• CAG : stylometric authorship attribution of multi-author documents using a co-authorship graph

      Sarwar, R; Urailertprasert, N; Vannaboot, N; Yu, C; Rakthanmanon, T; Chuangsuwanich, E; Nutanong, S (Institute of Electrical and Electronics Engineers (IEEE), 2020-01-17)
      Stylometry has been successfully applied to perform authorship identification of single-author documents (AISD). The AISD task is concerned with identifying the original author of an anonymous document from a group of candidate authors. However, AISD techniques are not applicable to the authorship identification of multi-author documents (AIMD). Unlike AISD, where each document is written by one single author, AIMD focuses on handling multi-author documents. Due to the combinatoric nature of documents, AIMD lacks the ground truth information - that is, information on writing and non-writing authors in a multi-author document - which makes this problem more challenging to solve. Previous AIMD solutions have a number of limitations: (i) the best stylometry-based AIMD solution has a low accuracy, less than 30%; (ii) increasing the number of co-authors of papers adversely affects the performance of AIMD solutions; and (iii) AIMD solutions were not designed to handle the non-writing authors (NWAs). However, NWAs exist in real-world cases - that is, there are papers for which not every co-author listed has contributed as a writer. This paper proposes an AIMD framework called the Co-Authorship Graph that can be used to (i) capture the stylistic information of each author in a corpus of multi-author documents and (ii) make a multi-label prediction for a multi-author query document. We conducted extensive experimental studies on one synthetic and three real-world corpora. Experimental results show that our proposed framework (i) significantly outperformed competitive techniques; (ii) can effectively handle a larger number of co-authors in comparison with competitive techniques; and (iii) can effectively handle NWAs in multi-author documents.
    • An effective and scalable framework for authorship attribution query processing

      Sarwar, R; Yu, C; Tungare, N; Chitavisutthivong, K; Sriratanawilai, S; Xu, Y; Chow, D; Rakthanmanon, T; Nutanong, S (Institute of Electrical and Electronics Engineers (IEEE), 2018-09-10)
      Authorship attribution aims at identifying the original author of an anonymous text from a given set of candidate authors and has a wide range of applications. The main challenge in authorship attribution problem is that the real-world applications tend to have hundreds of authors, while each author may have a small number of text samples, e.g., 5-10 texts/author. As a result, building a predictive model that can accurately identify the author of an anonymous text is a challenging task. In fact, existing authorship attribution solutions based on long text focus on application scenarios, where the number of candidate authors is limited to 50. These solutions generally report a significant performance reduction as the number of authors increases. To overcome this challenge, we propose a novel data representation model that captures stylistic variations within each document, which transforms the problem of authorship attribution into a similarity search problem. Based on this data representation model, we also propose a similarity query processing technique that can effectively handle outliers. We assess the accuracy of our proposed method against the state-of-the-art authorship attribution methods using real-world data sets extracted from Project Gutenberg. Our data set contains 3000 novels from 500 authors. Experimental results from this paper show that our method significantly outperforms all competitors. Specifically, as for the closed-set and open-set authorship attribution problems, our method have achieved higher than 95% accuracy.