Exploring the Anonymity of Social Media Users Using Micro-messages

Aykent, Sarp; Seals, Cheryl

doi:10.1007/978-3-031-35915-6_2

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…Authorship identification determines the author of a particular text or a document [1]; that can be done by analyzing various writing characteristics [2], such as vocabulary [3], grammar [4], structure and punctuation [5], [3]. This function is critical in various fields, including forensic linguistics [6], [7], [8], information retrieval [9], [10], [11], [12], plagiarism detection, and literary studies [13]. Analysts can use authorship identification techniques to thoroughly study and assess textual communication to uncover the author's true identity [14].…”

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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