Authorship Attribution with Topic Drift Model

Min Yang, Dingju Zhu, Yong Tang, Jingxuan Wang

AAAI 2017 pp. 5015-5016

doi:10.1609/AAAI.V31I1.11062 /aaai/2017/yang2017aaai-authorship/

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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Cite

Text

Yang et al. "Authorship Attribution with Topic Drift Model." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.11062

Markdown

[Yang et al. "Authorship Attribution with Topic Drift Model." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/yang2017aaai-authorship/) doi:10.1609/AAAI.V31I1.11062

BibTeX

@inproceedings{yang2017aaai-authorship,
  title     = {{Authorship Attribution with Topic Drift Model}},
  author    = {Yang, Min and Zhu, Dingju and Tang, Yong and Wang, Jingxuan},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {5015-5016},
  doi       = {10.1609/AAAI.V31I1.11062},
  url       = {https://mlanthology.org/aaai/2017/yang2017aaai-authorship/}
}