Integration of Social Network Analysis in the Evaluation of Advertising Communication Effectiveness and Legal Compliance

Abstract

A fuzzy genetic algorithm (FGA) is a hybrid optimization technique that combines the principles of fuzzy logic with genetic algorithms (GAs). Both fuzzy logic and genetic algorithms are powerful tools for optimization and decision-making in complex, uncertain environments. A Fuzzy Genetic Algorithm (FGA) can be a valuable approach for Social Network Analysis (SNA), where the complexity of social interactions often involves uncertainty and imprecision. This paper proposes an innovative approach to evaluate advertising communication effectiveness and legal compliance by integrating Social Network Analysis (SNA) with Sugeno Fuzzy Genetic Network Analysis (SF-GNA). By combining SNA's insights into communication dynamics and SF-GNA's ability to model complex relationships, the proposed framework offers a comprehensive assessment of advertising campaigns. The integration of SNA enables the identification of influential nodes and communication pathways within social networks, shedding light on the dissemination and reception of advertising messages. Concurrently, SF-GNA facilitates the analysis of legal compliance by assessing the fuzzy relationships between advertising content and regulatory requirements. Social Network Analysis identified key influencers within the network, with nodes A, B, and C demonstrating high centrality scores of 0.85, 0.79, and 0.76, respectively. SF-GNA evaluated the effectiveness of advertising messages, with a membership value of 0.92 indicating strong alignment with consumer preferences and engagement. SF-GNA analyzed the compliance of advertising content with regulatory standards, yielding a compliance score of 0.88, indicating adherence to legal requirements. With Specific regulatory aspects, such as truthfulness and data privacy, were evaluated with membership values of 0.90 and 0.85, respectively, indicating robust compliance.