# Estimating predictive uncertainty for rumour verification models

arXiv: https://arxiv.org/abs/2005.07174 · HTML: https://mlsanity.com/paper/2005.07174/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2020.

## Built on by (most cited first, 6 of 6)

- [Uncertainty in Natural Language Processing: Sources, Quantification, and Applications](https://mlsanity.com/paper/2306.04459/) (2023)
- [It's about Time: Rethinking Evaluation on Rumor Detection Benchmarks using Chronological Splits](https://mlsanity.com/paper/2302.03147/) (2023)
- [Ambiguity Meets Uncertainty: Investigating Uncertainty Estimation for Word Sense Disambiguation](https://mlsanity.com/paper/2305.13119/) (2023)
- [PANACEA: An Automated Misinformation Detection System on COVID-19](https://mlsanity.com/paper/2303.01241/) (2023)
- [Tribrid: Stance Classification with Neural Inconsistency Detection](https://mlsanity.com/paper/2109.06508/) (2021)
- [The Alignment Bottleneck in Decomposition-Based Claim Verification](https://mlsanity.com/paper/2602.10380/) (2026)

## Abstract (arXiv)

The inability to correctly resolve rumours circulating online can have harmful real-world consequences. We present a method for incorporating model and data uncertainty estimates into natural language processing models for automatic rumour verification. We show that these estimates can be used to filter out model predictions likely to be erroneous, so that these difficult instances can be prioritised by a human fact-checker. We propose two methods for uncertainty-based instance rejection, supervised and unsupervised. We also show how uncertainty estimates can be used to interpret model performance as a rumour unfolds.