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|Title:||Authorship Attribution of Modern Standard Arabic Short Texts||Authors:||Abuhammad, Yara
|Keywords:||Arabic authorship attribution;Support vector classification;Machine learning - Technique;Social media;Twitter;Authorship attribution datasets;Text processing (Computer science), Arabic;Natural language processing (Computer science) - Arab countries;Tweets, Arabic;Computational linguistics, Arabic||Issue Date:||22-Aug-2021||Publisher:||Association for Computing Machinery-ACM||Source:||Abuhammad Y., Aldabe Y., Ayyad N. and Yahya, A. "Authorship Attribution of Modern Standard Arabic Short Texts". Proceedings of the Seventh International Conference on Arab Women in Computing ArabWIC21. Sharjah. United Arab Emirates. 25 August 2021.ACM proceedings.||Conference:||ArabWIC 2021: ArabWIC 7th International Conference on Arab Women in Computing.||Abstract:||Text data, including short texts, constitute a major share of web content. The availability of this data to billions of users triggers frequent plagiarism attacks. Authorship Attribution (AA) seeks to identify the most probable author of a given text based on similarity to the writing style of potential authors. In this paper, we approach AA as a writing style profile generation process, where we group text instances for each author into a single profile. We use Twitter as the source for our short Modern Standard Arabic (MSA) texts. Numerous experiments with various training approaches, tools and features allowed us to settle on a text representation method that relies on text concatenation of Arabic tweets to form chunks, which are then duplicated to reach a precalculated length. These chunks are used to train machine learning models for our 45 author profiles. This allowed us to achieve accuracies up to 99%, which compares favorably with the best results reported in the literature.||Description:||ArabWIC 2021: ArabWIC 7th International Conference on Arab Women in Computing In Conjunction with 2nd Forum for Women in Research As Part of the ArabWIC 2021 Conference Series August 25, 2021||URI:||http://hdl.handle.net/20.500.11889/6787|
|Appears in Collections:||Fulltext Publications|
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