Web Scraping Design for Text Data Acquisition on Platform X in Adolescent Sexual Deviant Behavior Research - Yanti Yusman; Noor Anida Zaria Mohd Noor (Journal of Computer Science, Artificial Intelligence and Communications, 2026)
The increasing use of social media among adolescents has generated vast amounts of digital textual data that can be utilized to investigate online behavioral phenomena, including discussions related to sexual behavior. As one of the most active social media platforms, X provides a rich source of publicly available textual information that reflects users' interactions, opinions, and behavioral expressions. However, obtaining high-quality textual data for behavioral research requires a systematic, transparent, and reproducible data acquisition process. This study aims to design a web scraping framework for acquiring textual data from Platform X to support research on adolescent sexual behavior deviance. The research adopts a Design Science Research (DSR) approach to develop a structured data acquisition pipeline consisting of requirement analysis, keyword formulation, web scraping design, data harvesting, data preprocessing, and dataset construction. The proposed framework emphasizes methodological rigor by integrating data quality assessment, reproducibility, and ethical considerations throughout the data acquisition process. The resulting dataset comprises structured textual data and relevant metadata that are prepared for subsequent analytical stages, such as natural language processing, text mining, machine learning, and behavioral pattern analysis. Furthermore, the proposed web scraping framework provides a systematic approach for researchers to collect social media data efficiently while ensuring data consistency and traceability. This study contributes to the field of social media analytics by providing a replicable methodology for acquiring textual data from Platform X and establishing a reliable foundation for future research on adolescent online behavior and digital risk assessment. The findings are expected to facilitate the development of evidence-based analytical models for understanding behavioral patterns in digital environments while supporting future studies employing Big Data Analytics and Computational Social Science.