Cascade-LSTM: Predicting Information Cascades using Deep Neural Networks

TL;DR

This paper introduces a deep learning approach using LSTM networks to predict the size and speed of information cascades in social networks, enabling more accurate modeling of how information spreads.

Contribution

It presents a novel LSTM-based method for predicting the temporal and topological structure of information cascades, which was previously underexplored.

Findings

Achieved over 73% accuracy in identifying information transmitters.

Achieved over 83% accuracy in predicting early transmitters.

Successfully generated cascade trees for Reddit and Github platforms.

Abstract

Predicting the flow of information in dynamic social environments is relevant to many areas of the contemporary society, from disseminating health care messages to meme tracking. While predicting the growth of information cascades has been successfully addressed in diverse social platforms, predicting the temporal and topological structure of information cascades has seen limited exploration. However, accurately predicting how many users will transmit the message of a particular user and at what time is paramount for designing practical intervention techniques. This paper leverages Long-Short Term Memory (LSTM) neural network techniques to predict two spatio-temporal properties of information cascades, namely the size and speed of individual-level information transmissions. We combine these prediction algorithms with probabilistic generation of cascade trees into a generative test model that is able to accurately generate cascade trees in two different platforms, Reddit and Github. Our approach leads to a classification accuracy of over 73% for information transmitters and 83% for early transmitters in a variety of social platforms.