Search (2 results, page 1 of 1)

  • × author_ss:"Ye, F."
  1. Bornmann, L.; Ye, A.; Ye, F.: Identifying landmark publications in the long run using field-normalized citation data (2018) 0.00 
    5.8941013E-4 = product of:
  0.008841151 = sum of:
    0.008841151 = weight(_text_:in in 4196) [ClassicSimilarity], result of:
      0.008841151 = score(doc=4196,freq=14.0), product of:
        0.044469737 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.032692216 = queryNorm
        0.19881277 = fieldWeight in 4196, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4196)
  0.06666667 = coord(1/15)
    Abstract
    The purpose of this paper is to propose an approach for identifying landmark papers in the long run. These publications reach a very high level of citation impact and are able to remain on this level across many citing years. In recent years, several studies have been published which deal with the citation history of publications and try to identify landmark publications. Design/methodology/approach In contrast to other studies published hitherto, this study is based on a broad data set with papers published between 1980 and 1990 for identifying the landmark papers. The authors analyzed the citation histories of about five million papers across 25 years. Findings The results of this study reveal that 1,013 papers (less than 0.02 percent) are "outstandingly cited" in the long run. The cluster analyses of the papers show that they received the high impact level very soon after publication and remained on this level over decades. Only a slight impact decline is visible over the years. Originality/value For practical reasons, approaches for identifying landmark papers should be as simple as possible. The approach proposed in this study is based on standard methods in bibliometrics.
  2. Kong, S.; Ye, F.; Feng, L.; Zhao, Z.: Towards the prediction problems of bursting hashtags on Twitter (2015) 0.00 
    3.1188648E-4 = product of:
  0.004678297 = sum of:
    0.004678297 = weight(_text_:in in 2338) [ClassicSimilarity], result of:
      0.004678297 = score(doc=2338,freq=2.0), product of:
        0.044469737 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.032692216 = queryNorm
        0.10520181 = fieldWeight in 2338, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2338)
  0.06666667 = coord(1/15)
    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.

Authors

Themes