Search (3 results, page 1 of 1)

  • × theme_ss:"Data Mining"
  • × theme_ss:"Internet"
  • × year_i:[2010 TO 2020}
  1. Kong, S.; Ye, F.; Feng, L.; Zhao, Z.: Towards the prediction problems of bursting hashtags on Twitter (2015) 0.00 
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    Abstract
    Hundreds of thousands of hashtags are generated every day on Twitter. Only a few will burst and become trending topics. In this article, we provide the definition of a bursting hashtag and conduct a systematic study of a series of challenging prediction problems that span the entire life cycles of bursting hashtags. Around the problem of "how to build a system to predict bursting hashtags," we explore different types of features and present machine learning solutions. On real data sets from Twitter, experiments are conducted to evaluate the effectiveness of the proposed solutions and the contributions of features.
    Type
    a
  2. Derek Doran, D.; Gokhale, S.S.: ¬A classification framework for web robots (2012) 0.00 
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    Abstract
    The behavior of modern web robots varies widely when they crawl for different purposes. In this article, we present a framework to classify these web robots from two orthogonal perspectives, namely, their functionality and the types of resources they consume. Applying the classification framework to a year-long access log from the UConn SoE web server, we present trends that point to significant differences in their crawling behavior.
    Type
    a
  3. Huvila, I.: Mining qualitative data on human information behaviour from the Web (2010) 0.00 
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    Type
    a

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