Peckalytics: Analyzing Experts and Interests on Twitter

Summary: Real-time, scalable processing of the Twitter data stream with a flexible search to identify topic experts and interested users, on a 30B-tweet archive at ~220k/min. Uses Twitter lists to assess expertise; analytics over experts, followers, and interests for large-scale, ad-targeted insights. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 4675
Venue: SIGMOD
Year: 2013
Pagerank: -
Overall Rank: 13,621 | 6.55%
DOI: 10.1145/2463676.2463679

Incoming Non-self Citations Over Time

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Authors

1. Alex Cheng (University of Toronto)
2. Nilesh Bansal (University of Toronto)
3. Nick Koudas (University of Toronto)

BibTeX Citation

@inproceedings{cheng_sigmod13,
        title = {{Peckalytics: Analyzing Experts and Interests on Twitter}},
        author = {Cheng, Alex and Bansal, Nilesh and Koudas, Nick},
        series = {{SIGMOD} '13},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2463676.2463679},
        url = {https://dl.acm.org/doi/10.1145/2463676.2463679},
        year = {2013}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

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

Rank	Cited Paper	Year	Venue	Pagerank

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