with sub-nodes that are definable by the node owner, such as menu items for a restaurant or songs in playlists for an artist. Users of the system may interact with the sub-nodes, and the interactions may be presented back on the page to a user, aggregated according to the user's connections in the social networking system (e.g., which songs your friends listened to the most by the artist, which menu items were consumed the most). Users may associate other sub-nodes to the node, such as identifying other menu items served by a restaurant, and the node owner may confirm these associations. Location awareness functionalities may be used to inform a user of highly recommended sub-nodes nearby as indicated by other users of the social networking system.

Abstract: A social networking system displays information about a user via a timeline included in a profile page. Narrative data about a user stored in a social networking system, including data about the user and social network activities related to the user is accessed. Timeline units summarizing information about the user are generated from the narrative data. When a viewing user requests a profile page, timeline units are displayed based information related to the viewing user and interactions with the profile page by other users connected to the viewing user. Users that administer the profile page are presented with additional information when viewing the profile page, allowing the administering users to modify appearance of the timeline or to identify actions for an administering user to take to increase interaction with the profile page.

Abstract: Structured information about nodes may be generated and shared using sub-nodes. A node in a social networking system may be associated with sub-nodes that are definable by the node owner, such as menu items for a restaurant or songs in playlists for an artist. Users of the system may interact with the sub-nodes, and the interactions may be presented back on the page to a user, aggregated according to the user's connections in the social networking system (e.g., which songs your friends listened to the most by the artist, which menu items were consumed the most). Users may associate other sub-nodes to the node, such as identifying other menu items served by a restaurant, and the node owner may confirm these associations. Location awareness functionalities may be used to inform a user of highly recommended sub-nodes nearby as indicated by other users of the social networking system.

Abstract: Structured information about nodes may be generated and shared using sub-nodes. A node in a social networking system may be associated with sub-nodes that are definable by the node owner, such as menu items for a restaurant or songs in playlists for an artist. Users of the system may interact with the sub-nodes, and the interactions may be presented back on the page to a user, aggregated according to the user's connections in the social networking system (e.g., which songs your friends listened to the most by the artist, which menu items were consumed the most). Users may associate other sub-nodes to the node, such as identifying other menu items served by a restaurant, and the node owner may confirm these associations. Location awareness functionalities may be used to inform a user of highly recommended sub-nodes nearby as indicated by other users of the social networking system.

Abstract: Users in public forums often mention certain topics in the course of their discussions. Member's comments in messages to other members are analyzed to obtain terms that co-occur with topics. Frequencies of co-occurrence of a term with topics are normalized based on frequency of the term in a random sample of message. The terms are ranked by their normalized frequency of co-occurrence with a topic in messages. The top terms are selected based on their rank. Analysis of demographic information associated with members that mentioned top terms associated with a topic is displayed in graphical format that highlights the relationship between the age, gender, and usage of the top terms over time. The demographic information presented includes average age of members that mentioned a top term or their gender information within a selected time interval.

Abstract: Users in public forums often mention certain topics in the course of their discussions. Member's comments in messages to other members are analyzed to obtain terms that co-occur with topics. Frequencies of co-occurrence of a term with topics are normalized based on frequency of the term in a random sample of message. The terms are ranked by their normalized frequency of co-occurrence with a topic in messages. The top terms are selected based on their rank. Analysis of demographic information associated with members that mentioned top terms associated with a topic is displayed in graphical format that highlights the relationship between the age, gender, and usage of the top terms over time. The demographic information presented includes average age of members that mentioned a top term or their gender information within a selected time interval.

Abstract: Users in public forums often mention certain topics in the course of their discussions. Member's comments in messages to other members are analyzed to obtain terms that co-occur with topics. Frequencies of co-occurrence of a term with topics are normalized based on frequency of the term in a random sample of message. The terms are ranked by their normalized frequency of co-occurrence with a topic in messages. The top terms are selected based on their rank. Analysis of demographic information associated with members that mentioned top terms associated with a topic is displayed in graphical format that highlights the relationship between the age, gender, and usage of the top terms over time. The demographic information presented includes average age of members that mentioned a top term or their gender information within a selected time interval.

Abstract: Users in public forums often mention certain topics in the course of their discussions. Member's comments in messages to other members are analyzed to obtain terms that co-occur with topics. Frequencies of co-occurrence of a term with topics are normalized based on frequency of the term in a random sample of message. The terms are ranked by their normalized frequency of co-occurrence with a topic in messages. The top terms are selected based on their rank. Analysis of demographic information associated with members that mentioned top terms associated with a topic is displayed in graphical format that highlights the relationship between the age, gender, and usage of the top terms over time. The demographic information presented includes average age of members that mentioned a top term or their gender information within a selected time interval.