Arabic News Research Articles

Correction: Framing the shooting of Al Jazeera journalist Shireen Abu Akleh in English and Arabic news headlines: a critical discourse study

Correction: Framing the shooting of Al Jazeera journalist Shireen Abu Akleh in English and Arabic news headlines: a critical discourse study

Ensemble based high performance deep learning models for fake news detection

Social media has emerged as a dominant platform where individuals freely share opinions and communicate globally. Its role in disseminating news worldwide is significant due to its easy accessibility. However, the increase in the use of these platforms presents severe risks for potentially misleading people. Our research aims to investigate different techniques within machine learning, deep learning, and ensemble learning frameworks in Arabic fake news detection. We integrated FastText word embeddings with various machine learning and deep learning methods. We then leveraged advanced transformer-based models, including BERT, XLNet, and RoBERTa, optimizing their performance through careful hyperparameter tuning. The research methodology involves utilizing two Arabic news article datasets, AFND and ARABICFAKETWEETS datasets, categorized into fake and real subsets and applying comprehensive preprocessing techniques to the text data. Four hybrid deep learning models are presented: CNN-LSTM, RNN-CNN, RNN-LSTM, and Bi-GRU-Bi-LSTM. The Bi-GRU-Bi-LSTM model demonstrated superior performance regarding the F1 score, accuracy, and loss metrics. The precision, recall, F1 score, and accuracy of the hybrid Bi-GRU-Bi-LSTM model on the AFND Dataset are 0.97, 0.97, 0.98, and 0.98, and on the ARABICFAKETWEETS dataset are 0.98, 0.98, 0.99, and 0.99 respectively. The study’s primary conclusion is that when spotting fake news in Arabic, the Bi-GRU-Bi-LSTM model outperforms other models by a significant margin. It significantly aids the global fight against false information by setting the stage for future research to expand fake news detection to multiple languages.

How user language affects conflict fatality estimates in ChatGPT

OpenAI’s ChatGPT language model has gained popularity as a powerful tool for problem-solving and information retrieval. However, concerns arise about the reproduction of biases present in the language-specific training data. In this study, we address this issue in the context of the Israeli–Palestinian and Turkish–Kurdish conflicts. Using GPT-3.5, we employed an automated query procedure to inquire about casualties in specific airstrikes, in both Hebrew and Arabic for the former conflict and Turkish and Kurdish for the latter. Our analysis reveals that GPT-3.5 provides 34 ± 11% lower fatality estimates when queried in the language of the attacker than in the language of the targeted group. Evasive answers denying the existence of such attacks further increase the discrepancy. A simplified analysis on the current GPT-4 model shows the same trends. To explain the origin of the bias, we conducted a systematic media content analysis of Arabic news sources. The media analysis suggests that the large-language model fails to link specific attacks to the corresponding fatality numbers reported in the Arabic news. Due to its reliance on co-occurring words, the large-language model may provide death tolls from different attacks with greater news impact or cumulative death counts that are prevalent in the training data. Given that large-language models may shape information dissemination in the future, the language bias identified in our study has the potential to amplify existing biases along linguistic dyads and contribute to information bubbles.

Accessibility barriers in arabic news websites for visually impaired users: a mixed-method evaluation approach

Accessibility barriers in arabic news websites for visually impaired users: a mixed-method evaluation approach

Computational linguistics and natural language processing techniques for semantic field extraction in Arabic online news

The research aimed to extract semantic fields from Arabic online news and advance Natural Language Processing (NLP) applications in understanding and managing news information effectively. It provides a comprehensive approach to processing and analyzing large volumes of Arabic news data by integrating semantic field analysis, NLP, and computational linguistics. Using quantitative methods, Arabic news articles were collected and processed with Python, a popular programming language in data analysis, and applied various NLP techniques and machine learning models to accurately extract semantic fields. The primary objective was to evaluate the effectiveness of different classification models in categorizing Arabic news and to identify the most suitable model for semantic field extraction. The research evaluated five classification models: Naive Bayes, Support Vector Machine (SVM), Logistic Regression, Random Forest, and Gradient Boosting. Among these, SVM achieves the highest overall accuracy of 90%. Specifically, SVM demonstrated exceptional performance in categorizing sports-related news, with a 99% probability and an F1-Score of 98%. However, it faced challenges in categorizing health and science news, achieving a lower F1-Score of 79%. Overall, the study demonstrated the effectiveness of computational methods, particularly SVM, in classifying Arabic news and extracting semantic fields, thereby advancing NLP and computational linguistics. The findings highlighted the potential of SVM for accurate news analysis and the need for further enhancement of NLP techniques to address multilingual and domain-specific challenges.

Framing the shooting of Al Jazeera journalist Shireen Abu Akleh in English and Arabic news headlines: a critical discourse study

The study aims to investigate the ideological manipulation of language within news media headlines regarding the killing of Palestinian journalist Shireen Abu Akleh. This study employs Van Dijk’s socio-cognitive model of Critical Discourse Analysis to conduct a comparative qualitative analysis of 100 Arabic and 100 English news headlines, exploring linguistic features at both microstructure and macrostructure levels. The analysis delves into identity construction for the victim and perpetrator, revealing the discursive linguistic manifestations of underlying ideologies in two distinct linguistic and cultural contexts. The microstructure analysis reveals the preference of Arabic headlines for active voice structure, rhetorical devices of numeration, emphatic language, and metaphors. On the other hand, English headlines preferred the passive voice structure and the rhetorical device of litotes. The macrostructure analysis unravels the cognitive interplay between news media outlets and their target audience, demonstrating how selective headlines significantly impact public opinion. It shapes their comprehension and interpretation, particularly within the broader context of the Israeli-Palestinian conflict. Ultimately, this research highlights the dynamic interplay between language, ideology, and power in news media, offering key recommendations for understanding their comparative impacts.

Pre-Trained Language Model Ensemble for Arabic Fake News Detection

Fake news detection (FND) remains a challenge due to its vast and varied sources, especially on social media platforms. While numerous attempts have been made by academia and the industry to develop fake news detection systems, research on Arabic content remains limited. This study investigates transformer-based language models for Arabic FND. While transformer-based models have shown promising performance in various natural language processing tasks, they often struggle with tasks involving complex linguistic patterns and cultural contexts, resulting in unreliable performance and misclassification problems. To overcome these challenges, we investigated an ensemble of transformer-based models. We experimented with five Arabic transformer models: AraBERT, MARBERT, AraELECTRA, AraGPT2, and ARBERT. Various ensemble approaches, including a weighted-average ensemble, hard voting, and soft voting, were evaluated to determine the most effective techniques for boosting learning models and improving prediction accuracies. The results of this study demonstrate the effectiveness of ensemble models in significantly boosting the baseline model performance. An important finding is that ensemble models achieved excellent performance on the Arabic Multisource Fake News Detection (AMFND) dataset, reaching an F1 score of 94% using weighted averages. Moreover, changing the number of models in the ensemble has a slight effect on the performance. These key findings contribute to the advancement of fake news detection in Arabic, offering valuable insights for both academia and the industry

Amina: an Arabic multi-purpose integral news articles dataset

AbstractElectronic newspapers are one of the most common sources of Modern Standard Arabic. Existing datasets of Arabic news articles typically provide a title, body, and single label. Ignoring important features, like the article author, image, tags, and publication date, can degrade the efficacy of classification models. In this paper, we propose the Arabic multi-purpose integral news articles (AMINA) dataset. AMINA is a large-scale Arabic news corpus with over 1,850,000 articles collected from 9 Arabic newspapers from different countries. It includes all the article features: title, tags, publication date and time, location, author, article image and its caption, and the number of visits. To test the efficacy of the proposed dataset, three tasks were developed and validated: article textual content (classification and generation) and article image classification. For content classification, we experimented the performance of several state-of-the-art Arabic NLP models including AraBERT and CAMeL-BERT, etc. For content generation, the reformer architecture is adopted as a character text generation model. For image classification applied on Al-Sharq and Youm7 news portals, we have compared the performance of 10 pre-trained models including ConvNeXt, MaxViT, ResNet18, etc. The overall study verifies the significance and contribution of our newly introduced Arabic articles dataset. The AMINA dataset has been released at https://huggingface.co/datasets/MohamedZayton/AMINA.

Approach for Detecting Arabic Fake News using Deep Learning

Approach for Detecting Arabic Fake News using Deep Learning

Rima Aliterasi dan Asonansi sebagai Sumber Ritma Linguistik Surah Fatihah yang Mensinkroni Gelombang Otak

Sound rhythm is one of the factors that make the sound of the Holy Quran rhythmic and synchronous that change the rhythm of human brain waves. However, less studies have been found, and previous studies have focused more on the end rhyme system (end faselah) of the verses of the Qur'an. Thus, we conducted an acoustic phonetic study on the rhyming system in the verses of the Qur'an which is the source of the rhythmic sound in the verses of the Qur'an which contributes to the aesthetics of the sound. Acoustic phonetic analysis was performed on sound waves from Surah al-Fatihah using PRAAT software by comparing its intensity with Arabic News. As a result, we found the existence of a rhyme system of alliteration and assonance in the verses of this surah, that is, in the first, third, fifth and seventh verses which are highlighted by the contours of intensity and time duration of letters, syllables and words that show the greatness of God, giving birth to a feeling of closeness to God in addition to giving birth to the effect of persuasion which is the basis of spiritual therapy. In addition, the end rhyme system found in the Fatihah Chapter is significant (p<0.05) compared to the Arabic News. In conclusion, the Fatihah Chapter also has a rhyme system in its verses, not only at the end of the verse, suggesting that the brain that listens to the sound of the Qur'an is able to synchronize and form rhythmic and harmonious waves that influence mental and physical well-being.

Kesan Modul Terapi Realiti (MTR) Terhadap Ciri-ciri dan Kemurungan Mangsa Buli dari Perspektif Islam

Sound rhythm is one of the factors that make the sound of the Holy Quran rhythmic and synchronous that change the rhythm of human brain waves. However, less studies have been found, and previous studies have focused more on the end rhyme system (end faselah) of the verses of the Qur'an. Thus, we conducted an acoustic phonetic study on the rhyming system in the verses of the Qur'an which is the source of the rhythmic sound in the verses of the Qur'an which contributes to the aesthetics of the sound. Acoustic phonetic analysis was performed on sound waves from Surah al-Fatihah using PRAAT software by comparing its intensity with Arabic News. As a result, we found the existence of a rhyme system of alliteration and assonance in the verses of this surah, that is, in the first, third, fifth and seventh verses which are highlighted by the contours of intensity and time duration of letters, syllables and words that show the greatness of God, giving birth to a feeling of closeness to God in addition to giving birth to the effect of persuasion which is the basis of spiritual therapy. In addition, the end rhyme system found in the Fatihah Chapter is significant (p<0.05) compared to the Arabic News. In conclusion, the Fatihah Chapter also has a rhyme system in its verses, not only at the end of the verse, suggesting that the brain that listens to the sound of the Qur'an is able to synchronize and form rhythmic and harmonious waves that influence mental and physical well-being.

Transediting the terms used for describing the US dollar in Arabic and English news websites: a triangulational study of transediting strategies adopted in four channels

News translation, which is often known as news transediting, which is concerned with the translation of materials for print and/or online mass media, has long stimulated interest within the field of translation studies. The present paper explores the strategies of transediting the terms used for describing the US dollar in the websites of four news channels, namely: Alarabiya, Aljazeera, BBC and CNN between Arabic and English from the first of July 2022 to the thirty-first of December 2022. The paper conducts triangulational analysis to identify the type and frequency of the transediting strategies used in each channel, present how certain examples of each strategy are adopted and demonstrate the similarities and differences in the type and frequency of the transediting strategies employed between the channels. The paper argues that the choice of the transediting strategy is based on the type of audience, context, the channel’s policy and the type of information intended for delivery. This paper offers a baseline for exploring the strategies of transediting the terms used for describing the US dollar in both Arabic and English news websites of the aforementioned channels, which may have implications for exploring other transediting strategies used in other news websites of different channels broadcast in different languages.

A Deep Learning-based Classification Model for Arabic News Tweets Using Bidirectional Long Short-Term Memory Networks

This research develops a classification model for Arabic news tweets using Bidirectional Long Short-Term Memory networks (BiLSTM). Tweets about Arabic news were gathered between August 2016 and August 2020 and divided into five categories. Custom Python scripts, Twitter API and the GetOldTweets3 Python library were used to collect the data. BiLSTM was used to train and test the model. The results indicated an average accuracy, precision, recall, and f1-score of 0.88, 0.92, 0.88, and 0.89, respectively. The results could have practical implications for Arabic machine learning and NLP tasks in research and practice.

Predictive Modeling for Arabic Fake News Detection: Leveraging Language Model Embeddings and Stacked Ensemble

The proliferation of fake news poses a substantial threat to information integrity, prompting the need for robust detection mechanisms. This study advances the research on Arabic fake news detection and overcomes the limitation of lower accuracy for fake news detection. This research addresses Arabic fake news detection using word embedding and a powerful stacking classifier. The proposed model combines bagging, boosting, and baseline classifiers, harnessing the strengths of each to create a robust ensemble. Extensive experiments are carried out to evaluate the proposed approach indicating remarkable results, with recall, F1 score, accuracy, and precision reaching 99%. The utilization of advanced stacking techniques, coupled with appropriate textual feature extraction, empowers the model to effectively detect Arabic fake news. Study results make a valuable contribution to fake news detection, particularly in the Arabic context, providing a valuable tool for enhancing information veracity and fostering a more informed public discourse. Furthermore, the proposed model’s accuracy is compared with other cutting-edge models from the existing literature to showcase its superior performance.

Rumor gatekeepers: Unsupervised ranking of Arabic twitter authorities for information verification

The advent of online social networks (OSNs) has catalyzed the formation of novel learning communities. Identifying experts within OSNs has become a critical component for facilitating knowledge exchange and enhancing self-awareness, particularly in contexts such as rumor verification processes. Research efforts aimed at locating authorities in OSNs are scant, largely due to the scarcity of annotated datasets. This work represents a contribution to the domain of unsupervised learning to address the challenge of authorities’ identification in Twitter. We have employed advanced natural language processing technique to transfer knowledge concerning topics in the Arabic language and to discern the semantic connections among candidates within Twitter in zero-shot learning. We take advantage of the Single-labeled Arabic News Articles Dataset (SANAD) to perform the process of extracting domain features and applying these features in finding authorities using the Authority Finding in Arabic Twitter (AuFIN) dataset. Our evaluation assessed the extent of extracted topical features transferred and the efficacy of authorities’ retrieval in comparison to the latest unsupervised models in this domain. Our approach successfully extracted and integrated the limited available topical semantic features of the language into the representation of candidates. The findings indicate that our hybrid model surpasses those that rely solely on lexical features of language and network topology, as well as other contemporary approaches to topic-specific expert finding.

Arabic text classification based on analogical proportions

AbstractText classification is the process of labelling a given set of text documents with predefined classes or categories. Existing Arabic text classifiers are either applying classic Machine Learning algorithms such as k‐NN and SVM or using modern deep learning techniques. The former are assessed using small text collections and their accuracy is still subject to improvement while the latter are efficient in classifying big data collections and show limited effectiveness in classifying small corpora with a large number of categories. This paper proposes a new approach to Arabic text classification to treat small and large data collections while improving the classification rates of existing classifiers. We first demonstrate the ability of analogical proportions (AP) (statements of the form ‘x is to as is to ’), which have recently been shown to be effective in classifying ‘structured’ data, to classify ‘unstructured’ text documents requiring preprocessing. We design an analogical model to express the relationship between text documents and their real categories. Next, based on this principle, we develop two new analogical Arabic text classifiers. These rely on the idea that the category of a new document can be predicted from the categories of three others, in the training set, in case the four documents build together a ‘valid’ analogical proportion on all or on a large number of components extracted from each of them. The two proposed classifiers (denoted AATC1 and AATC2) differ mainly in terms of the keywords extracted for classification. To evaluate the proposed classifiers, we perform an extensive experimental study using five benchmark Arabic text collections with small or large sizes, namely ANT (Arabic News Texts) v2.1 and v1.1, BBC‐Arabic, CNN‐Arabic and AlKhaleej‐2004. We also compare analogical classifiers with both classical ML‐based and Deep Learning‐based classifiers. Results show that AATC2 has the best average accuracy (78.78%) over all other classifiers and the best average precision (0.77) ranked first followed by AATC1 (0.73), NB (0.73) and SVM (0.72) for the ANT corpus v2.1. Besides, AATC1 shows the best average precisions (0.88) and (0.92), respectively for the BBC‐Arabic corpus and AlKhaleej‐2004, and the best average accuracy (85.64%) for CNN‐Arabic over all other classifiers. Results demonstrate the utility of analogical proportions for text classification. In particular, the proposed analogical classifiers are shown to significantly outperform a number of existing Arabic classifiers, and in many cases, compare favourably to the robust SVM classifier.

Survey of machine learning techniques for Arabic fake news detection

Social media platforms have emerged as primary information sources, offering easy access to a wide audience. Consequently, a significant portion of the global population relies on these platforms for updates on current events. However, fraudulent actors exploit social networks to disseminate false information, either for financial gain or to manipulate public opinion. Recognizing the detrimental impact of fake news, researchers have turned their attention to automating its detection. In this paper, we provide a thorough review of fake news detection in Arabic, a low-resource language, to contextualize the current state of research in this domain. In our research methodology, we recall fake news terminology, provide examples for clarity, particularly in Arabic contexts, and explore its impact on public opinion. We discuss the challenges in fake news detection, outline the used datasets, and provide Arabic annotation samples for label assignment. Likewise, preprocessing steps for Arabic language nuances are highlighted. We also explore features from shared tasks and their implications. Lastly, we address open issues, proposing some future research directions like dataset improvement, feature refinement, and increased awareness to combat fake news proliferation. We contend that incorporating our perspective into the examination of fake news aspects, along with suggesting enhancements, sets this survey apart from others currently available.

A Qualitative Exploration Of The Impact Of Fostering AFL Learners' Vocabulary Skills Using BBC Arabic Live News

This qualitative study aims to analyze the impact of BBC Arabic Live on fostering vocabulary skills. The subject totaled 37 slow learners in their bachelor of art program; the Arabic department was at The New College, Chennai, India. The Experimental Group was trained for vocabulary acquisition through ten classroom activities using BBC Arabic Live. The qualitative questionnaires were given after carrying out the classroom activities. The data were analyzed using a qualitative approach. The results showed that a live BBC Arabic News classroom activity approach brought about potential changes in their vocabulary acquisition. This study applied pedagogical implications for fostering vocabulary skills through listening skills. It underscored the practical importance of student-centered approaches for honing modern Arabic vocabulary skills. Overall, the study's findings point to the potential benefits of using BBC Arabic Live in the classroom to help students learning Arabic as a foreign language acquire vocabulary. The study's findings emphasize the necessity of learner-centric and student-centered approaches to language learning, which has significant ramifications for approaches to language teaching in higher education. Future studies in this field might look at the impact of vocabulary development on other language abilities like reading and writing, or they might examine the usage of different media sources for vocabulary acquisition.

An Ensemble Keyword Extraction Model for News Texts with Statistical and Graphical Features

Keyword extraction is an essential tool for many text mining applications such as automatic indexing, summarizing, classification, clustering and automatic filtering. Automated keyword extraction is essential as the daily text data to be reached and processed have increased tremendously over the Internet, e.g. millions of news articles are published daily online. In this paper, a novel ensemble model for automatic extraction of keywords from news articles is proposed. The proposed model handles keyword extraction as a sequence labeling task. Two sub-modules representing the statistical and graphical features by their calculated scores for each input token were combined in the token classification module. The Ensemble Token Classification module was trained and tested separately with the ensemble algorithms Random Forest, XgBoost, Decision Tree and Voting Classification. For training, we collected two news datasets from Kazakh and Russian news sites published in Cyrillic alphabet. We also collected an Arabic news dataset, ArabianNews. The performance of the model was also compared with the widely used 500N-KPCrowd dataset in the literature, which consists of English news content in Latin alphabet. The proposed model achieved the best performance with an [Formula: see text]-score of 0.71 and 0.86 on the 500N-KPCrowd and Russian datasets, respectively. We attained the best [Formula: see text]-score (0.97) with the KazakhNews and ArabianNews datasets.

The Use of Television Visual Technologies in Presenting Terrorism

Abstract This study examines how RT Arabic and Sky News Arabia channels present the news about ISIS through their newscasts; revealing the visual technologies these networks adopted in covering ISIS. This study centers its analysis on a sample of news stories solely about ISIS in two international news networks, RT Arabic and Sky News Arabia as broadcast on their satellite channels and their websites from the period 2014 to 2017. The study found that among the eleven different types of newsroom visual technologies identified in the study, both channels relied on graphs most frequently to present ISIS news visually. However, unlike Sky News Arabia, RT Arabic employed limited usage of visual technology to cover the brutality and violent acts of ISIS in its coverage.