Investigating Characteristics, Biases and Evolution of Fact-Checked Claims on the Web
Susmita Gangopadhyay (GESIS – Leibniz Institute for the Social Sciences, Cologne, Germany), Sebastian Schellhammer (GESIS – Leibniz Institute for the Social Sciences, Cologne, Germany), Salim Hafid (University of Montpellier, LIRMM, CNRS, Montpellier, France), Danilo Dessì (GESIS – Leibniz Institute for the Social Sciences, Cologne, Germany), Christian Koß (Heinrich Heine University Düsseldorf, Faculty of Arts and Humanities, Düsseldorf, Germany), Konstantin Todorov (University of Montpellier, LIRMM, CNRS, Montpellier, France), Stefan Dietze (GESIS – Leibniz Institute for the Social Sciences, Cologne, Germany & Heinrich Heine University Düsseldorf, Düsseldorf, Germany), and Hajira Jabeen (GESIS – Leibniz Institute for the Social Sciences, Cologne, Germany)
Published in HT '24: 35th ACM Conference on Hypertext and Social Media · DOI: 10.1145/3648188.3675135 · License: © Copyright held by the owner/author(s). Publication rights licensed to ACM.
Authors: Stefan Dietze, Danilo Dessi, Salim Hafid, Sumsita Gangopadhyay, Christian Koß, Hajira Jabeen, Sebastian Schellhammer, Konstantin Todorov
Keywords: Claims Analysis, Claims Classification, Fact-checking, Knowledge Graphs, Mis- and disinformation
Session: Social Media Practices
Pages: 246–258
Conference: HT'24
Abstract
Given the recent proliferation of fake news online, fact-checking has emerged as a critical defence against misinformation. Several fact-checking organisations are currently employed in the initiative to assess the truthfulness of online claims. Verified claims serve as foundational data for various cross-domain research, including fields of social science and natural language processing, where they are used to study misinformation and several downstream tasks such as automated fact-verification. However, these fact-checking websites inherently harbour biases, posing challenges for academic endeavours aiming to discern truth from misinformation. In this study, we aim to explore the evolving landscape of online claims verified by multiple fact-checking organisations and analyse the underlying biases of individual fact-checking websites. Leveraging ClaimsKG, the largest available corpus of fact-checked claims, we analyse the temporal evolution of claims, focusing on topics, veracity levels, and entities to offer insights into the complex dimensions of online information. We utilise data and dimensions available from ClaimsKG for our analysis and for dimensions such as topics which are not present in ClaimsKG, we create a topic taxonomy and implement a transformer-based model, for multi-label classification of claims. We also observe how similar claims are co-occurant amongst different websites. Our work serves as a standardised framework for categorising claims sourced from diverse fact-checking organisations, laying the foundation for coherent and interpretable fact-checking datasets. The analysis conducted in this work sheds light on the dynamic landscape of online claims verified by several fact-checking organisations and dives into biases and distributions of several fact-checking websites.
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