A Scalable Framework for Stylometric Analysis of Multi-author Documents

Sarwar, Raheem; Yu, Chenyun; Nutanong, Sarana; Urailertprasert, Norawit; Vannaboot, Nattapol; Rakthanmanon, Thanawin

doi:10.1007/978-3-319-91452-7_52

Cited by 14 publications

(31 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Definition 1.1 (AISD): ''Given a corpus D of singleauthor documents written by a set A of candidate authors, identify the original author of an anonymous single-author document from A'' [2]. The second variant of authorship identification focuses on identifying the authors of a multi-author document, and is formally defined as follows [12]. Definition 1.2 (AIMD): ''Given a corpus D of multiauthor documents labeled with their co-authors, identify the co-authors of an anonymous multi-author document from a set of authors A of the given D'' [12].…”

Section: Introductionmentioning

confidence: 99%

“…The second variant of authorship identification focuses on identifying the authors of a multi-author document, and is formally defined as follows [12]. Definition 1.2 (AIMD): ''Given a corpus D of multiauthor documents labeled with their co-authors, identify the co-authors of an anonymous multi-author document from a set of authors A of the given D'' [12].…”

Section: Introductionmentioning

confidence: 99%

“…The AISD problem has been extensively investigated by researchers, and most AISD techniques have shown high accuracy levels [2]- [4], [8], [9], [12], [13]. However, little attention has been paid to solving AIMD problems.…”

Section: Introductionmentioning

confidence: 99%

“…That is, AISD can be considered a multi-class, single-label classification problem in which each author represents a class label. In contrast, in the AIMD problem, each document is associated with multiple authors [12]. That is, AIMD can be considered a multiclass, multi-label classification problem (see Section II-C for details).…”

Section: Introductionmentioning

confidence: 99%

“…One of the main challenges associated with AIMD problems is that each document in the corpus is associated with multiple authors. Because of the combinatoric nature of the AIMD problem, the same group of co-authors may not be repeated in the corpus, which makes such problems more challenging to model [12]. Moreover, given a multi-author document (e.g., a scholarly article), we do not know how many authors in a co-author list contributed as writers and how many only provided ideas and feedback.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

$CAG$ : Stylometric Authorship Attribution of Multi-Author Documents Using a Co-Authorship Graph

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

$CAG$ : Stylometric Authorship Attribution of Multi-Author Documents Using a Co-Authorship Graph

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

Author Gender Identification for Urdu Articles

Sarwar

2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Webometrics: evolution of social media presence of universities

et al. 2021

Self Cite

View full text Add to dashboard Cite

This paper aims at an important task of computing the webometrics university ranking and investigating if there exists a correlation between webometrics university ranking and the rankings provided by the world prominent university rankers such as QS world university ranking, for the time period of 2005-2016. However, the webometrics portal provides the required data for the recent years only, starting from 2012, which is insufficient for such an investigation. The rest of the required data can be obtained from the internet archive. However, the existing data extraction tools are incapable of extracting the required data from internet archive, due to unusual link structure that consists of web archive link, year, date, and target links. We developed an internet archive scrapper and extract the required data, for the time period of 2012-2016. After extracting the data, the webometrics indicators were quantified, and the universities were ranked accordingly. We used correlation coefficient to identify the relationship between webometrics university ranking computed by us and the original webometrics university ranking, using the spearman and pearson correlation measures. Our findings indicate a strong correlation between ours and the webometrics university rankings, which proves that the applied methodology can be used to compute the webometrics university ranking of those years for which the ranking is not available, i.e., from 2005 to 2011. We compute the webometrics ranking of the top 30 universities of North America, Europe and Asia for the time period of 2005-2016. Our findings indicate a positive correlation for North American and European universities, but weak correlation for Asian universities. This can be explained by the fact that Asian universities did not pay much attention to their websites as compared to the North American and European universities. The overall results reveal the fact that North American and European universities are higher in rank as compared to Asian universities. To the best of our knowledge, such an investigation has been executed for the very first time by us and no recorded work resembling this has been done before.

show abstract

A Scalable Framework for Stylometric Analysis of Multi-author Documents

Cited by 14 publications

References 18 publications

$CAG$ : Stylometric Authorship Attribution of Multi-Author Documents Using a Co-Authorship Graph

$CAG$ : Stylometric Authorship Attribution of Multi-Author Documents Using a Co-Authorship Graph

Author Gender Identification for Urdu Articles

Webometrics: evolution of social media presence of universities

Contact Info

Product

Resources

About