Using a Model of Social Dynamics to Predict Popularity of News

TL;DR

This paper presents a stochastic model of user behavior on social media to predict content popularity, leveraging early reactions and site design features, validated on Digg data.

Contribution

It introduces a novel stochastic modeling approach that improves popularity prediction accuracy by incorporating social dynamics and interface effects.

Findings

Model accurately predicts Digg news story popularity.

Incorporating site design improves prediction over simple early vote extrapolation.

Early user reactions are strong indicators of future popularity.

Abstract

Popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both companies that host social media sites and their users. Accurate and timely prediction would enable the companies to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the ever-growing amount of content. Predicting popularity of content in social media, however, is challenging due to the complex interactions among content quality, how the social media site chooses to highlight content, and influence among users. While these factors make it difficult to predict popularity \emph{a priori}, we show that stochastic models of user behavior on these sites allows predicting popularity based on early user reactions to new content. By incorporating aspects of the web site design, such models improve on predictions based on simply extrapolating from the early votes. We validate this claim on the social news portal Digg using a previously-developed model of social voting based on the Digg user interface.