Uncertainty Estimation For Community Standards Violation In Online Social Networks
Narjes Torabi, Nimar S. Arora, Emma Yu, Kinjal Shah, Wenshun Liu,, Michael Tingley

TL;DR
This paper introduces two novel statistical methods, Bucketed-Beta-Binomial and Bucketed-Gaussian Process, to improve the estimation of the upper bound of content violations in online social networks, especially in low prevalence scenarios.
Contribution
It proposes new techniques for prevalence estimation that outperform existing bootstrapping methods in coverage, addressing challenges of low violation rates and limited ground truth labels.
Findings
Better coverage than bootstrapping in real and simulated data
Effective in low prevalence regimes ($10^{-4}$ to $10^{-5}$)
Improves accuracy of community standards violation estimates
Abstract
Online Social Networks (OSNs) provide a platform for users to share their thoughts and opinions with their community of friends or to the general public. In order to keep the platform safe for all users, as well as to keep it compliant with local laws, OSNs typically create a set of community standards organized into policy groups, and use Machine Learning (ML) models to identify and remove content that violates any of the policies. However, out of the billions of content that is uploaded on a daily basis only a small fraction is so unambiguously violating that it can be removed by the automated models. Prevalence estimation is the task of estimating the fraction of violating content in the residual items by sending a small sample of these items to human labelers to get ground truth labels. This task is exceedingly hard because even though we can easily get the ML scores or features for…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Network Security and Intrusion Detection · Mobile Crowdsensing and Crowdsourcing
