Discovering items with potential popularity on social media

TL;DR

This paper introduces a novel temporal model for predicting potential popular items on social media by considering recent interactions and popularity decay, outperforming existing models on multiple datasets.

Contribution

The paper presents a new model that uses only temporal features to identify potentially popular items, addressing the long-tailed distribution problem caused by preferential attachment.

Findings

The model achieves higher prediction accuracy than state-of-the-art methods.

It effectively identifies potential popular items by analyzing recent user interactions.

Experimental results on Movielens, Netflix, and Facebook datasets validate its effectiveness.

Abstract

Predicting the future popularity of online content is highly important in many applications. Preferential attachment phenomena is encountered in scale free networks.Under it's influece popular items get more popular thereby resulting in long tailed distribution problem. Consequently, new items which can be popular (potential ones), are suppressed by the already popular items. This paper proposes a novel model which is able to identify potential items. It identifies the potentially popular items by considering the number of links or ratings it has recieved in recent past along with it's popularity decay. For obtaining an effecient model we consider only temporal features of the content, avoiding the cost of extracting other features. We have found that people follow recent behaviours of their peers. In presence of fit or quality items already popular items lose it's popularity. Prediction accuracy is measured on three industrial datasets namely Movielens, Netflix and Facebook wall post. Experimental results show that compare to state-of-the-art model our model have better prediction accuracy.