Crosstown Foundry: A Scalable Data-driven Journalism Platform for Hyper-local News
Summary: Scalable data-driven journalism platform leveraging a massive multi-modal dataset to generate hyper-local newsletters. Automates analytics and personalized publishing for LA County, advancing data integration, synthesis, and automated output in newsroom workflows. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Luciano Nocera (University of Southern California)
- 2. George Constantinou (University of Southern California)
- 3. Luan V. Tran (University of Southern California)
- 4. Seon Ho Kim (University of Southern California)
- 5. Gabriel Kahn (University of Southern California)
- 6. Cyrus Shahabi (University of Southern California)
BibTeX Citation
@inproceedings{nocera_sigmod21,
title = {{Crosstown Foundry: A Scalable Data-driven Journalism Platform for Hyper-local News}},
author = {Nocera, Luciano and Constantinou, George and Tran, Luan V. and Kim, Seon Ho and Kahn, Gabriel and Shahabi, Cyrus},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3452751},
url = {https://dl.acm.org/doi/10.1145/3448016.3452751},
year = {2021}
}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,768 | DeepTRANS: A Deep Learning System for Public Bus Travel Time Estimation using Traffic Forecasting | 2020 | VLDB | 7.1406582e-05 |
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