Guided Clustering for Social Media Nowcasting
Summary: Guided, interactive clustering of massive social-media feature spaces (billions of features) to enable nowcasting for low-/no-training-data phenomena by surfacing interpretable clusters under multiple relatedness metrics (statistical, semantic). Supports rapid user feedback (merge/split) and optimization to avoid full re-clustering, trading conventional clustering metrics for conformity with domain priors so users can iteratively produce usable features without supervised labels. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Dolan Antenucci (University of Michigan)
BibTeX Citation
@inproceedings{antenucci_cidr15,
address = {Amsterdam, Netherlands},
series = {{CIDR} '15},
title = {{Guided Clustering for Social Media Nowcasting}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Antenucci, Dolan},
year = {2015}
}
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