SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity

TL;DR

This paper introduces SEISMIC, a self-exciting point process model that accurately predicts the final popularity of social media posts, requiring no training and enabling real-time forecasting of information cascades.

Contribution

The paper presents a novel, simple, and computationally efficient model for predicting tweet popularity without the need for training or feature engineering.

Findings

Achieves 15% relative error in predicting cascade size after one hour.

Outperforms existing methods in predictive accuracy on Twitter data.

Enables real-time estimation of future resharing behavior.

Abstract

Social networking websites allow users to create and share content. Big information cascades of post resharing can form as users of these sites reshare others' posts with their friends and followers. One of the central challenges in understanding such cascading behaviors is in forecasting information outbreaks, where a single post becomes widely popular by being reshared by many users. In this paper, we focus on predicting the final number of reshares of a given post. We build on the theory of self-exciting point processes to develop a statistical model that allows us to make accurate predictions. Our model requires no training or expensive feature engineering. It results in a simple and efficiently computable formula that allows us to answer questions, in real-time, such as: Given a post's resharing history so far, what is our current estimate of its final number of reshares? Is the post resharing cascade past the initial stage of explosive growth? And, which posts will be the most reshared in the future? We validate our model using one month of complete Twitter data and demonstrate a strong improvement in predictive accuracy over existing approaches. Our model gives only 15% relative error in predicting final size of an average information cascade after observing it for just one hour.