Is checkworthiness generalizable? Evaluating task and domain generalization of datasets for claim detection

Author:

Nenno SamiORCID

Abstract

AbstractThe spread of misinformation has reached a level at which neither research nor fact-checkers can monitor it only manually anymore. Accordingly, there has been much research on models and datasets for detecting checkworthy claims. However, the research in NLP is mostly detached from findings in communication science on misinformation and fact-checking. Checkworthiness is a notoriously vague concept whose meaning is contested among different stakeholders. Against the background of news value theory, i.e., the study of factors that make an event relevant for journalistic reporting, this is not surprising. It is argued that this vagueness leads to inconsistencies and poor generalization across different datasets and domains. For the experiments, models are trained on one dataset, tested on the remaining, and evaluated against the results on the original performance, against a random baseline, and against the scores when the models are not trained at all. The study finds that there is a drastic reduction in comparison with the performance on the original dataset. Moreover, often the models are outperformed by the random baseline and training on one dataset has no or even a negative impact on the performance on the other datasets. This paper proposes that future research should abandon this task design and instead take inspiration from research in communication science. In the style of news values, Claim Detection should focus on factors that are relevant for fact-checkers and misinformation.

Funder

Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie

Universität Bremen

Publisher

Springer Science and Business Media LLC

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3