<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/10013763" mdate="2024-08-13 00:00:00"> <author>Wenbo Wang and Yi-fang Brook Wu</author> <title>Automated FactChecking By Incorporating Contextual Knowledge and MultiFaceted Search</title> <pages>487 - 496</pages> <year>2024</year> <volume>18</volume> <number>8</number> <journal>International Journal of Cognitive and Language Sciences</journal> <ee>https://publications.waset.org/pdf/10013763</ee> <url>https://publications.waset.org/vol/212</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. As a means to address this phenomenon, automated factchecking has emerged as a safeguard against the spread of misinformation and disinformation. Existing factchecking approaches aim to determine whether a news claim is true or false, and they have achieved decent veracity prediction accuracy. However, the state of the art methods rely on manually verified external information to assist the checking model in making judgments, which requires significant human resources. This study presents a framework, SAC, which focuses on 1) augmenting the representation of a claim by incorporating additional context using generalpurpose, comprehensive and authoritative data; 2) developing a search function to automatically select relevant, new and credible references; 3) focusing on the important parts of the representations of a claim and its reference that are most relevant to the factchecking task. The experimental results demonstrate that 1) Augmenting the representations of claims and references through the use of a knowledge base, combined with the multihead attention technique, contributes to improved performance of factchecking. 2) SAC with autoselected references outperforms existing factchecking approaches with manual selected references. Future directions of this study include I) exploring knowledge graph in Wikidata to dynamically augment the representations of claims and references without introducing too much noises; II) exploring semantic relations in claims and references to further enhance factchecking.</abstract> <index>Open Science Index 212, 2024</index> </article>