Багатофакторна модель виявлення пропаганди в текстових даних

Detecting elements of propaganda in large volumes of textual data is currently one of the key tools in combating the information warfare taking place worldwide. This paper presents a multifactor model for determining the level of propaganda in a publication. The analyzed publications included text-based news articles and social media posts, which were processed using both quantitative and semantic text analysis methods. The model was constructed using the method of linear convolution, which enables the integration of multiple heterogeneous indicators into a unified value reflecting the degree of propaganda. The proposed model considers thirteen indicators, each of which, when exhibiting a high value, signals the potential presence of propaganda within a text. The indicators encompass lexical, syntactic, and semantic characteristics such as emotional tone, subjective evaluation, presence of manipulative triggers, and calls to action. The value of each indicator was calculated using methods of statistical analysis, intelligent data analysis, and machine learning. An algorithm for determining the influence level of each factor was proposed, as well as a scale for assessing the overall level of propaganda. For every analyzed publication, a utility function value was computed to quantify its propaganda intensity. The threshold value of this utility function – beyond which a publication is considered propagandistic – was defined as the sample mean across the dataset. This approach allows for an objective classification of textual materials without the need for expert labeling. The advantage of the developed method lies in the fact that each indicator is derived exclusively from empirical statistical data and validated computational procedures, ensuring the elimination of human subjectivity. The study demonstrates that the modified multifactor model can serve as a universal analytical tool for detecting propaganda in various types of textual data, thereby enhancing the transparency and reliability of media content analysis.