System and Method for Measuring and Predicting Content Dissemination in Social Networks

Number: 20180189668
Published: 2018-07-05
Filed: 2016-12-29
Assignee: Disney Enterprises, Inc.
Inventors: Ray; Abhik et al.
CPC: G06N20/20; G06N5/022; G06N20/00; G06Q10/40
Verdict: Set aside social-network content dissemination analytics/business
Source: Google Patents · FreePatentsOnline

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.

Background

BACKGROUND

It is common for media companies and other entities to use social networks, e.g., such as Facebook®, Twitter®, Google+® (or Google Plus), Snapchat®, and the like, to publish (or “post”) content that is of interest to their viewing or listening audience (or the public in general). To achieve this objective, the media companies desire to measure or predict the amount (or degree) of content dissemination reflective of audience engagement for such published content.

One challenge is that predictive techniques that rely on structural features typically require access to “friendship graphs” of social network members, which shows, e.g., for a given member, which other members are connected to the given member. Friendship graphs are useful because the structure of such graphs can provide visibility into different patterns and rates of content dissemination among members. However, social networks do not typically expose these data for privacy reasons making it very difficult for media companies and third parties (vendors, and the like) to create and evaluate metrics on how content is disseminating.

Accordingly, it would be desirable to have a method and system that can measure and predict the dissemination of content in social networks.

Claims

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. 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.