Browsing by Author "Canbay, Pelin"

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Authorship Modelling Approach for Authorship Verification on the Turkish Texts
(Ieee, 2018) Canbay, Pelin; Sever, Hayri; Akcapinar Sezer, Ebru; Sever, Hayri; 11916; Bilgisayar Mühendisliği
Authorship attribution which aims to extract information about an author by analyzing the text of the author is a challenging field that has been studied for years. This study becomes even more difficult when there is limited data on this field. The need for this study carried out under the name of Authorship Verification is increasing day by day with the increase of anonymous authors in the electronic environments. In this study, a model-based solution approach is presented for the authorship verification problem. With the presented approach, it was determined what should be the success interval to be considered in the authorship verification problem.
Citation - WoS: 2
Citation - Scopus: 2
Binary background model with geometric mean for author-independent authorship verification
(Sage Publications Ltd, 2023) Canbay, Pelin; Sever, Hayri; Sezer, Ebru A.; Sever, Hayri; 11916; Bilgisayar Mühendisliği
Authorship verification (AV) is one of the main problems of authorship analysis and digital text forensics. The classical AV problem is to decide whether or not a particular author wrote the document in question. However, if there is one and relatively short document as the author's known document, the verification problem becomes more difficult than the classical AV and needs a generalised solution. Regarding to decide AV of the given two unlabeled documents (2D-AV), we proposed a system that provides an author-independent solution with the help of a Binary Background Model (BBM). The BBM is a supervised model that provides an informative background to distinguish document pairs written by the same or different authors. To evaluate the document pairs in one representation, we also proposed a new, simple and efficient document combination method based on the geometric mean of the stylometric features. We tested the performance of the proposed system for both author-dependent and author-independent AV cases. In addition, we introduced a new, well-defined, manually labelled Turkish blog corpus to be used in subsequent studies about authorship analysis. Using a publicly available English blog corpus for generating the BBM, the proposed system demonstrated an accuracy of over 90% from both trained and unseen authors' test sets. Furthermore, the proposed combination method and the system using the BBM with the English blog corpus were also evaluated with other genres, which were used in the international PAN AV competitions, and achieved promising results.
Deep Combination of Stylometry Features in Forensic Authorship Analysis
(2020) Sever, Hayri; Sezer, Ebru Akcapinar; Canbay, Pelin; Sever, Hayri; 11916; Bilgisayar Mühendisliği
Authorship Analysis (AA) in forensic is a process aim to extract information about an author from his/her writings.Forensic AA is needed for detection characteristics of anonymous authors to make better the security of digital media userswho are exposed to disturbing entries such as threats or harassment emails. To analyze whether two anonymous short textswere written by the same author, we propose a combination of stylometry features from different categories in differentprogress. In the majority of the previous AA studies, the stylometric features from different categories are concatenated in apreprocess. In these studies, during the learning process, no category-specific operations are performed; all categories used areevaluated equally. On the other hand, the proposed approach has a separate learning process for each feature category due totheir qualitative and quantitative characteristics and combines these processes at the decision phase by using a Combination ofDeep Neural Networks (C-DNN). To evaluate the Authorship Verification (AV) performance of the proposed approach, wedesigned and implemented a problem-specific Deep Neural Network (DNN) for each stylometry category we used.Experiments were conducted on two English public datasets. The results show that the proposed approach significantlyimproves the generalization ability and robustness of the solutions, and also have better accuracy than the single DNNs.
Citation - WoS: 0
Detection of Stylometric Writeprint from the Turkish Texts
(Ieee, 2020) Canbay, Pelin; Sever, Hayri; Sezer, Ebru Akcapinar; Sever, Hayri; 11916; Bilgisayar Mühendisliği
Authorship attribution studies aim to extract information about the author by analyzing the data in the text form. With the increase of anonymous authors in digital environments, the need for these works is increasing day by day. Although there exists lots of studies focuse on stylometric writeprint detection in different languages using different attributes, there is no standard feature set and detection algorithm to be evaluated in these studies. Giving priority to Turkish texts, in this study, which features are more distinctive for determining stylistic writeprint of text, and which methods will contribute to increase the success to be achieved are shown with experimental studies.