Opinion Dynamics in Social Networks with Hesitant Linguistic Representations

The study of linguistic variables for representing information processing in contexts where individuals articulate their opinions constitutes a significant research domain within decision sciences and artificial intelligence. However, the integration of mathematical structures capable of capturing hesitancy through linguistic terms has not yet been explored in the context of opinion dynamics within social networks. Addressing this gap, Project UDIN aims to examine the social network interactions and opinion dynamics, particularly focusing on confronting communities using the theoretical framework of Hesitant Fuzzy Linguistic Term Sets (HFLTS) to measure interactions.

This project seeks to analize how interactions among individuals with different profiles contribute to either consensus formation or divergence of opinions.

Additionally, it will investigate whether such interactions intensify opinion polarity or stabilize at a specific polarity threshold. Project UDIN further is oriented towards identifiying whether social network communities are inherently cohesive or exhibit potential for division into sub-communities, even when members hold similar opinions. The analysis will concentrate on highly emotionally charged events, such as online sports forums, cause-driven marketing campaigns or sustainability issues, where individuals with differing perspectives engage in near real-time interactions.

The anticipated outcomes of this research include:
- Innovative methodologies for leveraging linguistic terms to capture opinion interactions.
- Enhanced explainability in large-scale opinion aggregation models.
- Comprehensive topological analysis of networks representing conflicting communities.
- Development of algorithms to extract multiscale backbones of such networks.
- Statistical validation tools to assess the robustness of results.

By achieving these outcomes, Project UDIN will contribute significantly to advancing models of opinion dynamics and aggregation in the context of social interaction-based systems.