Authorship attribution is the problem of identifying the most plausible author of an anonymous text from a set of candidate authors. Researchers have investigated same-topic and cross-topic scenarios of authorship attribution, which differ according to whether new, unseen topics are used in the testing phase. However, neither scenario allows us to explain whether errors are caused by failure to capture authorship writing style or by the topic shift. Motivated by this, we propose the topic confusion task where we switch the author-topic configuration between the training and testing sets. This setup allows us to investigate two types of errors: one caused by the topic shift and one caused by the features’ inability to capture the writing styles. We show that stylometric features with part-of-speech tags are the least susceptible to topic variations. We further show that combining them with other features leads to significantly lower topic confusion and higher attribution accuracy. Finally, we show that pretrained language models such as BERT and RoBERTa perform poorly on this task and are surpassed by simple features such as word-level n-gram.

The Topic Confusion Task: A Novel Evaluation Scenario for Authorship Attribution

Authorship attribution infers the likely author of an unsigned, single-authored document from a pool of candidates. Despite recent advances, a lack of standard, reproducible testbeds for Chinese language documents impedes progress. In this paper, we present the Chinese Cross-Topic Authorship Attribution (CCTAA) corpus. It is the first standard testbed for authorship attribution on contemporary Chinese prose. The cross-topic design and relatively inflexible genre of newswire contribute to an appropriate level of difficulty. It supports reproducible research by using pre-defined data splits. We show that a sequence classifier based on pre-trained Chinese RoBERTa embedding and a support vector machine classifier using function character n-gram frequency features perform below expectations on this task. The code for generating the corpus and reproducing the baselines is freely available at https://codeberg.org/haining/cctaa.

CCTAA: A Reproducible Corpus for Chinese Authorship Attribution Research

Authorship attribution is the problem of identifying the most plausible author of an anonymous text from a set of candidate authors. Researchers have investigated same-topic and cross-topic scenarios of authorship attribution, which differ according to whether unseen topics are used in the testing phase. However, neither scenario allows us to explain whether errors are caused by failure to capture authorship style, by the topic shift or by other factors. Motivated by this, we propose the \emph{topic confusion} task, where we switch the author-topic configuration between training and testing set. This setup allows us to probe errors in the attribution process. We investigate the accuracy and two error measures: one caused by the models' confusion by the switch because the features capture the topics, and one caused by the features' inability to capture the writing styles, leading to weaker models. By evaluating different features, we show that stylometric features with part-of-speech tags are less susceptible to topic variations and can increase the accuracy of the attribution process. We further show that combining them with word-level $n$-grams can outperform the state-of-the-art technique in the cross-topic scenario. Finally, we show that pretrained language models such as BERT and RoBERTa perform poorly on this task, and are outperformed by simple $n$-gram features.

The Topic Confusion Task: A Novel Scenario for Authorship Attribution.

This article is the third paper in a series aimed at the establishment of the authorship of Russian-language texts. This paper considers methods for determining the authorship of classical Russian literary texts, as well as fanfiction texts. The process of determining the author was first considered in the classical version of classification experiments using a closed set of authors, and experiments were also completed for a complicated modification of the problem using an open set of authors. The use of methods to identify the author of the text is justified by the conclusions about the effectiveness of the fastText and Support Vector Machine (SVM) methods with the selection of informative features discussed in our past studies. In the case of open attribution, the proposed methods are based on the author’s combination of fastText and One-Class SVM as well as statistical estimates of a vector’s similarity measures. The feature selection algorithm for a closed set of authors is chosen based on a comparison of five different selection methods, including the previously considered genetic algorithm as a baseline. The regularization-based algorithm (RbFS) was found to be the most efficient method, while methods based on a complete enumeration (FFS and SFS) are found to be ineffective for any set of authors. The accuracy of the RbFS and SVM methods in the case of classical literary texts averaged 83%, which outperforms other selection methods by 3 to 10% for an identical number of features, and the average accuracy of fastText was 84%. For the open attribution in cross-topic classification, the average accuracy of the method based on the combination of One-Class SVM with RbFS and fastText was 85%, and for in-group classification, it was 75 to 78%, depending on the group, which is the best result among the open attribution methods considered.

/pdf/digital-authorship-attribution-in-russian-language-21wdszvk.pdf

Digital Authorship Attribution in Russian-Language Fanfiction and Classical Literature

Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation

Recent research has documented that results reported in frequently-cited authorship attribution papers are difficult to reproduce. Inaccessible code and data are often proposed as factors which block successful reproductions. Even when original materials are available, problems remain which prevent researchers from comparing the effectiveness of different methods. To solve the remaining problems—the lack of fixed test sets and the use of inappropriately homogeneous corpora—our paper contributes materials for five closed-set authorship identification experiments. The five experiments feature texts from 106 distinct authors. Experiments involve a range of contemporary non-fiction American English prose. These experiments provide the foundation for comparable and reproducible authorship attribution research involving contemporary writing.

A Call for Clarity in Contemporary Authorship Attribution Evaluation.

The success of authorship attribution relies on the presence of linguistic features specific to individual authors. There is, however, limited research assessing to what extent authorial style remains constant when individuals switch from one writing modality to another. We measure the effect of writing mode on writing style in the context of authorship attribution research using a corpus of documents composed online (in a web browser) and documents composed offline using a traditional word processor. The results confirm the existence of a “mode effect” on authorial style. Online writing differs systematically from offline writing in terms of sentence length, word use, readability, and certain part-of-speech ratios. These findings have implications for research design and feature engineering in authorship attribution studies.

/pdf/mode-effects-challenge-to-authorship-attribution-4etoim12kk.pdf

Mode Effects’ Challenge to Authorship Attribution

/pdf/the-challenge-of-vernacular-and-classical-chinese-cross-4m73j4ao0u.pdf

The Challenge of Vernacular and Classical Chinese Cross-Register Authorship Attribution.

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A Call for Clarity in Contemporary Authorship Attribution Evaluation.

Mode Effects’ Challenge to Authorship Attribution

The Challenge of Vernacular and Classical Chinese Cross-Register Authorship Attribution.