doubletagging

Doubletagging is the practice of assigning two distinct tags to the same item, such as a digital photo, document, or data record. The two tags may derive from separate tagging schemes or reflect different aspects of the item, such as subject matter and context, making the item accessible through multiple search paths.

In media platforms, doubletagging often means applying several hashtags to improve visibility and reach. In library and archival work, items may be tagged with a controlled vocabulary term and a second user-generated keyword to capture both formal subject classification and informal relevance.

In data labeling and machine learning, double tagging can occur when an item receives two independent labels from different annotators or label spaces, or when a single annotation is modeled as two interrelated tags. This approach can support cross-domain analysis but may require reconciliation to avoid inconsistent semantics.

Benefits of doubletagging include improved discoverability, redundancy against tag outages, and support for multiple audiences. Drawbacks include tag duplication, fragmentation of semantics, higher maintenance cost, and potential confusion if the two tags misalign or collide.

Best practice guidelines for doubletagging include defining clear roles for primary and secondary tags, using a controlled vocabulary for one tag space while allowing free-form keywords for the other, and implementing deduplication and reconciliation workflows to align overlapping terms.