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Peckalytics: analyzing experts and interests on Twitter

Published: 22 June 2013 Publication History

Abstract

We provide a description of Peckalytics, its technology and functionality. Peckalytics processes the entire Twitter data stream in real time and provides a flexible search interface to identify experts in any topic area as well as users with interests in any topic. It provides flexible analytics around sets of experts, their followers as well as sets of users with specific interests. The system is implemented to scale for large data sizes. At the time of this writing it operates on an archive of 30 billion tweets, with 220,000 new tweets crawled every minute. In addition to raw tweets, the social graph of users, and profile information, Peckalytics makes novel use of Twitter lists to assess the expertise of different users. Our aim is to facilitate targeting and optimization of advertising campaigns on the Twitter platform.

References

[1]
T. Inc. Twitter for small business. 2012.
[2]
D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. 2003.

Cited By

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  • (2016)Mining half a billion topical experts across multiple social networksSocial Network Analysis and Mining10.1007/s13278-016-0356-76:1Online publication date: 22-Aug-2016
  • (2014)Price trade-offs in social media advertisingProceedings of the second ACM conference on Online social networks10.1145/2660460.2660462(169-176)Online publication date: 1-Oct-2014

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    cover image ACM Conferences
    SIGMOD '13: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
    June 2013
    1322 pages
    ISBN:9781450320375
    DOI:10.1145/2463676
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 22 June 2013

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    Author Tags

    1. experts analysis
    2. interest analysis
    3. twitter analytics

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    SIGMOD/PODS'13
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    SIGMOD '13 Paper Acceptance Rate 76 of 372 submissions, 20%;
    Overall Acceptance Rate 785 of 4,003 submissions, 20%

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    Cited By

    View all
    • (2016)Mining half a billion topical experts across multiple social networksSocial Network Analysis and Mining10.1007/s13278-016-0356-76:1Online publication date: 22-Aug-2016
    • (2014)Price trade-offs in social media advertisingProceedings of the second ACM conference on Online social networks10.1145/2660460.2660462(169-176)Online publication date: 1-Oct-2014

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