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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for determining at least one metric indicative of dissemination of social media content, comprising: receiving shares data over time, forming a shares time series, indicative of how the content is being shared on social media; fitting a plurality of curve fit models to the shares time series; identifying a best curve fit model from the plurality of curve fit models that best fits the shares time series; and calculating a virality score metric, indicative of the rate of dissemination of the social media content, using the best curve fit model. 2. The method of claim 1, wherein the plurality of curve fit models comprises at least one parametric curve with an increasing gradient, followed by a decreasing gradient, and an inflection point there-between. 3. The method of claim 2, wherein the plurality of curve fit models comprises at least one of: an exponential bounded curve and a logistic curve. 4. The method of claim 1, wherein the plurality of curve fit models comprises at least one of: an exponential curve and a linear curve. 5. The method of claim 1, wherein the step of identifying the best curve fit model comprises using a Bayesian Information Criteria. 6. The method of claim 1, wherein the shares data comprises at least one of: shares, likes, re-tweets, comments, favorites, and bookmarks. 7. The method of claim 2, further comprising calculating at least one of: a hype lifetime, a number of shares at the hype lifetime, and a hype lifetime percentage, using the inflection point. 8. The method of claim 7, further comprising providing an alert when the hype lifetime percentage reaches at least one of: a predetermined threshold or a predetermined rate of change. 9. The method of claim 1, wherein the steps of receiving, fitting, identifying, and calculating are continuously performed in real-time during a predetermined sample lifetime, at a predetermined shares sample interval. 10. The method of claim 1, further comprising providing a graphical user interface showing an ex-post timeline and at least one of: a virality graphic, virality stats, and virality prediction. 11. The method of claim 1, further comprising predicting a shares growth range at a future prediction time, using at least one ensemble-based classification model, based on feature data obtained at a wait time after the content was posted to social media. 12. The method of claim 11, wherein the feature data comprises at least one of: virality and statistics-based features, post/article content-based features, comment-based features, external-based features, and account-based features. 13. The method of claim 11, wherein the feature data comprises at least one of: the virality score, a hype, a diffusion pattern, diversity/generality of topics, and similarity of content with the most recent few posts contents. 14. The method of claim 13, wherein the diversity or generality of topics is determined by calculating a distance between topics in the content. 15. The method of claim 11, wherein the step of predicting is only performed when the topic of the content fails to meet any predetermined “auto-viral” content trigger rules. 16. A method of predicting future dissemination of social media content, comprising: performing an ensemble-based classification, using feature data obtained at a wait time after the content was posted to social media, the feature data comprising at least one of: a virality score, a hype, a diffusion pattern, diversity/generality of topics, and a similarity of content with most recent few posts contents; and the classification iteratively increasing a share growth factor to determine a shares growth range at a future prediction time, which is indicative of the future dissemination of the content. 17. The method of claim 16, wherein the feature data comprise at least one of: virality and statistics-based features, post or article content-based features, comment-based features, external-based features, and account-based features. 18. The method of claim 16, wherein the diversity/generality of topics is determined by calculating a distance between topics in the content. 19. The method of claim 16, wherein the step of predicting is not performed when the topic of the content meets predetermined “auto-viral” content trigger rules. 20. The method of claim 16, further comprising providing a graphical user interface showing an ex-post timeline and at least one of: a virality graphic, virality stats, and virality prediction.