DECODING DIGITAL HEURISTICS: MIXED-METHODS ANALYSIS OF PSYCHOLOGICAL DRIVERS IN GOOGLE SEARCH BEHAVIOR

Abstract

This study uses a mixed-methods corpus analysis of Google search queries to explore how heuristic thinking influences users' language and query formulation. Combining quantitative linguistic analysis with qualitative thematic coding, it examines the interaction between fast, intuitive (System 1) and slow, deliberative (System 2) reasoning as described by Kahneman (2011). Findings reveal distinct lexical and syntactic patterns linked to cognitive biases like anchoring and availability. Integrating corpus linguistics with psychological theory, the study highlights how digital search behavior reflects underlying cognitive heuristics. The results have implications for designing more adaptive, user-centered search algorithms and advance interdisciplinary understanding between psycholinguistics and cognitive science. Additionally, the research examines how Google’s algorithms influence language use and can perpetuate societal biases, contributing valuable insights to computational linguistics, digital humanities, and information science