DBScholar

Back to papers

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)

Paper ID
241
Venue
CIDR
Year
2015
Pagerank
-
Overall Rank
13,567 | 6.92%
DOI
-

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

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}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank Citing Paper Year Venue Pagerank
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
Previous Page 1 / 1 Next

Semantically Similar Papers