Bluefin Labs, Inc.

Twitter, Inc.

Michael Ben Fleischman

Deb Kumar Roy

Jeremy Rishel

Matthew Miller

Richard Douglas Whitcomb, Jr

Mark Watabe

Anthony Sciola

Anjali Midha

Noah Vihinen

Andrew John Carroll

Indranrita Deshmukh

Artur B Adib

Richard Douglas Whitcomb

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.

Generating audience response metrics and ratings from social interest in time-based media

A messaging system automatically populates a stream of messages using only a seed selected by the requesting account holder. In one embodiment, the seed includes the streams of one or more of the 'top' accounts of the messaging system . Here, 'top' is according to any one of a number of different metrics stored in the messaging system. With knowledge of the seed, the messaging system automatically populates a stream for the requesting account holder, without requiring any other input. As a result, an account holder is provided with a fully functioning stream with very little effort or knowledge required on their part.

Automated social message stream population

A messaging system automatically populates a stream of messages using only a seed selected by the requesting account holder. In one embodiment, the seed includes the streams of one or more of the 'top' accounts of the messaging system. Here, 'top' is according to any one of a number of different metrics stored in the messaging system. With knowledge of the seed, the messaging system automatically populates a stream for the requesting account holder, without requiring any other input. As a result, an account holder is provided with a fully functioning stream with very little effort or knowledge required on their part.

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 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.

Social networking system targeted message synchronization

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.

Topic and time based media affinity estimation

A real time messaging platform identifies an audio snippet of a time-based media (TBM) event. The messaging platform maintains a real time repository of concurrently broadcasting TBM events as well as a historical repository of previously broadcast TBM events. These repositories contain acoustic fingerprints of their respective TBM events. The messaging platform matches an acoustic fingerprint of the audio snippet with one of the stored acoustic fingerprints to identify the TBM event in the recorded snippet. To identify the TBM event, the messaging platform matches multiple overlapping reference audio segments of the reference audio stream with multiple test audio segments of the audio snippet. This allows the platform to account for time delays between the test and reference audio segments that would otherwise hinder the matching process.

Identification of concurrently broadcast time-based media

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.

Estimating social interest in time-based media

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.

Growing community of inventors

Somerville, MA, United States of America

Michael Ben Fleischman