Integration of Text Mining and Complex Social Network Analysis for Quantitative Characterization of Discursive Evolution in Artificial Intelligence Through Streaming Platforms
DOI:
https://doi.org/10.62161/revvisual.v16.5288Keywords:
Artificial Intelligence, Discursive Evolution, Text Mining, Social Network Analysis, Streaming PlatformsAbstract
This study provides a quantitative characterization of the discursive evolution in Artificial Intelligence (AI) across streaming platforms (such as YouTube), by integrating text mining and complex social network analysis. Using a comprehensive corpus from leading platforms, natural language processing algorithms were employed to analyze the textual content, identifying patterns, emerging themes, and shifts in discourse about AI. Concurrently, a social network analysis was conducted to examine the interaction structures and the influence of different actors in disseminating information. The findings reveal significant trends in the presentation and perception of AI, highlighting the evolution of specific themes, differences in perception among various groups, and the influence of factors such as technological advancements and global events. This analysis provides a deeper understanding of AI communication and perception in the digital realm, offering valuable insights for academics, communicators, and policymakers in the field of Artificial Intelligence.
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