A Hybrid Fact-Checking Model: A Methodology for Integrating AI-Based Tools into the Editorial Workflow of a News Portal

Sprinchinat Kateryna

Citation: Sprinchinat Kateryna, "A Hybrid Fact-Checking Model: A Methodology for Integrating AI-Based Tools into the Editorial Workflow of a News Portal", Universal Library of Innovative Research and Studies, Special Issue.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Amid the exponential growth in the volume and velocity of misinformation, countering contemporary information threats effectively is outside the structural capacity of customary editorial fact-checking models. Manual verification remains the gold standard for quality, yet it lacks the requisite scalability for real-time operation. This lack creates critical vulnerabilities for both media and society. This methodology presents the author’s Hybrid Fact-Checking Model, a thorough sociotechnical framework that integrates artificial intelligence (AI)- based tools into newsroom workflows. It aims for faster verification, broader coverage, and greater accuracy while preserving journalistic ethics and assuring complete human supervision. The methodological novelty is found in a formalizing of three key components: (1) human, machine interaction protocols that operate for a regulation of collaboration between journalists and AI assistants; (2) a task allocation matrix that clearly demarcates zones of responsibility between automated systems that monitor and collect primary data and humans who analyze context, appraise ethics, and adjudicate finally; and (3) a risk management and effectiveness evaluation system that manages risk, evaluates effectiveness, and includes practical methods for minimization of AI hallucinations and algorithmic bias, as well as a set of key performance indicators (KPIs) for a hybrid newsroom. The methodology targets editors and media leaders, serving as a deployment-ready guide to technological modernization that aims to strengthen the competitiveness and authority of news outlets in today’s information environment.


Keywords: Hybrid Fact-Checking, AI-Assisted Verification, Misinformation Detection, Human-in-the-Loop, Algorithmic Bias Management, Newsroom KPI.

Download doi https://doi.org/10.70315/uloap.ulirs.2022.003