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The rise of social platforms enabling widespread information dissemination has led to an increase in threats related to disinformation attacks. Addressing these threats is crucial for ensuring access to credible information and maintaining social stability. To this end, enhancing current methods and developing new solutions aimed at ensuring safety in the digital environment is essential. In the pursuit of identifying features determining the truthfulness of disseminated information, research has primarily focused on analyzing content, stylistic means, and other linguistic aspects. To a lesser extent, the propagation process itself and the associated graphs illustrating its scope and dynamics have been examined, which can also significantly influence the truthfulness of information. To explain and counteract complex phenomena dependent on numerous factors, a multifaceted approach is crucial. This approach should encompass as many relevant areas as possible, allowing for a broader understanding of the problem and the development of countermeasures that pose a more challenging obstacle to subsequent disinformation efforts. By integrating content analysis with the study of propagation dynamics, researchers can develop more robust tools to detect and counter disinformation campaigns effectively. The conducted study focused on the statistical analysis of datasets derived from information propagation graphs. Various statistics, such as node centrality measures and network parameters, were utilized to provide insights into the network structure. The results support the hypothesis that the truthfulness of news influences the dissemination process in virtual social networks. These emerging differences can be utilized to develop solutions to detect and counter disinformation campaigns.
Bryła et al. (Fri,) studied this question.