Understanding and Deobfuscating Textual Data: Managerial Insights and Computational Challenges

This paper addresses the business implications of deobfuscating textual data, emphasizing the costs and strategies managers must consider. Obfuscated words, often used to bypass content filters and avoid censorship, create significant challenges for employee and data monitoring, content moderation, and data analysis. While humans excel in this task due to their contextual understanding and pattern recognition abilities, machines face substantial computational hurdles, especially with the exponential growth of possible permutations needed to decode obfuscated words. This paper outlines the computational challenges of analyzing corporate text and offers strategies to mitigate costs through heuristic approaches, pre-filtering, parallel processing, and machine learning. Comparative analysis highlights humans' and machines' strengths and limitations in deobfuscating text, supported by a case study on analyzing tweets. The findings underscore the need for balanced approaches that combine computational efficiency with accuracy, which is crucial for improving content moderation and data analysis on digital platforms.