[论文解读] Will This Paper Increase Your h-index? Scientific Impact Prediction

Yuxiao Dong, R. A. Johnson+1 more|arXiv (Cornell University)|Dec 15, 2014

scientometrics and bibliometrics research参考文献 26被引用 11

一句话总结

本文提出了一种新颖的科学影响力预测模型，旨在回答一个实际问题：'这篇论文会提高我的h指数吗？' 基于来自ArnetMiner的200万篇论文数据集，该模型识别出作者在主题领域的专长以及发表期刊/会议是决定性因素，能够在五年内实现超过87.5%的预测准确率。该模型将关注点从引文数量预测转向h指数贡献预测，为希望最大化学术影响力的科研人员提供了可操作的见解。

ABSTRACT

Scientific impact plays a central role in the evaluation of the output of scholars, departments, and institutions. A widely used measure of scientific impact is citations, with a growing body of literature focused on predicting the number of citations obtained by any given publication. The effectiveness of such predictions, however, is fundamentally limited by the power-law distribution of citations, whereby publications with few citations are extremely common and publications with many citations are relatively rare. Given this limitation, in this work we instead address a related question asked by many academic researchers in the course of writing a paper, namely: "Will this paper increase my h-index?" Using a real academic dataset with over 1.7 million authors, 2 million papers, and 8 million citation relationships from the premier online academic service ArnetMiner, we formalize a novel scientific impact prediction problem to examine several factors that can drive a paper to increase the primary author's h-index. We find that the researcher's authority on the publication topic and the venue in which the paper is published are crucial factors to the increase of the primary author's h-index, while the topic popularity and the co-authors' h-indices are of surprisingly little relevance. By leveraging relevant factors, we find a greater than 87.5% potential predictability for whether a paper will contribute to an author's h-index within five years. As a further experiment, we generate a self-prediction for this paper, estimating that there is a 76% probability that it will contribute to the h-index of the co-author with the highest current h-index in five years. We conclude that our findings on the quantification of scientific impact can help researchers to expand their influence and more effectively leverage their position of "standing on the shoulders of giants."

研究动机与目标

• 为科研人员面临的实际问题提供解答：'这篇论文会提高我的h指数吗？'
• 克服引文数量预测的局限性，后者受幂律分布影响而产生偏差
• 识别出推动论文对第一作者h指数产生贡献的关键因素
• 将h指数贡献预测正式化为一个以h指数作为动态阈值的新科学影响力预测问题
• 为科研人员提供可操作的见解，以战略性地提升其学术影响力

提出的方法

• 将h指数贡献预测形式化为二分类任务，目标是判断某篇论文是否会在五年内使第一作者的h指数上升
• 使用来自ArnetMiner的大规模学术数据集（170万名作者，200万篇论文，800万条引文）进行模型训练与评估
• 纳入六类因素：作者专长、发表场所、主题流行度、合作者影响力、出版年份以及引文历史
• 通过特征工程量化作者在该出版主题上的权威性，基于历史引文模式
• 采用机器学习模型（如XGBoost、逻辑回归）基于工程化特征预测h指数是否会上升
• 使用五年预测窗口验证模型性能，并以AUC和准确率作为评估指标

实验结果

研究问题

• RQ1 哪些因素最能预测某篇论文是否会提升第一作者的h指数？
• RQ2 作者专长和发表场所的预测能力与主题流行度及合作者影响力相比如何？
• RQ3 在引文分布高度偏斜的背景下，h指数贡献能在多大程度上被高精度预测？
• RQ4 当将该模型应用于论文自身的自我预测时，其表现如何？
• RQ5 这些预测模式在不同科学领域中是否具有一致性？

主要发现

• 作者在论文主题领域的专长是决定论文是否能提升第一作者h指数的最关键因素。
• 论文发表的场所是第二关键因素，其影响力显著超过主题流行度和合作者影响力。
• 主题流行度和合作者h指数与论文提升第一作者h指数的能力之间表现出出人意料的弱相关性。
• 该模型在五年内对论文是否能贡献于第一作者h指数的预测准确率超过87.5%。
• 本论文的自我预测显示，其有76%的概率会使合作者中当前h指数最高的那位在五年内提升h指数。
• 研究结果表明，基于作者专长和发表场所的战略性出版决策，可显著增强学术影响力。