Prioritizing Original News on Facebook
This work addresses the challenge of news quality for social media users and platforms, though it is incremental as it builds on existing methods like PageRank.
The researchers tackled the problem of identifying and prioritizing original news content on Facebook by developing a system that calculates an originality score using normalized PageRank values and three-step clustering, refreshed hourly. Their deployment showed that this approach increased user engagement with news and improved proprietary cumulative metrics.
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.