# Scalable prediction of global online media news virality

## TL;DR

This paper introduces a scalable, community-based probabilistic framework for early prediction of news virality in online media, leveraging latent community structures to improve detection speed and accuracy.

## Contribution

It presents a novel, scalable probabilistic model that exploits community structures for early prediction of news virality, with efficient parallelization for large-scale data.

## Key findings

- 20% improvement in early detection accuracy
- Linear time complexity in number of reports
- Orders of magnitude speedup through parallelization

## Abstract

News reports shape the public perception of the critical social, political and economical events around the world. Yet, the way in which emergent phenomena are reported in the news makes the early prediction of such phenomena a challenging task. We propose a scalable community-based probabilistic framework to model the spreading of news about events in online media. Our approach exploits the latent community structure in the global news media and uses the affiliation of the early adopters with a variety of communities to identify the events widely reported in the news at the early stage of their spread. The time complexity of our approach is linear in the number of news reports. It is also amenable to efficient parallelization. To demonstrate these features, the inference algorithm is parallelized for message passing paradigm and tested on RPI Advanced Multiprocessing Optimized System (AMOS), one of the fastest Blue Gene/Q supercomputers in the world. Thanks to the community-level features of the early adopters, the model gains an improvement of 20% in the early detection of the most massively reported events compared to the feature-based machine learning algorithm. Its parallelization scheme achieves orders of magnitude speedup.