Abstract: An affinity server estimates an affinity between two different time based media events (e.g., TV, radio, social media content stream), between a time based media event and a specific topic, or between two different topics, where the affinity score represents an intersection between the populations of social media users who have authored social media content items regarding the two different events and/or topics. The affinity score represents an estimation of the real world affinity between the real world population of people who have an interest in both time based media events, both topics, or in a time based media event and a topic. One possible threshold for including a social media user in a population may be based on a confidence score that indicates the confidence that one or more social media content items authored by the social media user are relevant to the topic or event in question.

Abstract: An identification server correlates the web browsing behavior of individuals with their use of social media systems. Based on these behaviors, web identifiers (referred to as identities or IDs) may be matched to social media identifiers; these matches being determined, stored, and output as a confidences that the web identity of the user corresponds to a social media identity. Each web identity may be associated with one or more social media identities. The identity correlations between web identities and social media identities may be used along with other alignments to provide information regarding the time-based media (e.g., advertisements) a user may have likely been exposed to, and to provide targeted advertisements.

Abstract: An identification server correlates the web browsing behavior of individuals with their use of social media systems. Based on these behaviors, web identifiers (referred to as identities or IDs) may be matched to social media identifiers; these matches being determined, stored, and output as a confidences that the web identity of the user corresponds to a social media identity. Each web identity may be associated with one or more social media identities. The identity correlations between web identities and social media identities may be used along with other alignments to provide information regarding the time-based media (e.g., advertisements) a user may have likely been exposed to, and to provide targeted advertisements.

Abstract: Targeted messages are sent to users of social networking system (SNS) based on the detection of airings of advertisements in time-based media. This approach allows advertisers to leverage their investment in, for example, television advertising by sending advertisements to SNS users who have likely seen the advertisements within shows that they have expressed interest in the context of a SNS. Time-based media streams are analyzed to detect the airings of advertisements within those streams. In one embodiment, SNS content items are received regarding individual users. Based on references in those content items between users and their interests, targeted messages may be sent to users. In another embodiment, targeted messages are sent to SNS users based on the airing of advertisements and targeting criteria provided by advertisers.

Abstract: Social media content items are mapped to relevant time-based media events. These mappings are used as the basis for calculating metrics based upon the mappings, and ratings of the time-based media there from. Audience response metrics (ARMs) are calculated from the mappings, which provide an indication of audience engagement or response. In addition, ARMs provide information about the virality, depth, and breadth of the response by the viewing audience. The calculated metrics are stored and are used to generate ratings for the time-based media. The ratings may be published in whole or in part, e.g., as part of a ratings system.

Abstract: Social media content items are mapped to relevant time-based media events. These mappings may be used as the basis for multiple applications, such as ranking of search results for time-based media, automatic recommendations for time-based media, prediction of audience interest for media purchasing/planning, and estimating social interest in the time-based media. Social interest in time-based media (e.g., video and audio streams and recordings) segments is estimated through a process of data ingestion and integration. The estimation process determines social interest in specific events represented as segments in time-based media, such as particular plays in a sporting event, scenes in a television show, or advertisements in an advertising block. The resulting estimates of social interest also can be graphically displayed.

Abstract: An affinity server estimates an affinity between two different time based media events (e.g., TV, radio, social media content stream), between a time based media event and a specific topic, or between two different topics, where the affinity score represents an intersection between the populations of social media users who have authored social media content items regarding the two different events and/or topics. The affinity score represents an estimation of the real world affinity between the real world population of people who have an interest in both time based media events, both topics, or in a time based media event and a topic. One possible threshold for including a social media user in a population may be based on a confidence score that indicates the confidence that one or more social media content items authored by the social media user are relevant to the topic or event in question.

Abstract: A system and method for implementing a game of chance based on historical events. The method consists of randomly selecting a set of competitors from an event database. The competitors are presented to a player. A set of criteria comprising a subset of the competitors are received from the player. The database of events is filtered to produce a set of events comprising those events that match the set of criteria. A single event is randomly selected from the set of events to determine an outcome for the game.

Abstract: Social media content items are mapped to relevant time-based media events. These mappings are used as the basis for calculating metrics based upon the mappings, and ratings of the time-based media there from. Audience response metrics (ARMs) are calculated from the mappings, which provide an indication of audience engagement or response. In addition, ARMs provide information about the virality, depth, and breadth of the response by the viewing audience. The calculated metrics are stored and are used to generate ratings for the time-based media. The ratings may be published in whole or in part, e.g., as part of a ratings system.

Abstract: A system and method for implementing a game of chance based on historical events. The method consists of randomly selecting a set of competitors from an event database. The competitors are presented to a player. A set of criteria comprising a subset of the competitors are received from the player. The database of events is filtered to produce a set of events comprising those events that match the set of criteria. A single event is randomly selected from the set of events to determine an outcome for the game.

Abstract: Social media content items and references to events that occur therein are aligned with the time-based media events they describe. These mappings may be used as the basis for sending messages to populations of authors of content items, where the populations are determined based on whether the author has written a content item that refers to a specific TV show or advertisement. TV streams are monitored to detect when and where a specific advertisement for a particular advertiser is shown. Concurrently, social media streams are monitored for content items that refer to or are about specific TV shows and advertisements. Responsive to a specific advertisement being detected as being shown during a specific TV show, a message associated with the advertisement is sent to the authors of the content items associated with that TV show or advertisement. The messages can be transmitted while the advertisement is being shown.

Abstract: A system and method for implementing a game of chance based on historical events. The method consists of randomly selecting a set of competitors from an event database. The competitors are presented to a player. A set of criteria comprising a subset of the competitors are received from the player. The database of events is filtered to produce a set of events comprising those events that match the set of criteria. A single event is randomly selected from the set of events to determine an outcome for the game.

Abstract: A system and method for implementing a game of chance based on historical events. The method consists of randomly selecting a set of competitors from an event database. The competitors are presented to a player. A set of criteria comprising a subset of the competitors are received from the player. The database of events is filtered to produce a set of events comprising those events that match the set of criteria. A single event is randomly selected from the set of events to determine an outcome for the game.

Abstract: Social media content items are mapped to relevant time-based media events. These mappings may be used as the basis for multiple applications, such as ranking of search results for time-based media, automatic recommendations for time-based media, prediction of audience interest for media purchasing/planning, and estimating social interest in the time-based media. Social interest in time-based media (e.g., video and audio streams and recordings) segments is estimated through a process of data ingestion and integration. The estimation process determines social interest in specific events represented as segments in time-based media, such as particular plays in a sporting event, scenes in a television show, or advertisements in an advertising block. The resulting estimates of social interest also can be graphically displayed.

Abstract: Social media content items are mapped to relevant time-based media events. These mappings may be used as the basis for multiple applications, such as ranking of search results for time-based media, automatic recommendations for time-based media, prediction of audience interest for media purchasing/planning, and estimating social interest in the time-based media. Social interest in time-based media (e.g., video and audio streams and recordings) segments is estimated through a process of data ingestion and integration. The estimation process determines social interest in specific events represented as segments in time-based media, such as particular plays in a sporting event, scenes in a television show, or advertisements in an advertising block. The resulting estimates of social interest also can be graphically displayed.