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首页> 外文会议>2017 ACM/IEEE Joint Conference on Digital Libraries >Understanding the Impact of Early Citers on Long-Term Scientific Impact
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Understanding the Impact of Early Citers on Long-Term Scientific Impact

机译：了解早期引用者对长期科学影响的影响

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This paper explores an interesting new dimension to the challenging problem of predicting long-term scientific impact (LTSI) usually measured by the number of citations accumulated by a paper in the long-term. It is well known that early citations (within 1-2 years after publication) acquired by a paper positively affects its LTSI. However, there is no work that investigates if the set of authors who bring in these early citations to a paper also affect its LTSI. In this paper, we demonstrate for the first time, the impact of these authors whom we call early citers (EC) on the LTSI of a paper. Note that this study of the complex dynamics of EC introduces a brand new paradigm in citation behavior analysis. Using a massive computer science bibliographic dataset we identify two distinct categories of EC - we call those authors who have high overall publication/citation count in the dataset as influential and the rest of the authors as non- influential. We investigate three characteristic properties of EC and present an extensive analysis of how each category correlates with LTSI in terms of these properties. In contrast to popular perception, we find that influential EC negatively affects LTSI possibly owing to attention stealing. To motivate this, we present several representative examples from the dataset. A closer inspection of the collaboration network reveals that this stealing effect is more profound if an EC is nearer to the authors of the paper being investigated. As an intuitive use case, we show that incorporating EC properties in the state-of-the-art supervised citation prediction models leads to high performance margins. At the closing, we present an online portal to visualize EC statistics along with the prediction results for a given query paper. The portal is accessible online at: http://www.cnergres.iitkgp.ac.in/earlyciters/. To facilitate reproducible research, we make all the codes and the processed dataset available in the public domain.
机译：本文探索了一个有趣的新维度，解决了预测长期科学影响（LTSI）这一具有挑战性的问题，该挑战通常由论文在长期中积累的引文数量来衡量。众所周知，论文的早期引用（发表后1-2年内）会对LTSI产生积极影响。但是，没有工作调查那些将这些早期引文引入论文的作者是否也影响了其LTSI。在本文中，我们首次证明了我们称之为早期引用者（EC）的这些作者对论文LTSI的影响。请注意，对EC复杂动力学的研究为引证行为分析引入了全新的范例。使用庞大的计算机科学书目数据集，我们确定了EC的两个不同类别-我们将那些在数据集中具有较高总体出版物/引用计数的作者称为有影响力的作者，将其余作者称为无影响力的作者。我们研究了EC的三个特性，并就这些特性对每种类别与LTSI的关系进行了广泛的分析。与流行的看法相反，我们发现有影响力的EC可能是由于窃取注意力而对LTSI产生了负面影响。为了激发这一点，我们从数据集中提供了几个代表性的例子。对协作网络的仔细检查表明，如果EC更接近所研究论文的作者，则这种窃取效果将更为显着。作为一个直观的用例，我们证明了将EC属性纳入最新的监督引文预测模型中会带来较高的性能裕度。在闭幕式上，我们提供了一个在线门户网站，以可视化EC统计信息以及给定查询文件的预测结果。可从以下网站在线访问该门户：http://www.cnergres.iitkgp.ac.in/earlyciters/。为了促进可重复的研究，我们在公共领域提供了所有代码和处理后的数据集。

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