A Novel Machine Learning-based Framework for Detecting Religious Arabic Hatred Speech in Social Networks

Masadeh, Mahmoud; Davanager, Hanumanthappa Jayappa; Muaad, Abdullah Y.

doi:10.14569/ijacsa.2022.0130991

Cited by 4 publications

(4 citation statements)

References 36 publications

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“…Ujian demi ujian mereka hadapi dengan keimanan, Nafisya yang sudah ikhlas menerima takdirnya, dan Alif yang berusaha sabar menerima takdir bahwa Nafisya meninggalkannya. Sampai pada akhirnya Allah kembali mempersatukan mereka dengan membuka tabir yang selama ini menjadi penghalang untuk keduanya bersatu (Masadeh, 2022). Alif yang ternyata masih sangat mencintai Nafisya, dengan ikhlas menerima segala ketetapan Allah yang diberikan pada istrinya.…”

Section: Wa'alaikumussalaam Pelengkap Imanunclassified

Analisis Novel-Novel Religi Karya Ima Madani Sebagai Bentuk Dakwah Bil-Kitabah

Safitri,

Razzaq,

Hamandia

2024

pssh

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Penelitian ini berjudul, “Analisis Novel-novel Religi Karya Ima Madani Sebagai Bentuk Dakwah Bil-kitabah”, yang menjadi titik rumusan masalah dalam penelitian adalah, apa pesan dakwah dari novel religi karya Ima Madani dan bagaimana bentuk dakwah bil-kitabah yang dilakukan Ima Madani. Tujuan dari penelitian ialah untuk mengetahui pesan-pesan religi yang ada di dalam novel karya Ima Madani serta untuk mengetahui bentuk dakwah bil-kitabah yang dilakukan Ima Madani. Penelitian ini menggunakan metode penelitian kualitatif dengan pendekatan analisis kritis. Data yang dikumpulkan berupa dokumentasi dan wawancara. Hasil penelitian ini menjelaskan bahwa novel-novel religi karya Ima Madani semuanya mengandung pesan-pesan dakwah yang ditulis Ima Madani sebagai bentuk berdakwah melalui tulisan atau dikenal dengan dakwah bil-kitabah atau dakwah bil-qalam. Hal ini dilihat dari karya-karyanya yang berjudul, Assalamualaikum Calon Imam, Wa’alaikumussalam Pelengkap Iman, dan Shaf. Semuanya mengandung pesan-pesan dakwah. Adapun pesan-pesan dakwah yang terkandung terbagi menjadi 3, Akidah, Akhlak, Syariah. Assalamualaikum Calon Imam (ACI) mengandung pesan akidah yaitu beriman kepada Allah, pesan syariah ialah ibadah salat, dan akhlak yaitu ikhlas. Wa’alaikumussalam Pelengkap Iman (WPI) mengandung pesan akidah yaitu beriman kepada Allah, pesan akhlak ialah sabar, dan pesan syariah ialah ibadah salat. Shaf, mengandung pesan akidah yaitu beriman kepada Allah, pesan akhlak ialah ikhlas, dan pesan syariah yaitu ibadah salat, membaca Al-Qur’an, dan berdoa.

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Section: Wa'alaikumussalaam Pelengkap Imanunclassified

Analisis Novel-Novel Religi Karya Ima Madani Sebagai Bentuk Dakwah Bil-Kitabah

Safitri,

Razzaq,

Hamandia

2024

pssh

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“…F1-score is 1 when both precision and recall are 1. So, the F1-score is the harmonic mean of precision and recall and it is a more reasonable measure than accuracy [22].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Investigating the Impact of Preprocessing Techniques and Representation Models on Arabic Text Classification using Machine Learning

Masadeh,

et al. 2024

IJACSA

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Arabic Text Classification (ATC) is a crucial step for various Natural Language Processing (NLP) applications. It emerged as a response to the exponential growth of online content like social posts and review comments. In this study, preprocessing techniques and representation models are used to evaluate the effectiveness of ATC using Machine Learning (ML). Generally, the ATC operation depends on various factors, such as stemming in preprocessing, feature extraction and selection, and the nature of the dataset. To enhance the overall classification performance, preprocessing methodologies are primarily employed to transform each Arabic term into its root form and reduce the dimensionality of representation. In the representation of Arabic text, feature extraction and selection processes are imperative, as they significantly enhance the performance of ATC. This study implements the chosen classifiers using various feature selection algorithms. The comprehensive assessment of classification outcomes is conducted by comparing various classifiers, including Multinomial Naive Bayes (MNB), Bernoulli Naive Bayes (BNB), Stochastic Gradient Descent (SGD), Support Vector Classifier (SVC), Logistic Regression (LR), and linear Support Vector Classifier (LSVC). These ML classifiers are assessed utilizing short and long Arabic text benchmark datasets called BBC Arabic corpus and the COVID-19 dataset. The assessment findings indicate that the efficacy of classification is significantly influenced by the preprocessing methods, representation model, classification algorithm, and the datasets' characteristics. In most cases, the SGDC and LSVC have consistently surpassed other classifiers for the datasets under consideration when significant features are chosen.

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“…The caveat is the misuse and provocation of hate and offence. The trend in cyberbullying and online harassment is on the rise due to the liberty of social media platforms ( Masadeh, Davanager & Muaad, 2022 ). The offence, harassment and cyberbullying are grave concerns on social networking platforms.…”

Section: Introductionmentioning

confidence: 99%

Detection of offensive terms in resource-poor language using machine learning algorithms

Raza,

Mahoto,

Hamdi

et al. 2023

PeerJ Computer Science

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The use of offensive terms in user-generated content on different social media platforms is one of the major concerns for these platforms. The offensive terms have a negative impact on individuals, which may lead towards the degradation of societal and civilized manners. The immense amount of content generated at a higher speed makes it humanly impossible to categorise and detect offensive terms. Besides, it is an open challenge for natural language processing (NLP) to detect such terminologies automatically. Substantial efforts are made for high-resource languages such as English. However, it becomes more challenging when dealing with resource-poor languages such as Urdu. Because of the lack of standard datasets and pre-processing tools for automatic offensive terms detection. This paper introduces a combinatorial pre-processing approach in developing a classification model for cross-platform (Twitter and YouTube) use. The approach uses datasets from two different platforms (Twitter and YouTube) the training and testing the model, which is trained to apply decision tree, random forest and naive Bayes algorithms. The proposed combinatorial pre-processing approach is applied to check how machine learning models behave with different combinations of standard pre-processing techniques for low-resource language in the cross-platform setting. The experimental results represent the effectiveness of the machine learning model over different subsets of traditional pre-processing approaches in building a classification model for automatic offensive terms detection for a low resource language, i.e., Urdu, in the cross-platform scenario. In the experiments, when dataset D1 is used for training and D2 is applied for testing, the pre-processing approach named Stopword removal produced better results with an accuracy of 83.27%. Whilst, in this case, when dataset D2 is used for training and D1 is applied for testing, stopword removal and punctuation removal were observed as a better preprocessing approach with an accuracy of 74.54%. The combinatorial approach proposed in this paper outperformed the benchmark for the considered datasets using classical as well as ensemble machine learning with an accuracy of 82.9% and 97.2% for dataset D1 and D2, respectively.

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A Novel Machine Learning-based Framework for Detecting Religious Arabic Hatred Speech in Social Networks

Cited by 4 publications

References 36 publications

Analisis Novel-Novel Religi Karya Ima Madani Sebagai Bentuk Dakwah Bil-Kitabah

Analisis Novel-Novel Religi Karya Ima Madani Sebagai Bentuk Dakwah Bil-Kitabah

Investigating the Impact of Preprocessing Techniques and Representation Models on Arabic Text Classification using Machine Learning

Detection of offensive terms in resource-poor language using machine learning algorithms

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