In the dynamic landscape of social science and communication studies, the traditional division between qualitative and quantitative methods not only presents a notable challenge but can also be misleading. This dichotomy often fails to encapsulate the complexity and richness of human behavior, with quantitative approaches focusing on numerical data and qualitative ones emphasizing content and context. Human experiences and interactions, imbued with nuanced emotions, intentions, and meanings, resist simplistic quantification. This limitation underscores the necessity for a methodological evolution capable of more effectively harnessing the depth of human complexities.
The advent of advanced artificial intelligence (AI) and big data technologies heralds a transformative approach to overcoming these challenges: treating content as data. This innovative methodology utilizes computational tools to analyze vast amounts of textual, audio, and video content, enabling a more nuanced understanding of human behavior and social dynamics. AI, with its prowess in natural language processing, machine learning, and data analytics, serves as the cornerstone of this approach. It facilitates the processing and interpretation of large-scale, unstructured data sets across multiple modalities, which traditional methods struggle to manage.