Developing Natural Language Processing Algorithms to Fact-Check Speech or Text

This paper explores the development of Natural Language Processing (NLP) systems designed to fact-check speech and text through a distributed architecture. The integration of various Question-Answering (QA) systems to improve question diversity, coverage, and adapt modular frameworks to dynamic data sources is being investigated. The efficacy of these systems enhancing vast data pools critically enhances the fact-checking process. This study proposes a new approach combining existing QA systems with innovative NLP methodologies to advance the fact-checking capabilities in mitigating misinformation.