Authorship attribution deals with identifying the authors of anonymous texts. Building on our earlier finding that the Latent Dirichlet Allocation (LDA) topic model can be used t...
This paper presents Anonymouth, a novel framework for anonymizing writing style. Without accounting for style, anonymous authors risk identification. This framework is necessary t...
Andrew W. E. McDonald, Sadia Afroz, Aylin Caliskan...
Program authorship attribution—identifying a programmer based on stylistic characteristics of code—has practical implications for detecting software theft, digital forensics, a...
Nathan E. Rosenblum, Xiaojin Zhu, Barton P. Miller
In this paper, we present a novel approach for authorship attribution, the task of identifying the author of a document, using probabilistic context-free grammars. Our approach in...
Sindhu Raghavan, Adriana Kovashka, Raymond J. Moon...
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...
Subjectivity analysis and authorship attribution are very popular areas of research. However, work in these two areas has been done separately. Our conjecture is that by combining...
Most studies in statistical or machine learning based authorship attribution focus on two or a few authors. This leads to an overestimation of the importance of the features extra...
Authorship attribution is the task of identifying the author of a given text. The main concern of this task is to define an appropriate characterization of documents that captures ...
Authorship attribution is used to determine the creator of works among many candidates, playing a vital role in software forensics, authorship disputes and academic integrity inve...
Steven Burrows, Alexandra L. Uitdenbogerd, Andrew ...