Arabic News Articles Research Articles

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.

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.

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.

Hybrid Neural Network Models for Detecting Fake News Articles

The prevalence of world-wide access to the Internet has come at a cost. A lot of misleading information is posted on public news websites and social media. Many news writers and organizations manipulate their posted data to propagate false information that target different societies and in different languages. Accurate and timely detection of false news is made possible in large part using machine learning-based technologies. This paper targets the problem of detecting fake news in Arabic language using machine learning models. A hybrid model of two deep neural networks is used to classify Arabic news articles in order to detect fake articles. The two types of neural networks are convolutional and bi-directional long-short term memory. Robust features are extracted using two different word vectors and a complex model of a convolutional neural network. Moreover, a set of auxiliary output layers are used to enhance the model accuracy. Multi-class classification is achieved via modifying the primary output layer. Results show an accuracy of 88% and 78% for binary classification and multi-class classification, respectively.

Speech Acts Used in Covid-19 English and Arabic News Reports

For the past three years, Covid-19 has been the dominant topic in international media. It has been the central topic of various researches and studies. Accordingly, this study aims at presenting the theoretical background of pragmatics, media discourse, and the relation between the two. In addition, it aims at identifying and uncovering the speech acts used in selected Covid-19 English and Arabic news articles following Searle (1979) taxonomy. The selected articles are published by different local and international digital newspapers, including USA Today, The Guardian, Daily Mail, Almada Newspaper, BAGHDADTODAY.NEWS, and Asharq Al-Awsat Newspaper. The findings of the study prove that speech acts are used more in the English reports than in the Arabic reports. Also, the most frequently used category is assertive speech acts in the two types of data.

Arabic News Classification Based on the Country of Origin Using Machine Learning and Deep Learning Techniques

With the rise of Arabic news articles published daily, people are becoming increasingly concerned about following the news from reliable sources, especially regarding events that impact their country. To assess a news article’s significance to the user, it is essential to identify the article’s country of origin. This paper proposes several classification models that categorize Arabic news articles based on their country of origin. The models were developed using comprehensive machine learning and deep learning techniques with several feature training methods. The results show the ability of our model to classify news articles based on their country of origin, with close accuracy between machine learning and deep learning techniques of up to 94%.

From Eco-Jihad to Politicization: A Corpus-based Eco-linguistic Discourse Analysis of the Arab Media Coverage of the Safer Floating Oil Tanker

This study attempts a corpus-based discourse analysis of the coverage of the FSO Safer in Arabic media to determine the main recurrent categories and themes in the coverage produced by outlets associated with conflicting/warring parties, as well as reports from more neutral media. This study, therefore, provides analysis of media coverage on the FSO Safer starting with the first report from Aljazeera on the issue in 2019, until June 2022. This study utilized Sketch Engine to compile a digital corpus of Arabic news articles. The corpus consists of 420,00 tokens. Additionally, this study explores how the word al-bīʾah (environment) is represented in the corpus by conducting a transitivity analysis of each concordance line or clause that included the word or one of its variants. This study also examines the various manipulative strategies that media outlets associated with conflicting parties used to determine how each presented the 'other', holding them accountable should the catastrophe strike. The findings of this study indicated that the themes recurrent in the corpus include the scale of catastrophe, environmental damage in the event of a spill, economic consequences, the UN emergency plan, echo-jihad, and 'we' vs 'them', among others. The presence of the word al-bīʾah in the corpus clearly shows that human beings are represented as the most active of beings; those who think, do and act in the world and those who behave and speak. Inanimate objects, on the other hand, are represented as passive participants; things are done to them.

Similarity Detection of Time-Sensitive Online News Articles Based on RSS Feeds and Contextual Data

This article tackles the problem of finding similarity between web time-sensitive news articles, which can be a challenge. This challenge was approached with a novel methodology that uses supervised learning algorithms with carefully selected features (Semantic, Lexical and Temporal features (content and contextual features)). The proposed approach considers not only the textual content, which is a well-studied approach that may yield misleading results, but also the context, community engagement, and community-deduced importance of that news article. This paper details the major procedures of title pair pre-processing, analysis of lexical units, feature engineering, and similarity measures. Thousands of web articles are being published every second, and therefore, it is essential to determine the similarity of these articles efficiently without wasting time on unnecessary text processing of the bodies. Hence, the proposed approach focuses on short contents (titles) and context. The conducted experiment showed high precision and accuracy on a Really Simple Syndication (RSS) dataset of 8000 Arabic news article pairs collected automatically from 10 different news sources. The proposed approach achieved an accuracy of 0.81. Contextual features increased the accuracy and the precision. The proposed algorithm achieved a 0.89 correlation with the evaluations of two human judges based on Pearson’s Correlation Coefficient. The results outperform the state-of-the-art systems on Arabic news articles.

Investigating the relevance of Arabic text classification datasets based on supervised learning

Training and testing different models in the field of text classification mainly depend on the pre-classified text document datasets. Recently, seven datasets have emerged for Arabic text classification, including Single-Label Arabic News Articles Dataset (SANAD), Khaleej, Arabiya, Akhbarona, KALIMAT, Waten2004, and Khaleej2004. This study investigates which of these datasets can provide significant training and fair evaluation for text classification. In this investigation, well-known and accurate learning models are used, including naive Bayes, random forest, K-nearest neighbor, support vector machines, and logistic regression models. We present relevance and time measures of training the models with these datasets to enable Arabic language researchers to select the appropriate dataset to use based on a solid basis of comparison. The performances of the five learning models across the seven datasets are measured and compared with the performance of the same models trained on a well-known English language dataset. The analysis of the relevance and time scores shows that training the support vector machine model on Khaleej and Arabiya obtained the most significant results in the shortest amount of time, with the accuracy of 82%.

High dimensional autonomous computing on Arabic language classification

Hyper vectors are holographic and randomly processed with independent and identically distributed tools. A hyper vector includes whole data merged as well as spread completely on its pieces as an encompassing portrayal. So, no spot is more dependable to store any snippet of data compared to others. Hyper vectors are joined with tasks likened to expansion, and changed the structure of numerical processing on vector regions. Hyper vectors are intended to analyze the closeness utilizing a separation metric over the vector region. These activities are nothing but hyper vectors in which it can be joined into intriguing processing conduct with novel highlights which make them vigorous and proficient. This paper focuses on a utilization of hyper dimensional processing for distinguishing the language of text tests for encoding sequential letters into hyper vectors. Perceiving the language of a given book is the initial phase in all sorts of language handling. Examples: text examination, arrangement, and interpretation. High dimension vector models are mainstream in Natural Language Processing and are utilized to catch word significance from word insights. In this research work, the first task is high dimensional computing classification, based on Arabic datasets which contain three datasets such as Arabiya, Khaleej and Akhbarona. High dimensional computing is applied to obtain the results from the previous dataset when it is applied to N-gram encoding. When utilizing SANAD single-label Arabic news articles datasets with 12 N-gram encoding, the accuracy of high computing is 0.9665%. The high dimensional computing with 6 N-gram encoding while utilizing RTA dataset, provides the accuracy of 0.6648%. ANT dataset with 12 N-gram encoding in high dimensional computing gives the accuracy 0.9248%. The second task is applying high dimensional computing on Arabic language recognition for Levantine dialects three dataset is utilized. The first dataset is SDC Shami Dialects Corpus which contains Jordanian, Lebanese, Palestinian and Syrian. The same provides an accuracy of 0.8234% while it is applied to high dimensional computing with 7 N-gram encoding. PADIC (Parallel Arabic dialect corpus) is the second dataset which contains Syria and Palestine Arabic dialects that provide an accuracy of 0.7458% when applied high dimensional computing with 5 N-gram encoding. The high dimensional computing when applied to third dataset MADAR (Multi-Arabic dialect applications and resources) with 6 N-gram encoding provides the accuracy rate of 0.7800%.

Arabic Fake News Detection Based on Textual Analysis

Over the years, social media has had a considerable impact on the way we share information and send messages. With this comes the problem of the rapid distribution of fake news which can have negative impacts on both individuals and society. Given the potential negative influence, detecting unmonitored ‘fake news’ has become a critical issue in mainstream media. While there are recent studies that built machine learning models that detect fake news in several languages, lack of studies in detecting fake news in the Arabic language is scare. Hence, in this paper, we study the issue of fake news detection in the Arabic language based on textual analysis. In an attempt to address the challenges of authenticating news, we introduce a supervised machine learning model that classifies Arabic news articles based on their context’s credibility. We also introduce the first dataset of Arabic fake news articles composed through crowdsourcing. Subsequently, to extract textual features from the articles, we create a unique approach of forming Arabic lexical wordlists and design an Arabic Natural Language Processing tool to perform textual features extraction. The findings of this study promises great results and outperformed human performance in the same task.

Arabic Fake News Detection Using Deep Learning

Nowadays, an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online communication. However, because user-generated content is unregulated, it may contain offensive content such as fake news, insults, and harassment phrases. The identification of fake news and rumors and their dissemination on social media has become a critical requirement. They have adverse effects on users, businesses, enterprises, and even political regimes and governments. State of the art has tackled the English language for news and used feature-based algorithms. This paper proposes a model architecture to detect fake news in the Arabic language by using only textual features. Machine learning and deep learning algorithms were used. The deep learning models are used depending on conventional neural nets (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), CNN+LSTM, and CNN + BiLSTM. Three datasets were used in the experiments, each containing the textual content of Arabic news articles; one of them is real-life data. The results indicate that the BiLSTM model outperforms the other models regarding accuracy rate when both simple data split and recursive training modes are used in the training process.

Media Coverage of Syrian Female Refugees in Jordan and Lebanon

Jordan and Lebanon are among the predominantly affected countries by the Syrian civil war that began after the outbreak of the Arab Spring in 2011. Hundreds of thousands of displaced Syrians, including women and children, moved to these two countries. As media plays an integral role in shaping societies and framing the world around us, this study explores the representation of Syrian female refugees in Jordan News Agency (Petra) and Lebanon National News Agency (NNA). It uses a 13-million-word corpus of Arabic news articles from 2012 to 2016. These 5 years represent the peak of the Syrian refugee crisis, and the media coverage of it was extensive and thorough. Adopting a corpus-assisted discourse approach to analyze the data yielded eight main categories that were recurrent in the news of the two agencies, namely, suffering, sexual exploitation, impact on the local female community, awareness-raising, making a living, support, vulnerability, and burden. The findings revealed that the focus in Petra news was mainly on empowering the Syrian female refugees, while NNA highlighted Syrian women’s suffering and efforts of alleviation. The study recommends that the governments of Jordan and Lebanon as well as international organizations provide more support and protection for vulnerable and disadvantaged refugee women. It also stresses the need for psychosocial support, counter-trafficking, protection, and women empowerment programs.

Multi-label Arabic text categorization: A benchmark and baseline comparison of multi-label learning algorithms

Multi-label text categorization refers to the problem of assigning each document to a subset of categories by means of multi-label learning algorithms. Unlike English and most other languages, the unavailability of Arabic benchmark datasets prevents evaluating multi-label learning algorithms for Arabic text categorization. As a result, only a few recent studies have dealt with multi-label Arabic text categorization on non-benchmark and inaccessible datasets. Therefore, this work aims to promote multi-label Arabic text categorization through (a) introducing “RTAnews”, a new benchmark dataset of multi-label Arabic news articles for text categorization and other supervised learning tasks. The benchmark is publicly available in several formats compatible with the existing multi-label learning tools, such as MEKA and Mulan. (b) Conducting an extensive comparison of most of the well-known multi-label learning algorithms for Arabic text categorization in order to have baseline results and show the effectiveness of these algorithms for Arabic text categorization on RTAnews. The evaluation involves four multi-label transformation-based algorithms: Binary Relevance, Classifier Chains, Calibrated Ranking by Pairwise Comparison and Label Powerset, with three base learners (Support Vector Machine, k-Nearest-Neighbors and Random Forest); and four adaptation-based algorithms (Multi-label kNN, Instance-Based Learning by Logistic Regression Multi-label, Binary Relevance kNN and RFBoost). The reported baseline results show that both RFBoost and Label Powerset with Support Vector Machine as base learner outperformed other compared algorithms. Results also demonstrated that adaptation-based algorithms are faster than transformation-based algorithms.

The Representation of Laji’een (Refugees) and Muhajireen (Migrants) in the Headlines of Jordan News Agency (PETRA)

This paper explores the representation of Laji’een (Refugees) and Muhajireen (Migrants) in Jordan News Agency (PETRA). It uses the headlines of a 2.5 million word corpus of Arabic news articles in a time span of 5 years from 2012 to 2016. Chronologically analyzing the headlines shows a change in the representation of and attitudes towards refugees and migrants over the investigated period. The analysis of the headlines shows that 2012 starts with providing the assistance to the refugees then at a later stage of 2012 and in 2013 and 2014, the language is shifted to calling for assistance to the country to contribute to solving the problem. In 2015, the language is changed to be more alerting about the scarce in funding. In 2016, the headlines have changed the focus to Europe due to the migration of the refugees. This motivates the National News Agency of Jordan to highlight the suffering of Europe because of the refugees despite having the capacity and resources to host the migrant refugees. The paper concludes that language in general, and the language of media in particular can change the attitudes of people and governments toward critical issues and phenomena like migration and asylum.

Using corpus linguistic techniques in (critical) discourse studies reduces but does not remove bias: Evidence from an Arabic corpus about refugees

Abstract Critical Discourse Analysis (CDA) investigates the relationship between language, power, and society. Corpus linguistics (CL) is the study of language based on examples of real life language use. Over the last two decades, various scholars have combined some approaches and notions of CDA with the analytical framework of CL to examine the representation of several phenomena in relatively large texts. This study follows a corpus-assisted (critical) discourse analysis approach to investigate a 2.5 million word corpus of Arabic news articles by Jordan’s News Agency (PETRA). It demonstrates how some researchers following this approach may make some decisions, at some stages of their analysis, which are likely to affect their findings. These potential decisions may include selecting what statistical measures to use, what threshold to consider, what terms from the frequency, cluster, and collocation results to further investigate, which concordance lines to include in their study, and some others. In this study, I argue that some of these decisions can be made to suit the researchers’ preconceived assumptions and pre-existing hypotheses. The study concludes that using corpus linguistic techniques to discursively analyze large data reduces but not completely removes researchers’ bias.

Automated arabic text classification with P‐Stemmer, machine learning, and a tailored news article taxonomy

Arabic news articles in electronic collections are difficult to study. Browsing by category is rarely supported. Although helpful machine‐learning methods have been applied successfully to similar situations for English news articles, limited research has been completed to yield suitable solutions for Arabic news. In connection with a Qatar National Research Fund (QNRF)‐funded project to build digital library community and infrastructure in Qatar, we developed software for browsing a collection of about 237,000 Arabic news articles, which should be applicable to other Arabic news collections. We designed a simple taxonomy for Arabic news stories that is suitable for the needs of Qatar and other nations, is compatible with the subject codes of the International Press Telecommunications Council, and was enhanced with the aid of a librarian expert as well as five Arabic‐speaking volunteers. We developed tailored stemming (i.e., a new Arabic light stemmer called P‐Stemmer) and automatic classification methods (the best being binary Support Vector Machines classifiers) to work with the taxonomy. Using evaluation techniques commonly used in the information retrieval community, including 10‐fold cross‐validation and the Wilcoxon signed‐rank test, we showed that our approach to stemming and classification is superior to state‐of‐the‐art techniques.