Authorship Identification using Generalized Features and Analysis of Computational Method

Nirkhi, Smita; Dharaskar, R. V.; Thakare, V. M.

doi:10.14738/tmlai.32.1064

Cited by 6 publications

(7 citation statements)

References 7 publications

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“…The dataset is are used C50 corpus(contain 50 authors with 50 documents each) and Enron corpus (contains 619,446 messages for each 158 users). The best accurcy is 93.3% for 5 author of Enron corpus and 100% for 7 or 25 author [20].…”

Section: Related Workmentioning

confidence: 97%

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User identification based on short text using recurrent deep learning

Hallawi

Kadhim

Abdullah

et al. 2023

IJ-AI

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Technological development is a revolutionary process by this time, it is mainly depending on electronic applications in our daily routines like (business management, banking, financial transfers, health, and other essential traits of life). Identification or approving identity is one of the complicated issues within online electronic applications. Person’s writing style can be employed as an identifying biological characteristic in order to recognize the identity. This paper presents a new way for identifying a person in a social media group using comments and based on the Deep Neural Network. The text samples are short text comments collected from Telegram group in Arabic language (Iraqi dialect). The proposed model is able to extract the person's writing style features in group comments based on pre-saved dataset. The analysis of this information and features forms the identification decision. This model exhibits a range of prolific and favorable results, the accuracy that comes with the proposed system reach to 92.88% (+/- 0.16%).

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Section: Related Workmentioning

confidence: 97%

“…Nirkhi et al identify the authors of short message by using SVM classifier and word Uni-gram [20]. The dataset is are used C50 corpus(contain 50 authors with 50 documents each) and Enron corpus (contains 619,446 messages for each 158 users).…”

Section: Related Workmentioning

confidence: 99%

User identification based on short text using recurrent deep learning

Hallawi

Kadhim

Abdullah

et al. 2023

IJ-AI

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“…Pandian et al [11] trained a decision tree (J48 learning algorithm) using text-based features for identifying different authors of poems. Nirkhi et al [12] worked with word and character unigram features and a support vector machine (SVM) classifier on the C50 dataset and achieved 88% accuracy. López-Monroy et al [13] also used an SVM along with the bag-of-terms model, obtaining 80.80% accuracy on the C50 dataset for the author identification task.…”

Section: Related Workmentioning

confidence: 99%

Author Identification from Literary Articles with Visual Features: A Case Study with Bangla Documents

et al. 2022

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Author identification is an important aspect of literary analysis, studied in natural language processing (NLP). It aids identify the most probable author of articles, news texts or social media comments and tweets, for example. It can be applied to other domains such as criminal and civil cases, cybersecurity, forensics, identification of plagiarizer, and many more. An automated system in this context can thus be very beneficial for society. In this paper, we propose a convolutional neural network (CNN)-based author identification system from literary articles. This system uses visual features along with a five-layer convolutional neural network for the identification of authors. The prime motivation behind this approach was the feasibility to identify distinct writing styles through a visualization of the writing patterns. Experiments were performed on 1200 articles from 50 authors achieving a maximum accuracy of 93.58%. Furthermore, to see how the system performed on different volumes of data, the experiments were performed on partitions of the dataset. The system outperformed standard handcrafted feature-based techniques as well as established works on publicly available datasets.

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“…Recently, a number of studies have been carried out on cross-domain authorship identification [17] (where the texts of known and unknown authorship belong to different domains) and style change detection (where single-author and multi-author texts are to be distinguished), featuring several methods involving the use of n-grams [18] and deep learning [19,20,21]. Nirkhi et al [22] investigated the effect of increasing the number of authors on an SVM-based authorship identification system's performance.…”

Section: Previous Researchmentioning

confidence: 99%

Can You Fool AI by Doing a 180? $\unicode{x2013}$ A Case Study on Authorship Analysis of Texts by Arata Osada

Nieuwazny,

Nowakowski,

Ptaszynski

et al. 2022

Preprint

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This paper is our attempt at answering a twofold question covering the areas of ethics and authorship analysis solutions. Firstly, since the methods used for performing authorship analysis imply that an author can be recognized by the content he or she creates, we were interested in finding out whether it would be possible for an author identification system to correctly attribute works to authors if in the course of years they have undergone a major psychological transition. Secondly -and from the point of view of the evolution of an author's ethical values -we checked what it would mean if the authorship attribution system encounters difficulties in detecting single authorship. We set out to answer those questions through performing a binary authorship analysis task using a text classifier based on a pre-trained transformer model and a baseline method relying on conventional similarity metrics. For the test set, we chose several works of Arata Osada, a Japanese educator and specialist in the history of education, with half of them being books written before the Second World War and another half in the 1950s, in between which the author underwent a transformation in terms of political opinions. As a result, we were able to confirm that in the case of texts authored by Arata Osada in a time span of more than 10 years, while the classification accuracy drops by a large margin and is substantially lower than for texts by other non-fiction writers, confidence scores of the predictions remain at a similar level as in the case of a shorter time span, indicating that the classifier was in many instances tricked into deciding that texts written by Arata Osada over a time span of multiple years were actually written by two different people, which in turn leads us to believe that such a change can affect authorship analysis, and that historical events have great impact on a person's ethical outlook as expressed in their writings.

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Authorship Identification using Generalized Features and Analysis of Computational Method

Cited by 6 publications

References 7 publications

User identification based on short text using recurrent deep learning

User identification based on short text using recurrent deep learning

Author Identification from Literary Articles with Visual Features: A Case Study with Bangla Documents

Can You Fool AI by Doing a 180? $\unicode{x2013}$ A Case Study on Authorship Analysis of Texts by Arata Osada

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