Authorship attribution, the science of inferring characteristics of the author from the characteristics of documents written by that author, is a problem with a long history and a wide range of application. Recent work in "non-traditional" authorship attribution demonstrates the practicality of automatically analyzing documents based on authorial style, but the state of the art is confusing. Analyses are difficult to apply, little is known about type or rate of errors, and few "best practices" are available. In part because of this confusion, the field has perhaps had less uptake and general acceptance than is its due.

This review surveys the history and present state of the discipline, presenting some comparative results when available. It shows, first, that the discipline is quite successful, even in difficult cases involving small documents in unfamiliar and less studied languages; it further analyzes the types of analysis and features used and tries to determine characteristics of well-performing systems, finally formulating these in a set of recommendations for best practices.

作者身份归因是从作者的文献特征中推断作者特征的科学，它是一个历史悠久且适用范围广泛的问题。最近关于“非传统”作者身份的工作证明了基于作者风格自动分析文档的实用性，但是现有技术令人困惑。分析难以应用，对错误的类型或错误率知之甚少，并且几乎没有“最佳实践”。在某种程度上，由于这种混乱，该领域的吸收和普遍接受可能少于应有的程度。

这篇综述调查了该学科的历史和现状，并在可获得时给出了一些比较结果。首先，它表明该学科是相当成功的，即使在困难的情况下，涉及不熟悉和学习较少的语言的小文件；它进一步分析了所使用的分析类型和功能，并尝试确定性能良好的系统的特征，最后将其总结为一组最佳实践建议。