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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.
WhatsApp is the most popular messaging app in the world. The closed nature of the app, in addition to the ease of transferring multimedia and sharing information to large-scale groups make WhatsApp unique among other platforms, where an anonymous enc
Risk-limiting audits (RLAs) are expected to strengthen the public confidence in the correctness of an election outcome. We hypothesize that this is not always the case, in part because for large margins between the winner and the runner-up, the numbe
Many researchers work on improving the data efficiency of machine learning. What would happen if they succeed? This paper explores the social-economic impact of increased data efficiency. Specifically, we examine the intuition that data efficiency wi
This paper describes our solution to the multi-modal learning challenge of ICML. This solution comprises constructing three-level representations in three consecutive stages and choosing correct tag words with a data-specific strategy. Firstly, we us
Using time series of US patents per million inhabitants, knowledge-generating cycles can be distinguished. These cycles partly coincide with Kondratieff long waves. The changes in the slopes between them indicate discontinuities in the knowledge-gene