Information Systems Perspective on Data Extraction in Social Media: Toward a Theoretical Framework

Abstract

The exponential growth of social media platforms has generated vast amounts of unstructured data, offering both opportunities and challenges for research and practice in the field of information systems. Effective data extraction from social media is not merely a technical problem but also an issue of integrating computational methods with organizational, social, and ethical considerations. This paper proposes a theoretical framework that situates data extraction within the broader context of information systems, highlighting the interplay between technological infrastructures, algorithmic techniques, and socio-organizational dynamics. By reviewing existing approaches to social media data extraction, including text mining, natural language processing, and big data analytics, the framework provides a structured lens for understanding the complexities of transforming unstructured social media content into meaningful insights. The study also addresses limitations such as data reliability, privacy concerns, and platform dependency, while emphasizing the importance of interdisciplinary perspectives. Ultimately, the framework seeks to advance the theoretical foundations of information systems research on social media data, bridging the gap between computational methodologies and organizational knowledge creation.