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    A Continuous Bursty Topic Discovery from Twitter through Topic Sketch

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    https://www.riss.kr/link?id=A104314223

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    Twitter has turned out to be one of the biggest microblogging stages for clients around the globe to impart anything happening around them to companions and past. A bursty theme in Twitter is one that triggers a surge of applicable tweets inside a brief timeframe, which frequently reflects vital occasions of mass intrigue. Step by step instructions to influence Twitter for early location of bursty points has in this way turn into a vital research issue with enormous viable esteem. Regardless of the abundance of research work on subject demonstrating and investigation in Twitter, it remains a test to distinguish bursty themes progressively. As existing strategies can barely scale to handle the assignment with the tweet stream progressively, we propose in this paper TopicSketch, a draw based subject model together with an arrangement of procedures to accomplish continuous discovery. We assess our answer on a tweet stream with more than 30 million tweets. Our investigation comes about show both productivity and viability of our approach. Particularly it is additionally exhibited that TopicSketch on a solitary machine can possibly handle several millions tweets for each day, which is on the same scale of the total number of daily tweets in Twitter, and present bursty events in finer-granularity.
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    Twitter has turned out to be one of the biggest microblogging stages for clients around the globe to impart anything happening around them to companions and past. A bursty theme in Twitter is one that triggers a surge of applicable tweets inside a bri...

    Twitter has turned out to be one of the biggest microblogging stages for clients around the globe to impart anything happening around them to companions and past. A bursty theme in Twitter is one that triggers a surge of applicable tweets inside a brief timeframe, which frequently reflects vital occasions of mass intrigue. Step by step instructions to influence Twitter for early location of bursty points has in this way turn into a vital research issue with enormous viable esteem. Regardless of the abundance of research work on subject demonstrating and investigation in Twitter, it remains a test to distinguish bursty themes progressively. As existing strategies can barely scale to handle the assignment with the tweet stream progressively, we propose in this paper TopicSketch, a draw based subject model together with an arrangement of procedures to accomplish continuous discovery. We assess our answer on a tweet stream with more than 30 million tweets. Our investigation comes about show both productivity and viability of our approach. Particularly it is additionally exhibited that TopicSketch on a solitary machine can possibly handle several millions tweets for each day, which is on the same scale of the total number of daily tweets in Twitter, and present bursty events in finer-granularity.

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    참고문헌 (Reference)

    1 G. Cormode, "What’s hot and what’s not: tracking most frequent items dynamically" 296-306, 2003

    2 C. Li, "Twevent: segment-based event detection from tweets" 155-164, 2012

    3 X. Wang, "Topics over time: a non-markov continuous-time model of topical trends" 424-433, 2006

    4 D. He, "Topic dynamics: an alternative model of bursts in streams of topics" 443-452, 2010

    5 S. Petrovi´c, "Streaming first story detection with application to twitter" 181-189, 2010

    6 S. Ross, "Stochastic processes" Wiley 1996

    7 T. Hofmann, "Probabilistic latent semantic indexing" 50-57, 1999

    8 G. P. C. Fung, "Parameter free bursty events detection in text streams" 181-192, 2005

    9 M. D. Hoffman, "Online learning for latent dirichlet allocation" 23 : 856-864, 2010

    10 K. R. Canini, "Online inference of topics with latent dirichlet allocation" 5 : 65-72, 2009

    1 G. Cormode, "What’s hot and what’s not: tracking most frequent items dynamically" 296-306, 2003

    2 C. Li, "Twevent: segment-based event detection from tweets" 155-164, 2012

    3 X. Wang, "Topics over time: a non-markov continuous-time model of topical trends" 424-433, 2006

    4 D. He, "Topic dynamics: an alternative model of bursts in streams of topics" 443-452, 2010

    5 S. Petrovi´c, "Streaming first story detection with application to twitter" 181-189, 2010

    6 S. Ross, "Stochastic processes" Wiley 1996

    7 T. Hofmann, "Probabilistic latent semantic indexing" 50-57, 1999

    8 G. P. C. Fung, "Parameter free bursty events detection in text streams" 181-192, 2005

    9 M. D. Hoffman, "Online learning for latent dirichlet allocation" 23 : 856-864, 2010

    10 K. R. Canini, "Online inference of topics with latent dirichlet allocation" 5 : 65-72, 2009

    11 J. Allan, "On-line new event detection and tracking" 37-45, 1998

    12 L. AlSumait, "On-line lda: adaptive topic models for mining text streams with applications to topic detection and tracking" 2008

    13 J. Leskovec, "Meme-tracking and the dynamics of the news cycle" 497-506, 2009

    14 D. Blei, "Latent dirichlet allocation" 3 : 993-1022, 2003

    15 T. Griffiths, "Finding scientific topics" 101 (101): 5228-5235, 2004

    16 Q. Diao, "Finding bursty topics from microblogs" 1 : 536-544, 2012

    17 J. Weng, "Event detection in twitter" 2011

    18 T. Sakaki, "Earthquake shakes twitter users: realtime event detection by social sensors" 851-860, 2010

    19 C. Jin, "Dynamically maintaining frequent items over a data stream" 287-294, 2003

    20 D. M. Blei, "Dynamic topic models" 113-120, 2006

    21 C. Wang, "Continuous time dynamic topic models" 579-586, 2008

    22 J. Kleinberg, "Bursty and hierarchical structure in streams" 7 (7): 373-397, 2003

    23 G. S. Manku, "Approximate frequency counts over data streams" 346-357, 2002

    24 G. Cormode, "An improved data stream summary: the count-min sketch and its applications" 55 (55): 58-75, 2005

    25 A. Ihler, "Adaptive event detection with time-varying poisson processes" 207-216, 2006

    26 L. Hong, "A time-dependent topic model for multiple text streams" 832-840, 2011

    27 T. Brants, "A system for new event detection" 330-337, 2003

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    학술지 이력

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    연월일 이력구분 이력상세 등재구분
    2022 평가 재인증평가 신청대상 (재인증)
    2020-02-04 학회명변경 한글명 : 사단법인 아태인문사회융합기술교류학회 -> 사단법인 미래융합기술연구학회 KCI등재
    2019-01-01 등재 등재학술지 선정 (계속평가) KCI등재
    2018-08-13 학술지명변경 외국어명 : Asia-pacific Journal or Convergent Recearch Interchange -> Asia-pacific Journal of Convergent Research Interchange KCI등재후보
    2018-02-28 학회명변경 영문명 : Asia-pacific Society for Humanities and Sociology Based Convergence Technology Exchange -> Asia-pacific Society of Convergent Research Interchange KCI등재후보
    2017-01-01 등재 등재후보학술지 선정 (신규평가) KCI등재후보
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