Explicit and Implicit Knowledge in Large-Scale Linguistic Data and Digital Footprints from Social Networks

Abstract

This study explores explicit and implicit knowledge in large-scale linguistic data and digital footprints from social networks. This research aims to develop and test algorithms for analyzing both explicit and implicit information in user-generated content and digital interactions. A dataset of social media discussions on avian influenza in Moscow (RF) was collected and analyzed (tokens: 1,316,387; engagement: 108,430; audience: 39,454,014), with data collection conducted from 1 March 2023, 00:00 to 31 May 2023, 23:59. This study employs Brand Analytics, TextAnalyst 2.32, ChatGPT o1, o1-mini, AutoMap, and Tableau as analytical tools. The findings highlight the advantages and limitations of explicit and implicit information analysis for social media data interpretation. Explicit knowledge analysis is more predictable and suitable for tasks requiring quantitative assessments or classification of explicit data, while implicit knowledge analysis complements it by enabling a deeper understanding of subtle emotional and contextual nuances, particularly relevant for public opinion research, social well-being assessment, and predictive analytics. While explicit knowledge analysis provides structured insights, it may overlook hidden biases, whereas implicit knowledge analysis reveals underlying issues but requires complex interpretation. The research results emphasize the importance of integrating various scientific paradigms and artificial intelligence technologies, particularly large language models (LLMs), in the analysis of social networks.

Keywords

• social network
• explicit knowledge
• implicit knowledge
• large-scale linguistic data
• LLMs
• ChatGPT (o1, o1-mini)