Arabic Messages Research Articles

Al-Madunat wa Syawty wa Tatbiquha fi Tarqiyatil Maharati Lughah ‘Arabiyah

This study aims to describe the benefits of blogs and spotify that can be used in developing Arabic language skills by expressing ideas or ideas in written form in Arabic so that they can improve skills reading and writing. Besides that, Spotify can also be used as a learning media
 which can improve students' listening skills.
 This research uses a qualitative method with a practical approach to improve reading & writing skills of PBA students at Nurul Jadid Paiton University. Various data collection techniques such as open interviews about the experience of using blogs & sporty, observing student reactions to the benefits of blogs & sporty in Arabic & students practicing fun drills. Then the collected data is analyzed inductively where the researcher parses, sorts out the main ones, interprets the data sequentially and finally becomes a complete & detailed conclusion.
 Research findings show that blogs can effectively improve students' writing and reading skills. By doing easy things such as writing a diary on a regular basis, it will increase vocabulary that can be used in Arabic so that gradually you will be able to write according to standard Arabic rules. Besides that, blogs can also improve reading skills, while spotify can improve listening skills, Arabic messages (simple conversation podcasts) are conveyed easily & attractively to listeners (mustami').

The 43rd Annual Session of St Petersburg Arabists

On April 19–21th, 2021, Institute of Oriental Manuscripts (Saint-Petersburg) hosted the 43rd Annual Session of St Petersburg Arabists in tribute to professor O. G. Bolshakov (1929 – 2020). The conference was held for two days under the chairmanship of Associate Professor S. A. Frantsuzov and included morning and afternoon sessions, which covered interesting reports by Arab scholars from St. Petersburg, Moscow and Kazan in person and online. Arabic messages were accompanied by scholarly discussions on various topics on the history of the Middle East and cultural linguistics. More than 35 speakers from different countries including Canada and Switzerland took part in the conference and were able not only to meet their colleagues, but also to take advantage of a spare day between the sessions and to visit unique sights St. Petersburg has to offer. MGIMO University was represented at the scientific conference by N. G. Antonova, lecturer of the Department of Middle East Languages, with a report on the topic The History of Arabisms in the Spanish Language devoted to the analysis of words borrowings, their introduction into the Spanish language, main groups and levels at which the linguistic process took place. A. O. Bolshakov and F. A. Asadullin delivered their reports about a prominent historian and Arabist O. G. Bolshakov who dedicated his life to the study of the history of Caliphate and Islam. Researchers in the field of linguistic cultural studies spoke about Moscow Arabs, development of the theory of jihad, and various aspects of the Arabic language. A lot of speakers touched upon the topic of religion, including pre-islamic beliefs, and the Quran, its translations and editions kept in the museum collections. The representatives of Saint Petersburg State University gave reports on a wide range of linguistic, historic and ethnographic issues. A. A. Mokrushina made an interactive presentation on the special aspects of commercials in Arab countries. Participation in the session of St. Petersburg Arabists served as a valuable opportunity to make a presentation of one’s current research to colleagues-Arabists, to carry out professional and scientific communication, to hold scientific discussions and to gain a deeper understanding of the history and culture of the Arabic language.

A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect

In this paper, we propose a semi-supervised approach for sentiment analysis of Arabic and its dialects. This approach is based on a sentiment corpus, constructed automatically and reviewed manually by Algerian dialect native speakers. This approach consists of constructing and applying a set of deep learning algorithms to classify the sentiment of Arabic messages as positive or negative. It was applied on Facebook messages written in Modern Standard Arabic (MSA) as well as in Algerian dialect (DALG, which is a low resourced-dialect, spoken by more than 40 million people) with both scripts Arabic and Arabizi. To handle Arabizi, we consider both options: transliteration (largely used in the research literature for handling Arabizi) and translation (never used in the research literature for handling Arabizi). For highlighting the effectiveness of a semi-supervised approach, we carried out different experiments using both corpora for the training (i.e. the corpus constructed automatically and the one that was reviewed manually). The experiments were done on many test corpora dedicated to MSA/DALG, which were proposed and evaluated in the research literature. Both classifiers are used, shallow and deep learning classifiers such as Random Forest (RF), Logistic Regression(LR) Convolutional Neural Network (CNN) and Long short-term memory (LSTM). These classifiers are combined with word embedding models such as Word2vec and fastText that were used for sentiment classification. Experimental results (F1 score up to 95% for intrinsic experiments and up to 89% for extrinsic experiments) showed that the proposed system outperforms the existing state-of-the-art methodologies (the best improvement is up to 25%).

A content analysis of Arabic YouTube videos for cancer treatment

Purpose The purpose of this paper is to describe and assess Arabic videos related to cancer treatment to gain insights about the nature of health information as it is shared on YouTube. Accordingly, future strategies for different bodies are suggested to promote effective communication. Design/methodology/approach The approach is to select a representative sample of YouTube videos for certain search terms related to cancer treatment in the Arabic language. In order to identify the search terms, Google Trends is utilized. To retrieve the most relevant videos, a simple python tool is developed using YouTube API V3. For this study, the first 150 relevant videos are quantitatively and qualitatively analyzed. Objective data and subjective data are collected for each video and analyzed. Objective data include video title, URL, length, view count, like count, dislike count, comment count and the associated tags. For content analysis, coding themes are defined for the subjective data as follows: video format, video authorship and video content. Video content includes three categories: types of treatments, targeted part and evidence-based indicators. Findings The study included 150 videos, from which 30 videos were not content related; therefore, 120 videos remain in the analysis. Using rounding values, it can be observed that the average video lasted 10 min, had 184,966 views, was commented on 263 times, was liked by 2,295 users and disliked by 148 users. Non-professional individuals (46 percent) posted less than half of the videos, whereas public institutions posted only 18 percent of videos. More than half of videos (56 percent) promoted using herbal, botanical, and other natural products for cancer treatment. The majority of YouTube video formats were videos (52 percent), followed by audio with captions (30 percent). News and stories were the dominant videos, with (16 percent), and other types of videos were mostly testimonials and private centers promotions. Only 6 and 9 percent of videos targeted the genetic and immune systems, respectively. Out of the 120 analyzed videos, 86 percent did not mention any risk factor for the recommended treatment, and 73 percent did not offer the details of their usage direction. Research limitations/implications Researchers need to understand the information that is currently available on social media platforms related to the high-risk diseases in order to design initiatives, tools, and actions to allow an easy effective transfer of knowledge. Practical implications Recounting in-depth knowledge of YouTube cancer treatment contents will allow policy makers, YouTube management, medical organizations, and government agencies to understand the viewers’ behavior of YouTube and their needs to provide accurate and trustworthy information to adopt evidence-based resources. Social implications Creating the suitable content, in terms of health promotion strategies, associated with the appropriate format and understandable language that people need will be one of the major responsibilities of YouTube management, government and professional bodies. The well-designed health messages will enhance users’ engagement and attention to health issues from trusted sources. Originality/value There is very less information about Arabic messages in social media, YouTube in particular, specifically regarding cancer treatment. Thus, this study is one of the first studies to explore how Arabic messages are presented on YouTube. The aim of the assessment is to extract the current status and suggest future strategies for different bodies to have effective communication toward the Arabic communities.

Using Classification Techniques to SMS Spam Filter

SMS is service that uses mobile phone that allows the users to exchange textual content. Spamming can be defined as sending unwanted content to a group of people for various purposes such as fraud. SMS spam is one form of spamming in which unwanted messages are delivered to many clients by spammers. Therefore, it has become necessary to develop SMS spam detection system to keep up with the current development of message services. Where the aim of this work is developing spam filter for Arabic and English languages by using two filter to be able to detect spam sms efficiently. Content based method was used to build spam filter for English and Arabic languages. based on this method, there are a number of steps should be taken which are Read English and Arabic dataset, Preprocessing phase, Feature Extraction and Classification. The first step after reading the dataset for Arabic and English languages is preprocessing phase which is important step to get more accurate results. The next step is extracting the features from the body of each message. Eight features have been extracted from English messages and six features from Arabic messages. Then features of messages for English and Arabic languages are splitted into two set: training set and testing set. Training set are used to train the algorithms while the test set are used evaluate the performance of proposed Spam filter for the English and Arabic language. In proposed system two classifiers are used. Naive Bayes is used as first classifier and neural network as second classifier. The incoming messages are passed through naive Bayes classifier. If it is classified as ham then passes to second classifier to make sure if it is spam, otherwise it doesn’t passes to second classifier. The results of the proposed system were acceptable with 97% accuracy is obtained for English language when using eight features and 80% from dataset for training .And 95% accuracy is obtained for Arabic language with six features and 70% from dataset for training.

Evaluating Rule-Based and Statistical Filters to Detecting Arabic E-Mail Alert Messages

Detecting and filtering e-mail alerts that are related to criminal or terrorist activities is of great interest for both security agencies and people. This paper evaluates and compares the performance of both the rule-based filter and Paul Graham statistical filter for detecting alerts in Arabic e-mail messages. To evaluate the two filters, a set of 1500 Arabic messages related to criminal activities were collected manually from some news websites such as Al-Jazeera Net and BBC Arabic news. The e-mails have been preprocessed, normalized, and then the relevant features were extracted from the collected e-mails by involving categorical proportional difference (CPD) and term frequency variance (TFV) as features weighting methods for the rule-based filter. To test the performance of the two filters, several experiments have been conducted and the result show that the Paul Graham statistical filter was more accurate. It was able to detect about 85% of the e-mail alerts used in the experiments. The rule-based filter has achieved 80% accuracy using the CPD method and 70% accuracy using the TFV method.

A hybrid approach to detecting alerts in Arabic e-mail messages

Detecting alert e-mails received daily by millions of subscribers from online news providers is a relatively new area of research which falls within the e-mail filtering field of research. Alert e-mails may address government, political issues, breaking news, and criminal attacks. This article proposes a hybrid approach based on both the Graham statistical filter and rule-based filters to detect and filter Arabic alert e-mails. The approach is basically language-independent. To test the performance of the proposed approach, several experiments have been conducted using a set of 1500 Arabic messages related to criminal activities collected manually from some news websites such as Al-Jazeera Net and BBC Arabic news. The results showed that the proposed approach has achieved a competitive performance in terms of accuracy, precision, and F-measure, where about 87% of the messages tested have been correctly detected and filtered by the proposed filter.

Text messaging in Kuwait. Is the medium the message?

Abstract In Kuwait the use of the mobile phone has spread rapidly and universally and the use of text messaging has become popular among the younger members of society. One aim of the paper was (by means of a questionnaire) to elicit some basic information about users' attitudes and practices with regard to the medium in their culture. Until comparatively recently it was not technically possible to send Arabic text messages using the Arabic alphabet and they had to be sent using the English alphabet. Another aim, therefore, was to ascertain whether Kuwaitis still use the English alphabet for Arabic text messages or whether they make use of the Arabic keypad now available. It was found that while Arabic messages are sent, a substantial percentage of messages use English and a mixture of Arabic and English. Of particular interest is how users use English characters to transcribe their Arabic texts. This raises linguistic and cultural questions which are explored in the paper.

Applying Authorship Analysis to Extremist-Group Web Forum Messages

The speed, ubiquity, and potential anonymity of Internet media - email, Web sites, and Internet forums - make them ideal communication channels for militant groups and terrorist organizations. Analyzing Web content has therefore become increasingly important to the intelligence and security agencies that monitor these groups. Authorship analysis can assist this activity by automatically extracting linguistic features from online messages and evaluating stylistic details for patterns of terrorist communication. However, authorship analysis techniques are rooted in work with literary texts, which differ significantly from online communication. To explore these problems, we modified an existing framework for analyzing online authorship and applied it to Arabic and English Web forum messages associated with known extremist groups. We developed a special multilingual model - the set of algorithms and related features - to identify Arabic messages, gearing this model toward the language's unique characteristics. Furthermore, we incorporated a complex message extraction component to allow the use of a more comprehensive set of features tailored specifically toward online messages. Evaluating the linguistic features of Web messages and comparing them to known writing styles offers the intelligence community a tool for identifying patterns of terrorist communication.