Author Identification of English Tweets for Social Media Forensics

Authorship Identification (AI) is the process of determining the most likely author of a given text by analysing writing style characteristics and linguistic patterns. Identifying the author of online social network (OSN) text becomes a pressing issue nowadays as the increase of cyberbully cases amo...

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Main Author:	Nursyahirah, Tarmizi
Format:	Thesis
Language:	English English English
Published:	2023
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://ir.unimas.my/id/eprint/43079/7/Nursyahirah%20Tarmizi_dsva.pdf http://ir.unimas.my/id/eprint/43079/8/Thesis%20Master_Nursyahirah%20Binti%20Tarmizi%20-%2024%20pages.pdf http://ir.unimas.my/id/eprint/43079/11/Nursyahirah%20ft.pdf
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id	my-unimas-ir.43079
record_format	uketd_dc
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English English English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Nursyahirah, Tarmizi Author Identification of English Tweets for Social Media Forensics
description	Authorship Identification (AI) is the process of determining the most likely author of a given text by analysing writing style characteristics and linguistic patterns. Identifying the author of online social network (OSN) text becomes a pressing issue nowadays as the increase of cyberbully cases among the social media users. AI plays vital role in social media forensics (SMF) to unveil the true identity of the cyberbullying perpetrator from the OSN text. However, OSN text has been an open problem in AI as the limited length of the text and the usage of Internet jargon affecting the performance of AI system. In this research, AI task is conducted to facilitate the SMF activity by analysing the writing style of tweets from Twitter in identifying most plausible author for anonymized tweet. The writing style of the author or known as the stylometric features including character n-grams, word n-grams and Part-of-Speech (POS) n-grams are extracted from the text. These features are used widely in identifying the author of short text as they are language independent and tolerant of grammatical errors. The features are represented using different text representation models namely TF-IDF and Embedding model. The models are examined to compare which one could best represent the OSN text. For classification, machine learning and deep learning are used to evaluate the classification model by maintaining the optimum performance of AI system. The findings shown that Twitter native features are very useful in boosting the performance of AI system. Embedding-based model achieved better performance in representing n-grams with fix and distributed representation. The best result was achieved when CNN mix with embedding-based model with accuracy of 95.02% for English and 94% for KadazanDusun and both 95 % precision for both languages.
format	Thesis
qualification_level	Master's degree
author	Nursyahirah, Tarmizi
author_facet	Nursyahirah, Tarmizi
author_sort	Nursyahirah, Tarmizi
title	Author Identification of English Tweets for Social Media Forensics
title_short	Author Identification of English Tweets for Social Media Forensics
title_full	Author Identification of English Tweets for Social Media Forensics
title_fullStr	Author Identification of English Tweets for Social Media Forensics
title_full_unstemmed	Author Identification of English Tweets for Social Media Forensics
title_sort	author identification of english tweets for social media forensics
granting_institution	Universiti Malaysia Sarawak
granting_department	Information System
publishDate	2023
url	http://ir.unimas.my/id/eprint/43079/7/Nursyahirah%20Tarmizi_dsva.pdf http://ir.unimas.my/id/eprint/43079/8/Thesis%20Master_Nursyahirah%20Binti%20Tarmizi%20-%2024%20pages.pdf http://ir.unimas.my/id/eprint/43079/11/Nursyahirah%20ft.pdf
_version_	1794023043007250432
spelling	my-unimas-ir.430792024-02-20T04:57:23Z Author Identification of English Tweets for Social Media Forensics 2023-08-24 Nursyahirah, Tarmizi QA75 Electronic computers. Computer science Authorship Identification (AI) is the process of determining the most likely author of a given text by analysing writing style characteristics and linguistic patterns. Identifying the author of online social network (OSN) text becomes a pressing issue nowadays as the increase of cyberbully cases among the social media users. AI plays vital role in social media forensics (SMF) to unveil the true identity of the cyberbullying perpetrator from the OSN text. However, OSN text has been an open problem in AI as the limited length of the text and the usage of Internet jargon affecting the performance of AI system. In this research, AI task is conducted to facilitate the SMF activity by analysing the writing style of tweets from Twitter in identifying most plausible author for anonymized tweet. The writing style of the author or known as the stylometric features including character n-grams, word n-grams and Part-of-Speech (POS) n-grams are extracted from the text. These features are used widely in identifying the author of short text as they are language independent and tolerant of grammatical errors. The features are represented using different text representation models namely TF-IDF and Embedding model. The models are examined to compare which one could best represent the OSN text. For classification, machine learning and deep learning are used to evaluate the classification model by maintaining the optimum performance of AI system. The findings shown that Twitter native features are very useful in boosting the performance of AI system. Embedding-based model achieved better performance in representing n-grams with fix and distributed representation. The best result was achieved when CNN mix with embedding-based model with accuracy of 95.02% for English and 94% for KadazanDusun and both 95 % precision for both languages. Universiti Malaysia Sarawak 2023-08 Thesis http://ir.unimas.my/id/eprint/43079/ http://ir.unimas.my/id/eprint/43079/7/Nursyahirah%20Tarmizi_dsva.pdf text en staffonly http://ir.unimas.my/id/eprint/43079/8/Thesis%20Master_Nursyahirah%20Binti%20Tarmizi%20-%2024%20pages.pdf text en public http://ir.unimas.my/id/eprint/43079/11/Nursyahirah%20ft.pdf text en validuser masters Universiti Malaysia Sarawak Information System Abbasi, A., & Chen, H. (2005). Applying authorship analysis to extremist-group web forum messages. IEEE Intelligent Systems, 67-75. Abbasi, A., Javed, A. R., Iqbal, F., Jalil, Z., Gadekallu, T. R., & Kryvinska, N. (2022). Authorship identification using ensemble learning. Scientific Reports, 1-16. Abdullah Al-Ajlan, M., & Ykhlef, M. (2018). Deep learning algorithm for cyberbullying detection. 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Author Identification of English Tweets for Social Media Forensics

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