Birdwatch: Crowd Wisdom and Bridging Algorithms can Inform Understanding and Reduce the Spread of Misinformation

TL;DR

This paper introduces a matrix-factorization based algorithm that selects annotations on social media posts to reduce misinformation spread by appealing to diverse user groups, demonstrated through online data and Twitter experiments.

Contribution

The study presents a novel bridging-based ranking algorithm that improves annotation quality and reduces misinformation sharing on social media platforms.

Findings

Algorithm-selected annotations outperform baselines in informativeness and helpfulness.

Users exposed to these annotations are less likely to reshare misinformation.

The approach effectively bridges diverse user groups to enhance misinformation mitigation.

Abstract

We present an approach for selecting objectively informative and subjectively helpful annotations to social media posts. We draw on data from on an online environment where contributors annotate misinformation and simultaneously rate the contributions of others. Our algorithm uses a matrix-factorization (MF) based approach to identify annotations that appeal broadly across heterogeneous user groups - sometimes referred to as "bridging-based ranking." We pair these data with a survey experiment in which individuals are randomly assigned to see annotations to posts. We find that annotations selected by the algorithm improve key indicators compared with overall average and crowd-generated baselines. Further, when deployed on Twitter, people who saw annotations selected through this bridging-based approach were significantly less likely to reshare social media posts than those who did not see the annotations.