Collapse and reorganization patterns of social knowledge representation in evolving semantic networks

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This study introduces semantic network analysis of natural language processing in collective social settings. It utilizes the spreading-activation theory of human long-term memories from social psychology to extract information and graph-theoretic linguistic approximations supporting rational propositional inference and formalisms. Using an empirical case study we demonstrate the process of extracting linguistic concepts from data and training a Hopfield artificial neural network for semantic network classification. We further develop an agent-based computational model of network evolution in order to study the processes and patterns of collective semantic knowledge representation, introducing incidents of collapses in central network structures. Large ensembles of simulation replication experiments are conducted and the resulted networks are analyzed using a variety of estimation techniques. We show how collective social structure emerges from simple interactions among semantic categories. Our findings provide evidence of the significance of collapse and reorganization effects in the structure of collective social knowledge; the statistical importance of the within-factor interactions in network evolution, and; stochastic exploration of whole parameter spaces in large ensembles of simulation runs can reveal important self-organizing aspects of the system’s behavior. The last session discusses the results and revisits the issues of generative semantic inference and the semantic networks as inferential formalisms in guiding self-organizing systemic complexity.