The Magnitude of Truth: On Using Magnitude Estimation for Truthfulness Assessment
Michael Soprano, Denis Eduard Tapu, David La Barbera, Kevin Roitero, Stefano Mizzaro
摘要
Assessing the truthfulness of information is a critical task in fact-checking, and is typically performed using binary or coarse ordinal scales (2-6 levels), though fine-grained scales (e.g., 100 levels) have also been explored. Magnitude Estimation (ME) takes this approach further by allowing assessors to assign any value in the range (0, + ∞). However, it introduces challenges, including the need for aggregation of assessments from individuals with different interpretations of the scale. Despite these, its successful applications in other domains suggest its potential suitability for truthfulness assessment. We conduct a crowdsourcing study by collecting assessments on claims sourced from the PolitiFact fact-checking organization using ME. To the best of our knowledge, this is the first systematic investigation of ME in the context of truthfulness assessment. Our results show that while aggregation methods significantly impact assessment quality, optimal aggregation strategies yield accuracy and reliability comparable to traditional scales. More importantly, ME allows capturing subtle differences in truthfulness, offering richer insights than conventional coarse-grained scales.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper3
- Birds of a feather don't fact-check each other: Partisanship and the evaluation of news in Twitter's Birdwatch crowdsourced fact-checking programJennifer Allen, Cameron Martel, David G. RandCHI 2022 · 被引用 104 次
- An Effectiveness Metric for Ordinal Classification: Formal Properties and Experimental ResultsEnrique Amigó, Julio Gonzalo, Stefano Mizzaro, Jorge Carrillo-de-AlbornozACL 2020 · 被引用 39 次
- Can The Crowd Identify Misinformation Objectively?: The Effects of Judgment Scale and Assessor's BackgroundKevin Roitero, Michael Soprano, Shaoyang Fan, Damiano Spina 等SIGIR 2020 · 被引用 2 次
相关 Paper
- Efficiency and Effectiveness of LLM-Based Summarization of Evidence in Crowdsourced Fact-CheckingKevin Roitero, Dustin Wright, Michael Soprano, Isabelle Augenstein 等SIGIR 2025 · 被引用 5 次
- Crowdsourcing System for Numerical Tasks based on Latent Topic Aware Worker ReliabilityZhuan Shi, Shanyang Jiang, Lan Zhang, Yang Du 等INFOCOM 2021 · 被引用 15 次
- Frustratingly Easy Truth DiscoveryReshef Meir, Ofra Amir, Omer Ben-Porat, Tsviel Ben Shabat 等AAAI 2023 · 被引用 2 次
- Efficient Online Crowdsourcing with Complex AnnotationsReshef Meir, Viet-An Nguyen, Xu Chen, Jagdish Ramakrishnan 等AAAI 2024 · 被引用 1 次
- Playing Planning Poker in Crowds: Human Computation of Software Effort EstimatesMohammed Alhamed, Tim StorerICSE 2021 · 被引用 18 次
