On the Role of Conductance, Geography and Topology in Predicting Hashtag Virality

TL;DR

This paper demonstrates that conductance-based features, especially their derivatives, are crucial for early prediction of hashtag virality, outperforming existing methods on large-scale datasets.

Contribution

It introduces conductance derivative features as key indicators for virality prediction and provides extensive experimental validation showing their effectiveness.

Findings

Conductance derivatives are strong early indicators of virality.

The proposed features outperform existing methods on large datasets.

Early prediction accuracy improves with conductance-based features.

Abstract

We focus on three aspects of the early spread of a hashtag in order to predict whether it will go viral: the network properties of the subset of users tweeting the hashtag, its geographical properties, and, most importantly, its conductance-related properties. One of our significant contributions is to discover the critical role played by the conductance based features for the successful prediction of virality. More specifically, we show that the first derivative of the conductance gives an early indication of whether the hashtag is going to go viral or not. We present a detailed experimental evaluation of the effect of our various categories of features on the virality prediction task. When compared to the baselines and the state of the art techniques proposed in the literature our feature set is able to achieve significantly better accuracy on a large dataset of 7.7 million users and all their tweets over a period of month, as well as on existing datasets.