# Prioritizing Original News on Facebook

arXiv: https://arxiv.org/abs/2102.08465 · HTML: https://mlsanity.com/paper/2102.08465/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [PyTorch-BigGraph: A Large-scale Graph Embedding System](https://mlsanity.com/paper/1903.12287/) (2019)
- [The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race](https://mlsanity.com/paper/1701.03017/) (2017)
- [Fame for sale: efficient detection of fake Twitter followers](https://mlsanity.com/paper/1509.04098/) (2015)
- [Preserving Integrity in Online Social Networks](https://mlsanity.com/paper/2009.10311/) (2020)
- [MediaRank: Computational Ranking of Online News Sources](https://mlsanity.com/paper/1903.07581/) (2019)
- [Breaking the News: First Impressions Matter on Online News](https://mlsanity.com/paper/1503.07921/) (2015)

## Built on by (most cited first, 2 of 2)

- [TopGQ: Fast GNN Post-Training Quantization Leveraging Topology Information](https://mlsanity.com/paper/2608.30394/) (2026)
- [RMB: Reward Model Boosting Mitigates Reward Hacking](https://mlsanity.com/paper/2609.33221/) (2026)

## Abstract (arXiv)

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.