Arabic News Research Articles

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

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.

Arabic Fake News Detection in Social Media Context Using Word Embeddings and Pre-trained Transformers

Arabic Fake News Detection in Social Media Context Using Word Embeddings and Pre-trained Transformers

Al-Quds Issues in The Media: A Comparative Study of Aqsa T.V. And Palestine T.V. Coverage During the American Embassy Relocating

This article examines the coverage of Al-Quds/Jerusalem issues by Palestinian political parties, FATAH and HAMAS, on their affiliated television channels, Palestine TV and Aqsa TV, respectively. The study aims to investigate the influence of political parties and religious perspectives on the coverage of Al-Quds and determine the significance of Al-Quds as a national issue for Palestinians. The study focuses on the period surrounding the relocation of the American embassy to Al-Quds, from 13 May to 19 May 2018, where 24 Arabic news broadcasts were collected. The study utilizes the Agenda Setting Theory to analyze the role of Aqsa TV in shaping public opinion and perceptions of Al-Quds. Through content analysis of prime-time news broadcasts from both channels during a specific week, the findings reveal differences in the prioritized topics, placement within the news, media formats employed, and duration of coverage. Palestine TV emphasizes the civil aspects of Al-Quds from a national perspective. At the same time, Aqsa TV highlights the religious significance of the Islamic-Jewish conflict. The study concludes that the news coverage of Al-Quds reflects the political agendas of FATAH and HAMAS and suggests further research on the contrasting portrayals of Al-Quds issues in Palestinian and Israeli media.

Detect Arabic fake news through deep learning models and Transformers

Fake news has become a serious problem due to many reasons; its rapid spread across the Internet, the difficulty of discovering and distinguishing it from the real news, and the increasing dependence of individuals on social media platforms as the main source. In addition, it has harmful consequences at different levels; individual, community, political, and financial levels. Arabic fake news detection task still needs a considerable amount of effort, due to the lack of datasets and limited research in this field. In this thesis, we investigate detecting the credibility of news in Arabic, based on deep learning methods and transformers models for capturing the hidden information and pattern rather than depending on a set of handcrafted features, that have been addressed in most of the related work in the literature. Also, we release “ArabicFakeNews” which is a manual fake news dataset that contains 2 k Arabic fake news collected from different sources. We also study how the length of news can affect the model performance by applying the deep learning model and transformer-based model on three datasets with different average lengths (title, description, and text dataset). The description dataset performs better in classifying the news, this is due to the fact that the average length of fake news is close to the average length of real news in this dataset. The evaluation result shows that transformers models outperform the deep learning models on the three datasets. The transformer results show that AraBERTv2 on the “description” dataset gets the highest result among all other transformers with an accuracy of 0.97, 0.97 f1-sore, 0.97 precision, and 0.9658 recall.

Mediated Clash of Civilizations: Examining the Proximity-Visual Framing Nexus in Al Jazeera Arabic and Fox News’ Coverage of the 2021 Gaza War

The Israeli–Palestinian conflict is amongst the most polarizing topics today, particularly when comparing predominant U.S. and Arab perspectives. News media are often criticized for being pro-Israel or pro-Palestine, and more so during deadly wars. This study examines all 320 images Al Jazeera Arabic and Fox News put out in their articles covering the May 2021 Gaza War and investigates how Qatar and the U.S.’s proximity to Israel and Palestine factored into the outlets’ visual framing choices. Despite both invoking the David and Goliath master narrative, they provided a polarized coverage of the war, with Al Jazeera proliferating narratives of Hamas and pro-Palestine supporters and exclusively foregrounding the suffering of Palestinian civilians, and Fox legitimating Israeli military actions and presenting Hamas as a terrorizing force that disrupts the lives of Israelis as well as wreaks havoc and fuels anti-Jewish sentiments in the U.S. After dissecting the cross-cultural differences in death and dying portrayals, displays of Israelis and Palestinians, and recurrent visual themes, the study concludes with a discussion on the influence of news agencies on media’s photographic decisions and the implications of extreme alignment in the proximity-framing nexus on the propagation of mediated clash of civilizations in the digital news sphere.

Ensemble-Based Machine Learning Approach for Detecting Arabic Fake News on Twitter

Ensemble-Based Machine Learning Approach for Detecting Arabic Fake News on Twitter