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Title

Real-time Event Detection in Twitter: A Case Study

Author(s)

Momen Sani Ali | MOEINI ALI

Pages

  -

Abstract

 In this paper we present the study which uses hashing as a vectorizer and locality sensitive hashing to approximately find similar items, combined with incremental clustering to implement a practical Real-Time Event Detection algorithm. By gathering a substantial amount of Persian tweets, the proposed algorithm is evaluated. It is shown that the presented pipeline and methods are capable of detecting the events related to 7 out of 10 Football matches during the days in which the Iranian national Football team took part in the 2018 FIFA World Cup. A total of 102 events were detected with a precision of 87. 25%.

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    APA: Copy

    Momen Sani, Ali, & MOEINI, ALI. (2020). Real-time Event Detection in Twitter: A Case Study. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949257/en

    Vancouver: Copy

    Momen Sani Ali, MOEINI ALI. Real-time Event Detection in Twitter: A Case Study. 2020. Available from: https://sid.ir/paper/949257/en

    IEEE: Copy

    Ali Momen Sani, and ALI MOEINI, “Real-time Event Detection in Twitter: A Case Study,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2020, [Online]. Available: https://sid.ir/paper/949257/en

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    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
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