Spitz Andreas, Horvát Emőke-Ágnes
Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany.
Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany; Northwestern Institute on Complex Systems (NICO), Northwestern University, Evanston, Illinois, United States of America.
PLoS One. 2014 Oct 8;9(10):e108857. doi: 10.1371/journal.pone.0108857. eCollection 2014.
Traditional measures of success for film, such as box-office revenue and critical acclaim, lack the ability to quantify long-lasting impact and depend on factors that are largely external to the craft itself. With the growing number of films that are being created and large-scale data becoming available through crowd-sourced online platforms, an endogenous measure of success that is not reliant on manual appraisal is of increasing importance. In this article we propose such a ranking method based on a combination of centrality indices. We apply the method to a network that contains several types of citations between more than 40,000 international feature films. From this network we derive a list of milestone films, which can be considered to constitute the foundations of cinema. In a comparison to various existing lists of 'greatest' films, such as personal favourite lists, voting lists, lists of individual experts, and lists deduced from expert polls, the selection of milestone films is more diverse in terms of genres, actors, and main creators. Our results shed light on the potential of a systematic quantitative investigation based on cinematic influences in identifying the most inspiring creations in world cinema. In a broader perspective, we introduce a novel research question to large-scale citation analysis, one of the most intriguing topics that have been at the forefront of scientific enquiries for the past fifty years and have led to the development of various network analytic methods. In doing so, we transfer widely studied approaches from citation analysis to the the newly emerging field of quantification efforts in the arts. The specific contribution of this paper consists in modelling the multidimensional cinematic references as a growing multiplex network and in developing a methodology for the identification of central films in this network.
传统的电影成功衡量标准，如票房收入和影评赞誉，缺乏量化长期影响力的能力，且依赖于电影制作工艺本身之外的诸多因素。随着创作的电影数量不断增加，以及通过众包在线平台可获取大规模数据，一种不依赖人工评估的内生性成功衡量标准变得愈发重要。在本文中，我们提出了一种基于中心性指标组合的排名方法。我们将该方法应用于一个包含40000多部国际故事片之间多种引用类型的网络。从这个网络中，我们得出了一份具有里程碑意义的电影清单，这些电影可被视为构成电影业的基础。与各种现有的“最伟大”电影清单相比，如个人喜爱清单、投票清单、专家个人清单以及从专家投票中推导出来的清单，具有里程碑意义的电影在类型、演员和主要创作者方面的选择更加多样化。我们的研究结果揭示了基于电影影响力进行系统定量研究在识别世界电影中最具启发性作品方面的潜力。从更广泛的角度来看，我们为大规模引用分析引入了一个新的研究问题，这是过去五十年来一直处于科学研究前沿且引发了各种网络分析方法发展的最具吸引力的话题之一。通过这样做，我们将广泛研究的引用分析方法应用于新兴的艺术量化领域。本文的具体贡献在于将多维电影引用建模为一个不断增长的多重网络，并开发了一种识别该网络中核心电影的方法。
