Prioritizing Original News on Facebook

TL;DR

This paper presents a real-time system for prioritizing original news on Facebook using normalized PageRank and clustering, which enhances user engagement and news quality.

Contribution

It introduces a novel approach combining normalized PageRank and clustering to score and prioritize original news in real-time on social media.

Findings

Prioritizing original news increases user engagement.

The methodology effectively captures news dynamics.

System deployment improves news quality metrics.

Abstract

This work outlines how we prioritize original news, a critical indicator of news quality. By examining the landscape and life-cycle of news posts on our social media platform, we identify challenges of building and deploying an originality score. We pursue an approach based on normalized PageRank values and three-step clustering, and refresh the score on an hourly basis to capture the dynamics of online news. We describe a near real-time system architecture, evaluate our methodology, and deploy it to production. Our empirical results validate individual components and show that prioritizing original news increases user engagement with news and improves proprietary cumulative metrics.