Limiting Tags Fosters Efficiency

TL;DR

This paper investigates how limiting tags in online communities like Stack Overflow can enhance tagging efficiency by promoting diversity and novelty, counteracting the decline in tag descriptiveness over time.

Contribution

It introduces a novel information-theoretic analysis of tag efficiency under tag limit constraints and proposes a statistical model explaining how diversity improves long-term efficiency.

Findings

Tagging efficiency stabilizes over time despite increasing tag content.

Limiting tags encourages diversity and novelty, which enhance efficiency.

A statistical model supports the positive impact of diversity on long-term tagging performance.

Abstract

Tagging facilitates information retrieval in social media and other online communities by allowing users to organize and describe online content. Researchers found that the efficiency of tagging systems steadily decreases over time, because tags become less precise in identifying specific documents, i.e., they lose their descriptiveness. However, previous works did not answer how or even whether community managers can improve the efficiency of tags. In this work, we use information-theoretic measures to track the descriptive and retrieval efficiency of tags on Stack Overflow, a question-answering system that strictly limits the number of tags users can specify per question. We observe that tagging efficiency stabilizes over time, while tag content and descriptiveness both increase. To explain this observation, we hypothesize that limiting the number of tags fosters novelty and diversity in tag usage, two properties which are both beneficial for tagging efficiency. To provide qualitative evidence supporting our hypothesis, we present a statistical model of tagging that demonstrates how novelty and diversity lead to greater tag efficiency in the long run. Our work offers insights into policies to improve information organization and retrieval in online communities.