Ensemble Methods for Instance-Based Arabic Language Authorship Attribution

Al-Sarem, Mohammed; Saeed, Faisal; Alsaeedi, Abdullah; Boulila, ‪Wadii; Al-Hadhrami, Tawfik

doi:10.1109/access.2020.2964952

Cited by 37 publications

(27 citation statements)

References 67 publications

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“…Due to the possibility of a single classifier making a mistake, the ensemble will misclassify only, if more than half of the classifiers are incorrect. So, an ensemble’s performance is more effective than a single classifier [ 39 ]. The suggested machine learning level classifier in our work makes use of the ensemble methodology.…”

Section: Proposed Methodsmentioning

confidence: 99%

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A two level learning model for authorship authentication

2021

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Nowadays, forensic authorship authentication plays a vital role in identifying the number of unknown authors as a result of the world’s rapidly rising internet use. This paper presents two-level learning techniques for authorship authentication. The learning technique is supplied with linguistic knowledge, statistical features, and vocabulary features to enhance its efficiency instead of learning only. The linguistic knowledge is represented through lexical analysis features such as part of speech. In this study, a two-level classifier has been presented to capture the best predictive performance for identifying authorship. The first classifier is based on vocabulary features that detect the frequency with which each author uses certain words. This classifier’s results are fed to the second one which is based on a learning technique. It depends on lexical, statistical and linguistic features. All of the three sets of features describe the author’s writing styles in numerical forms. Through this work, many new features are proposed for identifying the author’s writing style. Although, the proposed new methodology is tested for Arabic writings, it is general and can be applied to any language. According to the used machine learning models, the experiment carried out shows that the trained two-level classifier achieves an accuracy ranging from 94% to 96.16%.

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Section: Proposed Methodsmentioning

confidence: 99%

“…The accuracy is used to evaluate the results. This evaluation is commonly used in the literature [ 37 , 39 , 40 ], and is described as: …”

Section: 1 Datasetmentioning

confidence: 99%

A two level learning model for authorship authentication

2021

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“…Interestingly enough, the best classifier was Random Forest, a traditional machine learning classifier working with a set of manually engineered text features as opposed to deep learning classifiers that used a sequence of text tokens as an input. Character n-grams were used with a Convolutional Neural Network (CNN) by [31] for authorship attribution of short texts. They hypothesized that a CNN will be able to capture the stylistic features of a text through the use of successive convolution layers.…”

Section: B Attribution Methodsmentioning

confidence: 99%

Towards Authorship Attribution in Arabic Short-Microblog Text

Jambi

Khan²,

Siddiqui

et al. 2021

IEEE Access

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Authorship attribution is the study to identify individuals by their writing styles without knowing their actual identities. This is a challenging task in natural language processing. Most work on authorship attribution focused on English, whereas, the problem is understudied in Arabic language. However, due to the complex and distinct morphological nature of the Arabic language, techniques developed for English are not directly applicable to Arabic. This paper explored the possibility of using state-of-the-art classifiers, Support Vector Machines (SVM), K-Nearest Neighbours(KNN) and Random Forest, to predict authorship in Arabic short-microblog text. We employed three commonly used linguistic features, character-, lexical-and syntactic-based, in an incremental manner to predict the accuracy of the selected classifiers. The results elucidate that a systematic combination of linguistic features improves authorship classification. However, an inverse correlation was observed in authorship classification accuracy and the number of authors. Overall, SVM and Random Forest classifier are comparable and attained 65% accuracy, whereas KNN hardly attained 35% accuracy. In addition, lexical features offer more discriminatory power as compared to the character and syntactic features.

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“…For many text classification systems, pre-processing is considered as an essential step to improve the quality of data as well as the efficiency and accuracy of ML models [22,23]. The common pre-processing steps include text cleansing, tokenization, removing stop words, stemming, and normalization.…”

Section: Pre-processingmentioning

confidence: 99%

An Improved Multiple Features and Machine Learning-Based Approach for Detecting Clickbait News on Social Networks

et al. 2021

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The widespread usage of social media has led to the increasing popularity of online advertisements, which have been accompanied by a disturbing spread of clickbait headlines. Clickbait dissatisfies users because the article content does not match their expectation. Detecting clickbait posts in online social networks is an important task to fight this issue. Clickbait posts use phrases that are mainly posted to attract a user’s attention in order to click onto a specific fake link/website. That means clickbait headlines utilize misleading titles, which could carry hidden important information from the target website. It is very difficult to recognize these clickbait headlines manually. Therefore, there is a need for an intelligent method to detect clickbait and fake advertisements on social networks. Several machine learning methods have been applied for this detection purpose. However, the obtained performance (accuracy) only reached 87% and still needs to be improved. In addition, most of the existing studies were conducted on English headlines and contents. Few studies focused specifically on detecting clickbait headlines in Arabic. Therefore, this study constructed the first Arabic clickbait headline news dataset and presents an improved multiple feature-based approach for detecting clickbait news on social networks in Arabic language. The proposed approach includes three main phases: data collection, data preparation, and machine learning model training and testing phases. The collected dataset included 54,893 Arabic news items from Twitter (after pre-processing). Among these news items, 23,981 were clickbait news (43.69%) and 30,912 were legitimate news (56.31%). This dataset was pre-processed and then the most important features were selected using the ANOVA F-test. Several machine learning (ML) methods were then applied with hyper-parameter tuning methods to ensure finding the optimal settings. Finally, the ML models were evaluated, and the overall performance is reported in this paper. The experimental results show that the Support Vector Machine (SVM) with the top 10% of ANOVA F-test features (user-based features (UFs) and content-based features (CFs)) obtained the best performance and achieved 92.16% of detection accuracy.

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Ensemble Methods for Instance-Based Arabic Language Authorship Attribution

Cited by 37 publications

References 67 publications

A two level learning model for authorship authentication

A two level learning model for authorship authentication

Towards Authorship Attribution in Arabic Short-Microblog Text

An Improved Multiple Features and Machine Learning-Based Approach for Detecting Clickbait News on Social Networks

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