Abstract: Methods and systems for measuring and predicting content dissemination in social networks includes computing a “virality score” for popularity of social media content, a “pattern” of diffusion of the content, and a “hype” parameter of such content without requiring a “friendship graph” or “information diffusion” structured data. It also includes an iterative, predictive model, which predicts future performance (or future rate of dissemination) of the content while mitigating a class imbalance problem inherent in predicting viral posts, and which provides updates to the model based on actual performance results.

Abstract: Methods and systems for automatic generation of sports highlights are described, where AV media data of a sporting event (game) are analyzed to identify sports highlights using a data-graph-based technique for “in-game” highlights, and “out-of-game” AV media data (e.g. post-game commentary/analysis/news) are analyzed for out-of-game content (OGC) related to each of the selected in-game highlights to produce a collective AV narrative around each of the in-game highlights selected. The format and appearance of the final highlights AV media content or video are also adjustable by the user or the highlights producer to optimize the highlight viewing experience.

Abstract: Methods and systems for automatic generation of sports highlights are described, where AV media data of a sporting event (game) are analyzed to identify sports highlights using a data-graph-based technique for “in-game” highlights, and “out-of-game” AV media data (e.g. post-game commentary/analysis/news) are analyzed for out-of-game content (OGC) related to each of the selected in-game highlights to produce a collective AV narrative around each of the in-game highlights selected. The format and appearance of the final highlights AV media content or video are also adjustable by the user or the highlights producer to optimize the highlight viewing experience.

Abstract: Methods and systems for measuring and predicting content dissemination in social networks includes computing a “virality score” for popularity of social media content, a “pattern” of diffusion of the content, and a “hype” parameter of such content without requiring a “friendship graph” or “information diffusion” structured data. It also includes an iterative, predictive model, which predicts future performance (or future rate of dissemination) of the content while mitigating a class imbalance problem inherent in predicting viral posts, and which provides updates to the model based on actual performance results.