An Effective and Scalable Framework for Authorship Attribution Query Processing

Sarwar, Raheem; Yu, Chenyun; Tungare, Ninad; Chitavisutthivong, Kanatip; Sriratanawilai, Sukrit; Xu, Yaohai; Dickson, Chow; Rakthanmanon, Thanawin; Nutanong, Sarana

doi:10.1109/access.2018.2869198

Cited by 14 publications

(18 citation statements)

References 43 publications

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“…Stylometry has been used extensively to differentiate between the literary styles of authors [2]- [4] Stylometry relies on the assumption that each individual author exhibits a distinct writing style, and it can be used to differentiate between documents written by different authors [2]- [4]. and has been used to solve authorship-related problems such as authorship verification, author profiling and authorship identification [2]- [4], [13], [28]- [31].…”

Section: Literature Review a Stylometrymentioning

confidence: 99%

“…Stylometry has been used extensively to differentiate between the literary styles of authors [1]- [4]. Stylometry relies on the assumption that each individual author exhibits a distinct writing style, and it can be used to differentiate between documents written by different authors [2], [3], [3]- [7].…”

Section: Introductionmentioning

confidence: 99%

“…problems aim to identify the original author of an anonymous document from a group of candidate authors [2], [8]. An authorship identification problem is generally solved by (i) computing the writing style markers (i.e., stylometric features) from documents written by candidate authors and (ii) applying a classifier to them to build a model that can identify the original author of an anonymous document [2]- [4], [9]- [11]. There are two main variants of the authorship identification problem.…”

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%

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

et al. 2020

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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.

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Section: Literature Review a Stylometrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…However, the PNN is sensitive to noise in the dataset [11]. To address this issue, we use a document transformation model that relies on set similarity search [33] such that the stylistic variations between the text samples can be computed as a set distance [12]. By using a corpus of 547 Thai documents from 200 authors, which is signifcantly larger than existing study (an increase of 32 folds in terms of the number of candidate authors), we perform experimental studies to show that our solution can (i) mitigate the efect of outliers in the dataset; (ii) handle a large number of candidate authors; (iii) perform well when the number of writing samples per candidate author is low; and (v) achieve the accuracy level of 91.02% which is higher than all competitors.…”

Section: Limitations Of Existing Studymentioning

confidence: 99%

StyloThai:

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et al. 2020

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Authorship identification helps to identify the true author of a given anonymous document from a set of candidate authors. The applications of this task can be found in several domains, such as law enforcement agencies and information retrieval. These application domains are not limited to a specific language, community, or ethnicity. However, most of the existing solutions are designed for English, and a little attention has been paid to Thai. These existing solutions are not directly applicable to Thai due to the linguistic differences between these two languages. Moreover, the existing solution designed for Thai is unable to (i) handle outliers in the dataset, (ii) scale when the size of the candidate authors set increases, and (iii) perform well when the number of writing samples for each candidate author is low. We identify a stylometric feature space for the Thai authorship identification task. Based on our feature space, we present an authorship identification solution that uses the probabilistic k nearest neighbors classifier by transforming each document into a collection of point sets. Specifically, this document transformation allows us to (i) use set distance measures associated with an outlier handling mechanism, (ii) capture stylistic variations within a document, and (iii) produce multiple predictions for a query document. We create a new Thai authorship identification corpus containing 547 documents from 200 authors, which is significantly larger than the corpus used by the existing study (an increase of 32 folds in terms of the number of candidate authors). The experimental results show that our solution can overcome the limitations of the existing solution and outperforms all competitors with an accuracy level of 91.02%. Moreover, we investigate the effectiveness of each stylometric features category with the help of an ablation study. We found that combining all categories of the stylometric features outperforms the other combinations. Finally, we cross compare the feature spaces and classification methods of all solutions. We found that (i) our solution can scale as the number of candidate authors increases, (ii) our method outperforms all the competitors, and (iii) our feature space provides better performance than the feature space used by the existing study.

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Webometrics: evolution of social media presence of universities

et al. 2021

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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.

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An Effective and Scalable Framework for Authorship Attribution Query Processing

Cited by 14 publications

References 43 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

StyloThai:

Webometrics: evolution of social media presence of universities

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