In the fast-paced, densely populated information landscape shaped by digitization, distinguishing information from misinformation is critical. Fact-checkers are effective in fighting fake news but face challenges such as cognitive overload and time pressure, which increase susceptibility to cognitive biases. Establishing standards to mitigate these biases can improve the quality of fact-checks, bolster audience trust, and protect against reputation attacks from disinformation actors. While previous research has focused on audience biases, we propose a novel approach grounded on relevance theory and the argumentum model of topics to identify (i) the biases intervening in the fact-checking process, (ii) their triggers, and (iii) at what level of reasoning they act. We showcase the predictive power of our approach through a multimethod case study involving a semi-automatic literature review, a fact-checking simulation with 12 news practitioners, and an online survey involving 40 journalists and fact-checkers. The study highlights the distinction between biases triggered by relevance by effort and effect, offering a taxonomy of cognitive biases and a method to map them within decision-making processes. These insights can inform trainings to enhance fact-checkers' critical thinking skills, improving the quality and trustworthiness of fact-checking practices.
