Introduction: Theory and Practice in Forensic Linguistics

Guillén-Nieto, Victoria; Stein, Dieter

doi:10.1007/978-3-030-84330-4_1

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Advanced reliability in forensic linguistics helps the process of analysis while increasing the resources of understanding of the situation it is describing. Describing a forensic situation requires sustainable management techniques that help the process of communication while making the authorities gain more fundamental resources of information with proper accuracy [3]. Sustainable measures of management in forensic linguistics helps the authorities to depict the process of communication with suitable measures that makes the analytical results more accurate as it has been identified here.…”

Section: Rationale and Research Problemmentioning

confidence: 99%

Forensic Linguistics: A Concise Depiction of Key Elements

Reyes¹,

Hasyim²

2023

TTLL

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This study presented the approach of data authentication of forensic analysis while focusing on the various aspects of forensic linguistics that require sustainable key elements in order to make it further reliable in contemporary times. The objectives along with the research questions have been depicted in the study while a brief critical review of its literary elements has been depicted. Methods of the process of evaluation of the study have been identified while analysing the resources of data collection for the research. The resources of secondary qualitative data have been taken for the development of objectives of this study while making the process of analysis in the forensic background more viable and understandable. Keyword : forensic linguistics, key elements of linguistics, linguistics in forensic analysis.

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Section: Rationale and Research Problemmentioning

confidence: 99%

Forensic Linguistics: A Concise Depiction of Key Elements

Reyes¹,

Hasyim²

2023

TTLL

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“…The ability to correctly identify the author of a particular text or document may be useful in various sectors, including law enforcement [15], academics [16], and literary study [17]. The author's writing style, spoken language, the subject matter and genre of the document, the content and tone of voice, structural aspects, metadata, and linguistic analysis are all key elements in authorship identification [18]. Each of these elements can provide vital clues to the author's identity and aid in identifying them in a particular text or document.…”

Section: Introductionmentioning

confidence: 99%

Active Learning for News Article’s Authorship Identification

Abbas,

Alsubai,

Sampedro

et al. 2023

IEEE Access

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Over time, the amount of textual data has increased drastically, especially due to the publication of articles. As a consequence, there has been a rise in anonymous content. Research is being conducted to determine alternative methods for identifying unknown text authors. To this end, a system has to be developed to accurately determine the author of unknown texts, given a group of writing samples. Active Learning is utilized in this study because it iteratively selects the most informative samples to include in the training set, which enables a more precise and accurate authorship identification approach with fewer examples. Makes it useful for analyzing the rising amount of anonymous content and identifying unknown text authors. This study proposes a novel approach that utilizes active learning (AL) based machine and deep learning models, namely Logistic Regression (AL-LR), Random Forest (AL-RF), XGboost (AL-XGB), and Multilayer Perceptron (AL-MLP) for authorship identification. The proposed approach extracts valuable characteristics of the writer using the Term Frequency-Inverse Document Frequency (TF-IDF). This study's selected comprehensive dataset, "All the news," is divided into three subsets: Article 1, Article 2, and Article 3. We have restricted the dataset's scope and selected the top 50 authors for our experimentation. The experimental outcomes reveal that the proposed AL-XGB model achieves superior performance on Article 1 of the "All the news" dataset. Further, the AL-LR model permed well on Article 2, and the AL-MLP performed well on Article 3. The results suggest using the proposed approach for authorship identification.

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