Fananalytic

Fananalytic is a cross-disciplinary practice that applies data-driven analysis to fans and fan cultures. It blends methods from data science, analytics, fan studies, and digital humanities to understand how fans engage with media properties, create content, form communities, and influence reception and markets.

Common data sources include social media posts, comments, forums, fan wikis, streaming data, and archives of fan fiction or fan art. Techniques used range from network analysis of fan communities and sentiment analysis of conversations to topic modeling, time-series analysis of trends, and qualitative interviews or ethnography to capture motivations.

The approach is used for purposes such as informing marketing and product strategy, guiding platform design and moderation, supporting community management, and contributing to scholarly work in areas like media studies and cultural analytics.

Origins of fananalytic lie in the growth of online communities and computational social science during the 2010s, drawing on fan studies, market research, and data analytics. It is not a single standardized discipline but a set of shared methods and questions that different researchers and organizations apply.

Ethical considerations include privacy, consent, data ownership, and representative sampling. Critics note the risk of overreliance on metrics that may not reflect fan experience and the potential for misinterpretation or manipulation. Proponents emphasize transparency about data sources, limitations, and respect for fan labor and intellectual property.