Deep learning-enhanced metadata and dynamic facet representations for temporal-semantic analysis of user-generated image tags

Purpose This study aims to explore the temporal evolution of user-generated popular tags in Flickr, a collaborative image tagging system, through the lens of facet classification. It aims to uncover how tagging behaviors reflect shifts in technology, culture and individual cognition and how deep learning techniques can enhance our understanding of the semantic structures within folksonomies. Design/methodology/approach Popular tags from three benchmark years (2006, 2010 and 2015) were collected and manually categorized using a faceted classification scheme rooted in Ranganathan’s model. To augment this analysis, we applied autoencoder-based deep learning models to extract latent semantic representations and pretrained word embeddings to measure semantic similarity. This hybrid approach enabled both qualitative categorization and quantitative analysis of temporal-semantic patterns in tag usage. Findings The study found that over 80% of the most popular tags were consistently associated with spatial, personality and material facets. Temporal analysis revealed a marked shift from time-based tags to more self-expressive, personality-oriented tags, reflecting users’ growing inclination toward individualism and identity expression. Location-based tags remained persistent across all years, suggesting the global, place-based nature of user engagement. Deep learning – enhanced analysis revealed semantic groupings and confirmed that tagging behavior evolves in alignment with technological and cultural developments. Originality/value This study offers a novel integration of traditional facet analysis (colon classification) and deep learning to model the dynamics of user-generated metadata on social image platforms. It advances understanding of how folksonomies, when enriched by neural representation learning, can serve as both mirrors of cultural shifts and tools for more adaptive, user-centered metadata systems. The approach contributes methodologically to digital classification research and provides insights into the cognitive and social factors shaping online tagging behavior.