DECODING DIGITAL HEURISTICS: A MIXED-METHODS CORPUS ANALYSIS OF PSYCHOLOGICAL UNDERPINNINGS IN GOOGLE SEARCH BEHAVIOR

Abstract

The burgeoning landscape of digital information retrieval offers a unique window into the cognitive processes that underpin human decision-making. This study employs a mixed-methods corpus analysis of Google search queries to investigate how heuristic thinking informs users' language choices and query formulations. Drawing on quantitative linguistic analytics and qualitative thematic coding, the research explores the interplay between rapid, intuitive (System 1) and slower, deliberative (System 2) reasoning processes as conceptualized by Kahneman (2011) and Tversky and Kahneman (1974). Initial findings reveal discernible patterns in lexical selection and syntactic construction that correspond to documented cognitive biases, such as anchoring and availability. Through the integration of advanced corpus linguistics techniques (Biber, 1988) with psychological theory frameworks, the study illuminates how digital search behavior serves as a mirror for underlying cognitive heuristics. The implications of these findings extend to the design of more responsive and user-centered search algorithms, potentially enhancing digital interface usability and fostering adaptive human-computer interactions. Furthermore, the study contributes to the interdisciplinary dialogue between psycholinguistics and cognitive science, offering a novel perspective on how everyday digital practices can both reflect and inform our understanding of human cognition. The study uses a mixed-methods strategy to analyze Google as a technological and linguistic corpus. Through a combination of quantitative corpus linguistics methods and qualitative discourse analysis, the research probes Google's search algorithms, autocomplete suggestions, and keyword trends in order to find patterns in language use, bias, and information retrieval. The results show how Google's algorithms can shape user behavior, enforce particular linguistic patterns, and reproduce societal biases. The research contributes to the areas of computational linguistics, digital humanities, and information science by making a detailed account of Google as a dynamic, changing corpus.