In the context of generative AI’s rapid advancement across various industries, training models require large and comprehensive datasets to improve AI models, yielding more accurate and realistic outputs. This increased demand has reshaped the landscape of data accessibility and economics, particularly with Application Programming Interfaces (APIs). This shift has led to data providers and social media platforms enforcing new access restrictions. Such changes have created significant barriers for researchers, especially those in social science research, in acquiring data. This article addresses these issues by evaluating alternative data collection methods, focusing on their application in social research. It critically examines the strengths and weaknesses of these methods, underscoring their practicality and reliability. As AI continues to transform industries, this paper provides a vital guide for researchers, data analysts, and businesses to navigate the evolving dynamics of data collection, particularly in the context of social research.