Google Dorking Commands-based Approach for Assisting Forensic Investigators in Gender Identification of Social Media Text Data

This paper presents the creation of a text classification method for identifying gender from social media text data, specifically addressing the problems that are set on by emoticons and other special characters. To classify social media textual matter data supported by gender to provide insights into the characteristics of scammers and their targets, forensic investigators can better understand and prevent scams, ultimately reducing the negative impact of online fraud and improving overall online safety. Investigative methods have changed because of the analysis of social media text data, which has helped investigations in many different fields and provided insightful information. Also, this research tackles obstacles related to gender-based classification by utilizing methods of Google Dorking. Multiple methods for determining gender from social media text, including the complexities mentioned, are evaluated through broad evaluation and analysis. The investigation includes classification techniques, data collection, data classification, and evaluation. For easy comparisons between different social media platforms and existing datasets, this research studies online tools which include Perplexity, Google Dorking, and web scraping. Understanding the methods and difficulties involved in extracting data from text content on social media for the purpose of gender classification is crucial. This research develops a methodology for correctly identifying an individual's gender while dealing with the difficulties introduced by emoticons and special characters.