Arabic News Research Articles

Underrepresented and marginalized: Television news framing of ordinary Arab citizens before the Arab uprisings of 2011

This study applied news framing theory with mixed quantitative and qualitative methods to analyse news items (N = 1348) about ordinary Arabs on Al Jazeera Arabic, Al Arabiya and Nile News TV shortly before the Arab Spring. Results show that ordinary Arab citizen representation was low. Overall, there were significant differences in networks’ framing of ordinary people. Importance, negativity and conflict values dominated the news featuring ordinary citizens. Arab news networks did not provide adequate time for citizens to voice opinions, and limited representation occurred via vox pop, footage and indirect reference. Networks employed negative sentimental framing (protest and rejection, economic problems, victimization, health problems and mistrust in governments) and mainly portrayed citizens of countries undergoing crises and wars. Arab television news should prioritize sharing the opinions, concerns and successes of ordinary Arab people and engage in constructive journalism rather than concentrating on problem frames without offering solutions.

La metáfora en los términos políticos y diplomáticos árabes y su traducción al español

Metaphor is one of the tools that describe something by resemblance. It’s used to give more focus to an idea or a feeling. It can be said that metaphor contributes to the building of any kind of discourse, used in the political and diplomatic fields; we find it in political or diplomatic texts, especially in media channels. This work explains some information about political and diplomatic metaphors with examples taken from press. The goal from choosing political metaphors is to explain their role in the structure of the political and diplomatic term. We have chosen seven political metaphors and six diplomatic terminologies from BBC Arabic (an Arabic news channel). For example: the term Arab Spring, a storm in a teacup.

Translating Ideologies in Arabic News: Textual Alterations in Al-Arabiya and Aljazeera’s Discourses

This article explores how the institutional and ideological practices of news media can impact on translating news articles by focusing on the ideological representations in media discourse. It tests the hypothesis that hidden ideologies in news texts are changed/re-presented in the process of reformulation, taking into account the translation practices followed by news institutions. The study applies a Critical Discourse Analysis model to analyse issues relayed to representing people and events in a range of media texts. To do this, it uses a corpus of 63 news articles collated from Aljazeera and Al-Arabiya websites to identify the textual, ideological, and institutional representations and alterations happening through translation. It shows that it is possible for a news translator to carry out minor or major alterations to a news story under translation for the purpose of ideologically reorienting their message or text-focus.

Improving Prediction of Arabic Fake News Using Fuzzy Logic and Modified Random Forest Model

Throughout the last few years, the world is witnessing the so-called age of social media, as there is a complete dependence on these sites for following up on events and activities. The problem is that the misinformation or fake news is always released at the appropriate time, so this false news spreads quickly and takes a very wide resonance. Although several studies are performed to determine English fake news, the identification of Arabic misinformation remains underdeveloped. This study aims to build an improved learning model for detecting fake news in the Arabic language. Unlike previous studies that depended on analyzing the content of the tweet only, this study focuses on the text, user features, and text features. Regarding the content of the tweet, the TF-IDF method was used to convert the words into features and then determine the features that have a high rank. In contrast, a fuzzy model was used to determine the relevant features for the user. Finally, the random forest algorithm has been adapted and improved, and its results are better as compared to other machine learning methods. The accuracy of Improved Random Forest is (0.895), whereas the accuracy of Naive Bayesian and SVM techniques are found to be (0.809) and (0.848), respectively.

Fake news detection for Arabic headlines-articles news data using deep learning

Fake news has become increasingly prevalent in recent years. The evolution of social websites has spurred the expansion of fake news causing it to a mixture with truthful information. English fake news detection had the largest share of studies, unlike Arabic fake news detection, which is still very limited. Fake news phenomenon has changed people and social perspectives through revolts in several Arab countries. False news results in the distortion of reality ignite chaos and stir public judgments. This paper provides an Arabic fake news detection approach using different deep learning models including long short-term memory and convolutional neural network based on article-headline pairs to differentiate if a news headline is in fact related or unrelated to the parallel news article. In this paper, a dataset created about the war in Syria and related to the Middle East political issues is utilized. The whole data comprises 422 claims and 3,042 articles. The models yield promising results.

Arabic Language Modeling Based on Supervised Machine Learning

Misinformation and misleading actions have appeared as soon as COVID-19 vaccinations campaigns were launched, no matter what the country’s alphabetization level or growing index is. In such a situation, supervised machine learning techniques for classification appears as a suitable solution to model the value & veracity of data, especially in the Arabic language as a language used by millions of people around the world. To achieve this task, we had to collect data manually from SM platforms such as Facebook, Twitter and Arabic news websites. This paper aims to classify Arabic language news into fake news and real news, by creating a Machine Learning (ML) model that will detect Arabic fake news (DAFN) about COVID-19 vaccination. To achieve our goal, we will use Natural Language Processing (NLP) techniques, which is especially challenging since NLP libraries support for Arabic is not common. We will use NLTK package of python to preprocess the data, and then we will use a ML model for the classification.

Combining Machine Learning and Semantic Analysis for Efficient Misinformation Detection of Arabic Covid-19 Tweets

With the spread of social media platforms and the proliferation of misleading news, misinformation detection within microblogging platforms has become a real challenge. During the Covid-19 pandemic, many fake news and rumors were broadcasted and shared daily on social media. In order to filter out these fake news, many works have been done on misinformation detection using machine learning and sentiment analysis in the English language. However, misinformation detection research in the Arabic language on social media is limited. This paper introduces a misinformation verification system for Arabic COVID-19 related news using an Arabic rumors dataset on Twitter. We explored the dataset and prepared it using multiple phases of preprocessing techniques before applying different machine learning classification algorithms combined with a semantic analysis method. The model was applied on 3.6k annotated tweets achieving 93% best overall accuracy of the model in detecting misinformation. We further build another dataset of Covid-19 related claims in Arabic to examine how our model performs with this new set of claims. Results show that the combination of machine learning techniques and linguistic analysis achieves the best scores reaching 92% best accuracy in detecting the veracity of sentences of the new dataset.

The media image of the Kurdistan Region of Iraq in Arabic Satellite News Channels Sites: Analysis Study (Aljazeera net)

This research presents the media image of the Kurdistan Region of Iraq, on (Aljazeera net) website. This study uses content analysis methodology to reveal the content of topics concern to the Kurdistan region. The study reachs with a set of conclusions such as; the main focus of the news has been on issues of problems and conflicts, whether political, military or economic. The editorial policies of Aljazeera.net sites reflect their selection and formulation of news content. The study also proposes a number of recommendations, such as working on diversification of news topics concerning the Kurdistan Region, not being limited to frames the conflicts and problems, and moving towards news that serve the culture of communication between the Iraqi people, in particular between Arabs and Kurds.

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%.

Activation of mental imagery neural network revealed during listening to Fatihah Chapter; a neuroimaging study

Background: Listening to Quranic recitation has been claimed to enhance educational performance and cognitive functionality. Unfortunately, scientific evidence for this remains scarce. Aim: Hence, we aimed to investigate the neural mechanism underlined the psychoacoustic and cognitive enhancement effects from listening to Quranic recitation. Occipital lobe beta rhythms (14-30 Hz) were excerpted from electroencephalography (EEG) data during psychoacoustic stimulation from sham (Rest), Arabic news and the Fatihah Chapter recitation. The stimulation comes in random sequence in the same length of duration of 5,8,6,6,10, 8 and 20 seconds for verses 1 to 7, respectively. The brain’s oscillatory waveforms were pre-processed using EGI System and then were analysed by Fast Fourier Transform (FFT) to derive the power spectrum in the Beta frequency band (14-30 Hz). Data were analysed by repeated-measures ANOVA and Discriminant Analysis from XLSTAT statistical package. Result: A profound and significant reduction of the power spectrum was found in the left occipital lobe while listening to theFatihah Chapter. In contrast, no significant changes were observed in the occipital lobe during listening to Arabic news. It may be postulated that listening to the Fatihah Chapter activates an oscillatory neural network associated with visual mental imagery. Bangladesh Journal of Medical Science Vol. 21 No. 03 July’22 Page: 710-716

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%.

Representative Speech Act in Arabic News Discourse: Study on Aljazeera and Al-Ahram

Covid-19 pandemic has caused many pros and cons which are mainly represented in the news in the mass media. Therefore, this study aims to observe how the mass media from Arab countries represent their position towards the Covid-19 pandemic through the news they release. In this study, the data is retrieved from Aljazeera and Al-Ahram. To find out their thoughts and opinions regarding the Covid-19 pandemic, the representative speech act framework presented by Yule (1996) and Searle (1979) is used. Meanwhile, the descriptive-analytical method on that news is relied, starting from providing data, classifying, analyzing, and presenting the results of the analysis. The result shows that there are four types of representative speech acts found in the Arabic news discourse, they are statement of fact, assertion, conclusion, and description. The writers represent their thoughts and opinions by choosing certain verbs or cognate objects, either agreeing or disputing some opinions, as well as citing various news sources or events that are considered valid and credible. Moreover, the comparison of the results from the two news sources did not show a significant difference because the language of these two newspapers is the Modern Standard Arabic (MSA).

Multi-scale Machine Learning Prediction of the Spread of Arabic Online Fake News

There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't look at what makes those fake news spread. The threat grows, and at some point, it gets out of hand. That's why this paper is trying to figure out how to predict the features that make Arabic online fake news spread. It's using Naive Bayes, Logistic Regression, and Random forest of Machine Learning to do this. Online news stories that were made up were used. They are found by using Term Frequency-Inverse Document Frequency (TF-IDF). The best partition for testing and validating the prediction was chosen at random and used in the analysis. So, all three machine learning classifications for predicting fake news in Arabic online were done. The results of the experiment show that Random Forest Classifier outperformed the other two algorithms. It had the best TF-IDF with an accuracy of 86 percent. Naive Bayes had an accuracy rate of 84%, and Logistic Regression had an accuracy rate of 85%, so they all did well. As such, the model shows that the features in TF-IDF are the most essential point about the content of an online Arabic fake news.

Synopsis on Arabic speech recognition

With the advancement and increased usage of intelligible smart devices, researchers have an intensified interest in the field of large-vocabulary speaker-independent continuous speech recognition. Although considerable research has been devoted to English speech recognition, less attention has been paid to the Arabic speech recognition. This paper aims to highlight the achievements that have been made during the last several decades of Arabic speech recognition. The paper also discusses speech recognition components such as corpora, phonemes, language models, acoustic models, and performance evaluation. For an empirical evaluation of Arabic speech recognition, the free, off-the-shelf Mac Soundflower tool was employed to evaluate the recognition performance using a continuous speech corpus that contains 2.63 h (by 10 male and 10 female speakers) of modern standard Arabic (MSA) broadcast news. The experimental results indicate recognition accuracy at 54.02%, and the accuracies for the male and female speakers are almost the same. This result promotes the need for further research to expose the practical range of accuracy. The performance’s decline might be an indication of the necessity for further research to boost the overall recognition accuracy.

A Revolution Or A Journey Towards Liberty? A Cognitive Metaphorical Analysis Of The Jasmine Revolution In English And Arabic News Discourse

This study offers a cognitive metaphorical analysis of the events framing the Tunisian Revolution in English and Arabic news discourse between 2010 and 2014. The study addressed two central questions: (1) How has the Tunisian Revolution been metaphorically represented in English and Arabic political news discourse based on the cognitive domain of journey? What are the similarities and differences between the metaphorical representations in the same news discourse? The conceptual integration network emerging in the analysis is single-scope as the two input spaces of journey and revolution were demonstrated to have different organizing frames. Specifically, only the frame of journey is projected to organize the blend. Findings showed that anchoring the concept of revolution to that of journey has created a kind of semantic tension that helped journalists to evoke emotional responses and manipulate the discursive function of metaphors as persuasive tools. Because of the cross-space mappings between journey and revolution, nearly all concepts related to the revolutionary experience proved to have a counterpart in the journey frame. Equally important, the results of comparing and contrasting cognitive metaphors in English and Arabic news discourse could offer insights into the way language develops based on a cognitive basis. Therefore, learners of both English and Arabic could access the system governing language learning concerning the formation of semantic networks based on different conceptual frames, thereby enhancing their metaphorical competence. Developing learners’ metaphorical competence would help them in analyzing, reconstructing, and translating diverse textual data

PERUBAHAN MAKNA DALAM BERITA ARAB aljazeera.net

This study aims to determine the change in meaning in Arabic news. One of the media that presents news is aljazeera.net, which is a web page that presents news in Arabic and English. Research with this analytical method uses a type of text analysis research to find out changes in meaning in Arabic news sourced from aljazeera.net, with this method we can find out the types of changes in meaning in Arabic news that have been determined. In presenting the news there will certainly be a change in meaning. There are 7 kinds of changes in meaning in linguistics, namely (1) expansion or generalization (2) narrowing of meaning or specialization (3) elevation of meaning or amelioration (4) reduction of meaning or exploration and (5) synesthesia, (6) association and (7) ) metaphor. The results of our analysis show that in one of the news texts published on the Aljazeera.net website there are 5 changes in meaning.

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.

An optimal approach for text feature selection

Traditionally, feature selection is conducted by first deriving a candidate list of features, then ranking and selecting the top features based on predefined threshold. These methods are highly dependent on the choice of the threshold, and therefore lead to sub-optimal text categorization results. In this paper, we address the selection problem by suggesting a one-step method designed to optimally select the subset of features. The selection is formulated mathematically as an optimization problem with the objective of maximizing classification accuracy while simultaneously deriving and choosing the most discriminative features. Our method, MFX, is applicable to many of the conventional methods, with two distinguishing aspects. First, it is based on considering all documents from the same category as one extended document, instead of analyzing individual documents. Second, it considers choosing the most discriminative terms that are frequent and common across all documents of the same category, and minimally present in other categories. Moreover, MFX is language-independent. It was tested on the well-known benchmark Reuters RCV1 dataset. To showcase its language independence, MFX was also tested on Arabic datasets extracted from Arabic news sources. The results indicated that MFX always performed similar to or better than other well-known feature selection methods. MFX with a Support Vector Machine (SVM) classifier was also shown to outperform recent text classification algorithms based on neural networks and word embeddings.

Arabic Fake News Detection: A Fact Checking Based Deep Learning Approach

Fake news stories can polarize society, particularly during political events. They undermine confidence in the media in general. Current NLP systems are still lacking the ability to properly interpret and classify Arabic fake news. Given the high stakes involved, determining truth in social media has recently become an emerging research that is attracting tremendous attention. Our literature review indicates that applying the state-of-the-art approaches on news content address some challenges in detecting fake news’ characteristics, which needs auxiliary information to make a clear determination. Moreover, the ‘Social-context-based’ and ‘propagation-based’ approaches can be either an alternative or complementary strategy to content-based approaches. The main goal of our research is to develop a model capable of automatically detecting truth given an Arabic news or claim. In particular, we propose a deep neural network approach that can classify fake and real news claims by exploiting ‘Convolutional Neuron Networks’. Our approach attempts to solve the problem from the fact checking perspective, where the fact-checking task involves predicting whether a given news text claim is factually authentic or fake. We opt to use an Arabic balanced corpus to build our model because it unifies stance detection, stance rationale, relevant document retrieval and fact-checking. The model is trained on different well selected attributes. An extensive evaluation has been conducted to demonstrate the ability of the fact-checking task in detecting the Arabic fake news. Our model outperforms the performance of the state-of-the-art approaches when applied to the same Arabic dataset with the highest accuracy of 91%.

JointBert for Detecting Arabic Fake News

The rapid rise in the use of social media platforms has resulted in a recent surge of fake rumours, particularly among Arab countries. Such false information could potentially be detrimental to individuals and society. Detecting and blocking the spread of the fraudulent news in Arabic is critical. Many artificial intelligence algorithms, including contemporary transformer models, such as BERT, have been employed to detect the fake news in the past. Therefore, the fake news in Arabic can be detected using a revolutionary combined BERT architecture implemented in this paper. Extensive experiments were conducted to test the technique on real-world Arabic fake news datasets. In two of the fake news datasets, covid19fakes and Satirical, the suggested technique had a higher accuracy score than the current state-of-the-art Arabic fake news model. A comparable result can be achieved in other datasets; however, the proposed strategy fails to do so. All datasets except AraNews show an average F1 score improvement of 10% by implementing the proposed strategy. It was found that the proposed method was effective and superior to numerous other baselines of Arabic fake news detection models.