Trends Prediction Using Social Diffusion Models

TL;DR

This paper introduces an analytic model for predicting social media trends by analyzing social diffusion dynamics and past interactions, providing a probabilistic lower bound for trend spread.

Contribution

It presents a novel theoretical framework for trend prediction based on social diffusion models and validates it with real-world social media datasets.

Findings

The model can predict trend success probabilities.

Validated with datasets from MIT social experiment and eToro.

Provides a lower bound for trend spread likelihood.

Abstract

The importance of the ability of predict trends in social media has been growing rapidly in the past few years with the growing dominance of social media in our everyday's life. Whereas many works focus on the detection of anomalies in networks, there exist little theoretical work on the prediction of the likelihood of anomalous network pattern to globally spread and become "trends". In this work we present an analytic model the social diffusion dynamics of spreading network patterns. Our proposed method is based on information diffusion models, and is capable of predicting future trends based on the analysis of past social interactions between the community's members. We present an analytic lower bound for the probability that emerging trends would successful spread through the network. We demonstrate our model using two comprehensive social datasets - the "Friends and Family" experiment that was held in MIT for over a year, where the complete activity of 140 users was analyzed, and a financial dataset containing the complete activities of over 1.5 million members of the "eToro" social trading community.