Authorship Attribution with Topic Drift Model

Authors

  • Min Yang The University of Hong Kong
  • Dingju Zhu South China Normal University
  • Yong Tang South China Normal University
  • Jingxuan Wang The University of Hong Kong

DOI:

https://doi.org/10.1609/aaai.v31i1.11062

Keywords:

Authorship attribution, Topic model

Abstract

Authorship attribution is an active research direction due to its legal and financial importance. The goal is to identify the authorship of anonymous texts. In this paper, we propose a Topic Drift Model (TDM), monitoring the dynamicity of authors’ writing style and latent topics of interest. Our model is sensitive to the temporal information and the ordering of words, thus it extracts more information from texts.

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Published

2017-02-12

How to Cite

Yang, M., Zhu, D., Tang, Y., & Wang, J. (2017). Authorship Attribution with Topic Drift Model. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11062