METHOD OF DETECTING FALSE INFORMATION BASED ON EXPERT ASSESSMENT - Lukova-Chuiko, Nataliya; Laptievа, Tetiana (Ukrainian Information Security Research Journal, 2024)
The article improves the method of detecting false information based on the method of expert evaluation. Expert methods are used to determine the nomenclature of quality indicators, their weighting coefficients, to measure quality indicators and evaluate them by the organoleptic method. The assessment of quality indicators by measuring, registration, and calculation methods is used to determine complex quality indicators at different levels of the hierarchy. Expert methods are based on making heuristic decisions based on the knowledge and experience accumulated by experts in a specific field in the past. The collective method of expert evaluations was chosen as the basic method for improvement. Because it has undoubted advantages compared to methods based on the usual statistical processing of the results of individual surveys. In contrast to the existing approach, the improved method allows for the selection of experts in a group, and not for correcting the answers of experts in order to obtain the required result. The peculiarity of the proposed method is that the selection of experts is done by averaging the scores. Averaging scores for each expert. Self-assessments of the expert and assessments of the same expert by the working group. This makes it possible to significantly reduce the error of the expert's real assessment. The ability to set a confidence interval for the assessment of false information will allow to obtain results that satisfy the task of detecting false information with appropriate accuracy. But this leads to solving the task of optimizing the evaluation criteria and the time to solve the set task. The scientific novelty consists in substantiating and evaluating the comparative importance of factors that limit the appointment of each individual expert to identify false information using the group expert evaluation method. The direction of further research is the task of optimizing evaluation criteria.