Journal articles: 'Arabic social media'

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Alwakid, Ghadah, Taha Osman, Mahmoud El Haj, Saad Alanazi, Mamoona Humayun, and Najm Us Sama. "MULDASA: Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media." Applied Sciences 12, no. 8 (April 9, 2022): 3806. http://dx.doi.org/10.3390/app12083806.

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The semantically complicated Arabic natural vocabulary, and the shortage of available techniques and skills to capture Arabic emotions from text hinder Arabic sentiment analysis (ASA). Evaluating Arabic idioms that do not follow a conventional linguistic framework, such as contemporary standard Arabic (MSA), complicates an incredibly difficult procedure. Here, we define a novel lexical sentiment analysis approach for studying Arabic language tweets (TTs) from specialized digital media platforms. Many elements comprising emoji, intensifiers, negations, and other nonstandard expressions such as supplications, proverbs, and interjections are incorporated into the MULDASA algorithm to enhance the precision of opinion classifications. Root words in multidialectal sentiment LX are associated with emotions found in the content under study via a simple stemming procedure. Furthermore, a feature–sentiment correlation procedure is incorporated into the proposed technique to exclude viewpoints expressed that seem to be irrelevant to the area of concern. As part of our research into Saudi Arabian employability, we compiled a large sample of TTs in 6 different Arabic dialects. This research shows that this sentiment categorization method is useful, and that using all of the characteristics listed earlier improves the ability to accurately classify people’s feelings. The classification accuracy of the proposed algorithm improved from 83.84% to 89.80%. Our approach also outperformed two existing research projects that employed a lexical approach for the sentiment analysis of Saudi dialects.

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Alnaeb, Jamal, Issam Salman, and Mohamad Bassam Kurdy. "Arabic Semantic Classifier of Arabic Social Media "Twitter" Users." Asian Journal of Information Technology 18, no. 1 (October 5, 2019): 20–27. http://dx.doi.org/10.36478/ajit.2019.20.27.

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Albantani, Azkia Muharom. "Social Media as Alternative Media for Arabic Teaching in Digital Era." ALSINATUNA 4, no. 2 (June 25, 2019): 148. http://dx.doi.org/10.28918/alsinatuna.v4i2.2043.

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The article aims to reveal the role of electronic social media such as youtube, instagram, and facebook as alternative media for Arabic teaching. The article employed a qualitative method through literature approach revealing phenomenon that are developing in the Arabic teaching. It is true that the three social media are alternative media that can be used by teachers in teaching Arabic. Students of elementary to secondary school, further students of college commonly use the three social media. The use of social media in the learning process is very likely may improve students’ motivation and interest. In addition, Arabic teaching will of course not bordered by space since it can be conducted anywhere

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Hegazi, Mohamed Osman, Yasser Al-Dossari, Abdullah Al-Yahy, Abdulaziz Al-Sumari, and Anwer Hilal. "Preprocessing Arabic text on social media." Heliyon 7, no. 2 (February 2021): e06191. http://dx.doi.org/10.1016/j.heliyon.2021.e06191.

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zayed, manal, hamdi mousa, and Mohamed Elmenshawy. "Sentiment Analysis for Arabic Social Media." IJCI. International Journal of Computers and Information 7, no. 1 (October 15, 2020): 14–31. http://dx.doi.org/10.21608/ijci.2020.16170.1004.

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Mallek, Fatma, Billal Belainine, and Fatiha Sadat. "Arabic Social Media Analysis and Translation." Procedia Computer Science 117 (2017): 298–303. http://dx.doi.org/10.1016/j.procs.2017.10.121.

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Hathlian, Nourah F. Bin, and Alaaeldin M. Hafez. "Subjective Text Mining for Arabic Social Media." International Journal on Semantic Web and Information Systems 13, no. 2 (April 2017): 1–13. http://dx.doi.org/10.4018/ijswis.2017040101.

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The need for designing Arabic text mining systems for the use on social media posts is increasingly becoming a significant and attractive research area. It serves and enhances the knowledge needed in various domains. The main focus of this paper is to propose a novel framework combining sentiment analysis with subjective analysis on Arabic social media posts to determine whether people are interested or not interested in a defined subject. For those purposes, text classification methods—including preprocessing and machine learning mechanisms—are applied. Essentially, the performance of the framework is tested using Twitter as a data source, where possible volunteers on a certain subject are identified based on their posted tweets along with their subject-related information. Twitter is considered because of its popularity and its rich content from online microblogging services. The results obtained are very promising with an accuracy of 89%, thereby encouraging further research.

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Tartir, Samir, and Ibrahim Abdul-Nabi. "Semantic Sentiment Analysis in Arabic Social Media." Journal of King Saud University - Computer and Information Sciences 29, no. 2 (April 2017): 229–33. http://dx.doi.org/10.1016/j.jksuci.2016.11.011.

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Sari, Risna Rianti, and Albar Adetary Hasibuan. "Students’ Perception toward Social Media Assisted Language Learning (SMALL) for Arabic Learning." Izdihar : Journal of Arabic Language Teaching, Linguistics, and Literature 2, no. 2 (October 31, 2019): 101. http://dx.doi.org/10.22219/jiz.v2i2.9911.

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The aims of this research were to determine the perception of students of Arabic Language Department towards the use of social media in teaching Arabic language and determine social media often used by students to help in the process of teaching Arabic language. This study used social anthropology approach in qualitative research because this study focusses on student’s perceptions towads mobile as their assistant for learning Arabic. The subject of study are 32 Arabic language education students at the State Islamic University Maulana Malik Ibrahim Malang. The results of the study show that social media can be used as a tool for language learning Arabic, can improve students' skills, and help them better interact with lecturers and classmates.

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Kurniati, Depi. "Penggunaan Media Sosial dalam Pembelajaran Bahasa Arab dengan Model Blended Learning." Ta'limi | Journal of Arabic Education and Arabic Studies 1, no. 2 (August 20, 2022): 119–38. http://dx.doi.org/10.53038/tlmi.v1i2.32.

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Limited face-to-face meetings are a new phase in learning Arabic after the number of victims of COVID-19. The blended learning model that integrates face-to-face learning with online learning becomes a learning model that is applied in schools. To assist teachers in maximizing students' Arabic learning outcomes, the use of social media is the most recommended alternative media. The purpose of this article is to describe the use of social media in learning Arabic with a blended learning model and its advantages and disadvantages in learning. This article is included in qualitative research with the type of literature review that produces descriptive data regarding the use of social media in learning Arabic with a blended learning model. This study explains that various social media applications can be developed to learn Arabic. Social media encourages students' interest in learning Arabic through its refreshing display and features. Its easy and practical use makes learning can be done from anywhere and anytime so that the objectives of learning Arabic will be achieved.

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.., Hani D., Ahmed A. Khamees, and Said A. Salloum. "Opinion mining for Arabic dialect in social media: A systematic review." International Journal of Advances in Applied Computational Intelligence 1, no. 2 (2022): 08–28. http://dx.doi.org/10.54216/ijaaci.010201.

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The huge text generated on social media in Arabic, especially the Arabic dialect becomes more attractive for Natural Language Processing (NLP) to extract useful and structured information that benefits many domains. The more challenging point is that this content is mostly written in an Arabic dialect, and the problem with these dialects it has no written rules like Modern Standard Arabic (MSA) or traditional Arabic, and it is changing slowly but unexpectedly. One of the ways to benefit from this huge data is opinion mining, so we introduce this systematic review for opinion mining from Arabic text dialect for the years from 2016 until 2019. We have found that Saudi, Egyptian, Algerian, and Jordanian are the most studied dialects even if it is still under development and need a bit more effort, nevertheless, dialects like Mauritanian, Yemeni, Libyan, and somalin have not been studied in this period; also we have found the main methods that show a good result is the last four years.

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Alothman, Manal Othman, Muhammad Badruddin Khan, and Mozaherul Hoque Abul Hasanat. "Review of Researches on Arabic Social Media Text Mining." Journal of Intelligent Systems and Computing 2, no. 1 (March 31, 2021): 20–33. http://dx.doi.org/10.51682/jiscom.00201005.2021.

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Social media sites and applications have allowed people to share their comments, opinions, and point of views in different languages on mass scale. Arabic language is one of the languages that has seen huge surge in production of its digital textual content. The Arabic content and its metadata are a goldmine of useful information for a wide variety of applications. A large number of researchers are working on Arabic data in various domains of research such as natural language processing, sentiment analysis, event detection, named entity recognition, etc. This article presents a review of number of such studies conducted between 2014 and 2019 using their data sources from social media websites. We found that Twitter was the most used source to contribute data for dataset construction for Arabic text mining researchers. Our study also found that the Sport Vector Machine (SVM) and Naïve Bayesian (NB) classifiers were the most used classifiers in the previous researches. Moreover, the results of the previous studies indicate that SVM classifier provided the best performance compared to other classifiers.

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A. Hnaif, Adnan, Emran Kanan, and Tarek Kanan. "Sentiment Analysis for Arabic Social Media News Polarity." Intelligent Automation & Soft Computing 28, no. 1 (2021): 107–19. http://dx.doi.org/10.32604/iasc.2021.015939.

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Alwagait, Esam. "Arabic Observatory for Websites and Social Media (AOWSM)." International Journal of Knowledge Society Research 5, no. 2 (April 2014): 1–6. http://dx.doi.org/10.4018/ijksr.2014040101.

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Ranking of the web sites and organizations is vital to maintain the quality of service and obtain improvements where possible. Arabic Observatory for Websites and Social Media (AOWSM) is a system that takes into consideration the web presence of organizations and ranks them based on different metrics and standards that are described later in the paper. This paper presents an introductory study about the AOWSM and describes its working, limitations, and modules. AOWSM is set to make its way for becoming a standard in the ranking of the organizations based on their quality of web services, presence, and standards.

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Al-Ghadir, Abdul Rahman I., Abdullatif Alabdullatif, and Aqil M. Azmi. "Gender Inference for Arabic Language in Social Media." International Journal of Knowledge Society Research 5, no. 4 (October 2014): 1–10. http://dx.doi.org/10.4018/ijksr.2014100101.

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The widespread usage of social media has attracted a new group of researchers seeking information on who, what and, where the users are. Some of the information retrieval researchers are interested in identifying the gender, age group, and the educational level of the users. The objective of this work is to identify the gender in the Arabic posts in the social media. Most of the works related to gender classification has been for English based content in the social media. Work for other languages, such as Arabic, is almost next to none. Typically people express themselves in the social media using colloquial, so this study is geared towards the identification of genders using the Saudi dialect of the Arabic language. To solve the gender identification problem the authors, a novel method called k-Top Vector (k-TV), which is based on the k-top words based on the words occurrences and the frequency of the stems, was introduced. Part of this work required compiling a dataset of Saudi dialect words. For this, a well-known widely used social site was relied on. To test the system, we compiled 1200 samples equally split between both genders. The authors trained Support Vector Machine (SVM) and k-NN classifiers using different number of samples for training and testing. SVM did a better job and achieved an accuracy of 95% for gender classification.

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Nur, Abdul Jawat. "Bentuk dan Fungsi Tulisan Tato Arab di Media Sosial." An Nabighoh: Jurnal Pendidikan dan Pembelajaran Bahasa Arab 21, no. 01 (July 19, 2019): 20. http://dx.doi.org/10.32332/an-nabighoh.v21i01.1530.

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This paper will describe the form of lingual units and the function of writing on Arabic tattoos on social media. The phenomenon of Arabic tattoo writing is interesting to study because based on the data found, it is known that there are various types of Arabic tattoo writing, such as words, phrases, and sentences. In addition, it was also found the function of Arabic tattoo writing on social media, including the form of self-expression or group expression. This research was carried out through three strategic stages, namely: the stage of data provision, data analysis, and presentation of the results of data analysis. Based on the research that has been done, it is concluded that on social media found Arabic tattoo writing in the form of words, phrases, and sentences. When viewed from the function of the language, Arabic tattoo writing has the functions of (1) fatigue, (2) referential, and (3) imaginative.

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Guellil, Imane, Ahsan Adeel, Faical Azouaou, Sara Chennoufi, Hanene Maafi, and Thinhinane Hamitouche. "Detecting hate speech against politicians in Arabic community on social media." International Journal of Web Information Systems 16, no. 3 (July 31, 2020): 295–313. http://dx.doi.org/10.1108/ijwis-08-2019-0036.

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Purpose This paper aims to propose an approach for hate speech detection against politicians in Arabic community on social media (e.g. Youtube). In the literature, similar works have been presented for other languages such as English. However, to the best of the authors’ knowledge, not much work has been conducted in the Arabic language. Design/methodology/approach This approach uses both classical algorithms of classification and deep learning algorithms. For the classical algorithms, the authors use Gaussian NB (GNB), Logistic Regression (LR), Random Forest (RF), SGD Classifier (SGD) and Linear SVC (LSVC). For the deep learning classification, four different algorithms (convolutional neural network (CNN), multilayer perceptron (MLP), long- or short-term memory (LSTM) and bi-directional long- or short-term memory (Bi-LSTM) are applied. For extracting features, the authors use both Word2vec and FastText with their two implementations, namely, Skip Gram (SG) and Continuous Bag of Word (CBOW). Findings Simulation results demonstrate the best performance of LSVC, BiLSTM and MLP achieving an accuracy up to 91%, when it is associated to SG model. The results are also shown that the classification that has been done on balanced corpus are more accurate than those done on unbalanced corpus. Originality/value The principal originality of this paper is to construct a new hate speech corpus (Arabic_fr_en) which was annotated by three different annotators. This corpus contains the three languages used by Arabic people being Arabic, French and English. For Arabic, the corpus contains both script Arabic and Arabizi (i.e. Arabic words written with Latin letters). Another originality is to rely on both shallow and deep leaning classification by using different model for extraction features such as Word2vec and FastText with their two implementation SG and CBOW.

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Alawadh, Husam M., Amerah Alabrah, Talha Meraj, and Hafiz Tayyab Rauf. "Attention-Enriched Mini-BERT Fake News Analyzer Using the Arabic Language." Future Internet 15, no. 2 (January 22, 2023): 44. http://dx.doi.org/10.3390/fi15020044.

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Internet use resulted in people becoming more reliant on social media. Social media have become the main source of fake news or rumors. They spread uncertainty in each sector of the real world, whether in politics, sports, or celebrities’ lives—all are affected by the uncontrolled behavior of social media platforms. Intelligent methods used to control this fake news in various languages have already been much discussed and frequently proposed by researchers. However, Arabic grammar and language are a far more complex and crucial language to learn. Therefore, work on Arabic fake-news-based datasets and related studies is much needed to control the spread of fake news on social media and other Internet media. The current study uses a recently published dataset of Arabic fake news annotated by experts. Further, Arabic-language-based embeddings are given to machine learning (ML) classifiers, and the Arabic-language-based trained minibidirectional encoder representations from transformers (BERT) is used to obtain the sentiments of Arabic grammar and feed a deep learning (DL) classifier. The holdout validation schemes are applied to both ML classifiers and mini-BERT-based deep neural classifiers. The results show a consistent improvement in the performance of mini-BERT-based classifiers, which outperformed ML classifiers, by increasing the training data. A comparison with previous Arabic fake news detection studies is shown where results of the current study show greater improvement.

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Fkih, Fethi, Tarek Moulahi, and Abdulatif Alabdulatif. "Machine Learning Model for Offensive Speech Detection in Online Social Networks Slang Content." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 20 (January 17, 2023): 7–15. http://dx.doi.org/10.37394/23209.2023.20.2.

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The majority of the world’s population (about 4 billion people) now uses social media such as Facebook, Twitter, Instagram, and others. Social media has evolved into a vital form of communication, allowing individuals to interact with each other and share their knowledge and experiences. On the other hand, social media can be a source of malevolent conduct. In fact, nasty and criminal activity, such as cyberbullying and threatening, has grown increasingly common on social media, particularly among those who use Arabic. Detecting such behavior, however, is a difficult endeavor since it involves natural language, particularly Arabic, which is grammatically and syntactically rich and fruitful. Furthermore, social network users frequently employ Arabic slang and fail to correct obvious grammatical norms, making automatic recognition of bullying difficult. Meanwhile, only a few research studies in Arabic have addressed this issue. The goal of this study is to develop a method for recognizing and detecting Arabic slang offensive speech in Online Social Networks (OSNs). As a result, we propose an effective strategy based on the combination of Artificial Intelligence and statistical approach due to the difficulty of setting linguistic or semantic rules for modeling Arabic slang due to the absence of grammatical rules. An experimental study comparing frequent machine learning tools shows that Random Forest (RF) outperforms others in terms of precision (90%), recall (90%), and f1-score (90%).

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Najadat, Hassan, Mohammad A. Alzubaidi, and Islam Qarqaz. "Detecting Arabic Spam Reviews in Social Networks Based on Classification Algorithms." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 1 (January 31, 2022): 1–13. http://dx.doi.org/10.1145/3476115.

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Reviews or comments that users leave on social media have great importance for companies and business entities. New product ideas can be evaluated based on customer reactions. However, this use of social media is complicated by those who post spam on social media in the form of reviews and comments. Designing methodologies to automatically detect and block social media spam is complicated by the fact that spammers continuously develop new ways to leave their spam comments. Researchers have proposed several methods to detect English spam reviews. However, few studies have been conducted to detect Arabic spam reviews. This article proposes a keyword-based method for detecting Arabic spam reviews. Keywords or Features are subsets of words from the original text that are labelled as important. A term's weight, Term Frequency–Inverse Document Frequency (TF-IDF) matrix, and filter methods (such as information gain, chi-squared, deviation, correlation, and uncertainty) have been used to extract keywords from Arabic text. The method proposed in this article detects Arabic spam in Facebook comments. The dataset consists of 3,000 Arabic comments extracted from Facebook pages. Four different machine learning algorithms are used in the detection process, including C4.5, kNN, SVM, and Naïve Bayes classifiers. The results show that the Decision Tree classifier outperforms the other classification algorithms, with a detection accuracy of 92.63%.

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Moudjari, Leila, Farah Benamara, and Karima Akli-Astouati. "Multi-level embeddings for processing Arabic social media contents." Computer Speech & Language 70 (November 2021): 101240. http://dx.doi.org/10.1016/j.csl.2021.101240.

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Fahmy, Ahmed. "Detecting Offensive Language in Multi-Dialectal Arabic Social Media." International Journal of Data Science and Big Data Analytics 2, no. 1 (May 5, 2022): 20–25. http://dx.doi.org/10.51483/ijdsbda.2.1.2022.20-25.

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KUCHERENKO, Alina. "Sociopragmatic Peculiarities of «Infodemic» in Arabic Social Media Discourse." Linguistic and Conceptual Views of the World, no. 72(2) (2022): 41–56. http://dx.doi.org/10.17721/2520-6397.2022.2.04.

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The term «infodemic» is nowadays used by the WHO to describe the excessive flows of inaccurate and unreliable data about the coronavirus both in the virtual and real worlds. This phenomenon is reflected in the social media posts containing the misleading information that involves fake news, rumours, nonchecked «facts», users’ thoughts, emotional reactions to different events, other online posts or messages. The previous researches include the creation of various Arabic Covid-19 misinformation datasets. However, a more in-depth analysis of the online discourse is needed due to the lack of its linguistic, sociolinguistic and communicative studies. This paper deals with the sociopragmatic aspect of the online communication in the Arab world during the Covid-19 pandemic. The communicants’ social features are presented within their Facebook and Twitter accounts. We manually collected nearly 100 online posts on Facebook and Twitter. We analysed the users’ communicative goals, and, especially, the linguistic tools utilized to achieve those goals, as well as the language variation caused by different communicative purposes of the «infodemic» posts. The key words of the data research are represented with the pandemic related lexemes of Twitter hashtags, such as kūrūnā «corona», liqāḥ «vaccine», dawāʼ «remedy», etc. We classified the studied publications according to the following topics: pharmaceutical companies’ profits; denial of the role of vaccination and the preventive measures; persuasion of the effective drugs existence; health tips. As speech acts, the studied posts involve such types as representatives (fake news, pseudofacts, etc.), directives (health advises), and expressives (attitudes, emotions, thoughts, etc.). On the other hand, the given texts are regarded as the acts of disagreement (explicit or implicit). The explicit means of negation is represented with the grammatical particles, meanwhile the implicit instruments include the lexis with the negative emotional expressivity or negative connotations. It was also noticed, that some male texts use the negative lexicon more frequently, than the female ones. The language variation reveals the relation between the post’s communicative purpose and the code choice (MSA is preferred for the representative posts (to demonstrate the credibility), ESA (Educated Spoken Arabic) is used in all types of «infodemic» posts, Colloquial Arabic as the language of everyday communication is mostly present in the expressives).

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Alsafari, Safa, Samira Sadaoui, and Malek Mouhoub. "Hate and offensive speech detection on Arabic social media." Online Social Networks and Media 19 (September 2020): 100096. http://dx.doi.org/10.1016/j.osnem.2020.100096.

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Abdul-Mageed, Muhammad, Mona Diab, and Sandra Kübler. "SAMAR: Subjectivity and sentiment analysis for Arabic social media." Computer Speech & Language 28, no. 1 (January 2014): 20–37. http://dx.doi.org/10.1016/j.csl.2013.03.001.

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Alomari, Ebtesam, Iyad Katib, Aiiad Albeshri, Tan Yigitcanlar, and Rashid Mehmood. "Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning." Sensors 21, no. 9 (April 24, 2021): 2993. http://dx.doi.org/10.3390/s21092993.

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Digital societies could be characterized by their increasing desire to express themselves and interact with others. This is being realized through digital platforms such as social media that have increasingly become convenient and inexpensive sensors compared to physical sensors in many sectors of smart societies. One such major sector is road transportation, which is the backbone of modern economies and costs globally 1.25 million deaths and 50 million human injuries annually. The cutting-edge on big data-enabled social media analytics for transportation-related studies is limited. This paper brings a range of technologies together to detect road traffic-related events using big data and distributed machine learning. The most specific contribution of this research is an automatic labelling method for machine learning-based traffic-related event detection from Twitter data in the Arabic language. The proposed method has been implemented in a software tool called Iktishaf+ (an Arabic word meaning discovery) that is able to detect traffic events automatically from tweets in the Arabic language using distributed machine learning over Apache Spark. The tool is built using nine components and a range of technologies including Apache Spark, Parquet, and MongoDB. Iktishaf+ uses a light stemmer for the Arabic language developed by us. We also use in this work a location extractor developed by us that allows us to extract and visualize spatio-temporal information about the detected events. The specific data used in this work comprises 33.5 million tweets collected from Saudi Arabia using the Twitter API. Using support vector machines, naïve Bayes, and logistic regression-based classifiers, we are able to detect and validate several real events in Saudi Arabia without prior knowledge, including a fire in Jeddah, rains in Makkah, and an accident in Riyadh. The findings show the effectiveness of Twitter media in detecting important events with no prior knowledge about them.

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Hejazi, Hani D., and Ahmed A. Khamees. "Opinion mining for Arabic dialect in social media data fusion platforms: A systematic review." Fusion: Practice and Applications 9, no. 1 (2022): 08–28. http://dx.doi.org/10.54216/fpa.090101.

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The huge text generated on social media in Arabic, especially the Arabic dialect becomes more attractive for Natural Language Processing (NLP) to extract useful and structured information that benefits many domains. The more challenging point is that this content is mostly written in an Arabic dialect with a big data fusion challenge, and the problem with these dialects it has no written rules like Modern Standard Arabic (MSA) or traditional Arabic, and it is changing slowly but unexpectedly. One of the ways to benefit from this huge data fusion is opinion mining, so we introduce this systematic review for opinion mining from Arabic text dialect for the years from 2016 until 2019. We have found that Saudi, Egyptian, Algerian, and Jordanian are the most studied dialects even if it is still under development and need a bit more effort, nevertheless, dialects like Mauritanian, Yemeni, Libyan, and somalin have not been studied in this period. Many data fusion models that show a good result is the last four years have been discussed.

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Elzayady, Hossam, Mohamed S. Mohamed, Khaled M. Badran, and Gouda I. Salama. "Detecting Arabic textual threats in social media using artificial intelligence: An overview." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (March 1, 2022): 1712. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1712-1722.

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Recent studies show that social media has become an integral part of everyone's daily routine. People often use it to convey their ideas, opinions, and critiques. Consequently, the increasing use of social media has motivated malicious users to misuse online social media anonymity. Thus, these users can exploit this advantage and engage in socially unacceptable behavior. The use of inappropriate language on social media is one of the greatest societal dangers that exist today. Therefore, there is a need to monitor and evaluate social media postings using automated methods and techniques. The majority of studies that deal with offensive language classification in texts have used English datasets. However, the enhancement of offensive language detection in Arabic has gotten less consideration. The Arabic language has different rules and structures. This article provides a thorough review of research studies that have made use of artificial intelligence (AI) for the identification of Arabic offensive language in various contexts.

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Alzahrani, Abdullah Ibrahim Abdullah, and Syed Zohaib Javaid Zaidi. "Recent developments in information extraction approaches from Arabic tweets on social networking sites." International Journal of ADVANCED AND APPLIED SCIENCES 9, no. 9 (September 2022): 145–52. http://dx.doi.org/10.21833/ijaas.2022.09.018.

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Information extraction from Arabic tweets has attracted the attention of researchers due to the huge data accessibility for the swift expansion of social media platforms. With the increasing use of social web applications, information extraction from the various platforms has gained importance for understanding the trending post and events predictions based on those sentiments written by the users on certain news feeds. The Arabic Language is mostly used in Middle Eastern and African countries and most users tweet on social media using the Arabic language, therefore Arabic text classification and sentiment analysis aimed to predict information extraction from social media platforms. This research provides a more detailed critical review of the information extraction presented in the literature focused on using different tools, methods, and techniques like k-NN, support vector machines, Naïve Bayes, and other machine learning tools for the data extraction and processing.

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Alruily, Meshrif. "Classification of Arabic Tweets: A Review." Electronics 10, no. 10 (May 12, 2021): 1143. http://dx.doi.org/10.3390/electronics10101143.

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Text classification is a prominent research area, gaining more interest in academia, industry and social media. Arabic is one of the world’s most famous languages and it had a significant role in science, mathematics and philosophy in Europe in the middle ages. During the Arab Spring, social media, that is, Facebook, Twitter and Instagram, played an essential role in establishing, running, and spreading these movements. Arabic Sentiment Analysis (ASA) and Arabic Text Classification (ATC) for these social media tools are hot topics, aiming to obtain valuable Arabic text insights. Although some surveys are available on this topic, the studies and research on Arabic Tweets need to be classified on the basis of machine learning algorithms. Machine learning algorithms and lexicon-based classifications are considered essential tools for text processing. In this paper, a comparison of previous surveys is presented, elaborating the need for a comprehensive study on Arabic Tweets. Research studies are classified according to machine learning algorithms, supervised learning, unsupervised learning, hybrid, and lexicon-based classifications, and their advantages/disadvantages are discussed comprehensively. We pose different challenges and future research directions.

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Awane, Widad, El Habib Ben Lahmar, and Ayoub El Falaki. "Hate Speech in the Arab Electronic Press and Social Networks." Revue d'Intelligence Artificielle 35, no. 6 (December 28, 2021): 457–65. http://dx.doi.org/10.18280/ria.350603.

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Nowadays we are witnessing an open world, characterized by globalization which is accompanied by a technology through which information circulates without borders, especially with the widespread use of social networking sites being the most common communication tool, that gives access through various applications to a large space for the presentation of multiple ideas, including extremist ideas, and the spread of hate speech. This paper introduces a system of detection of hate speech in the texts of Arabic read media and social media, which is based on a combined use of NLP, and machine learning methods. The training of the detection model is done on a large Dataset of articles, tweets and comments, collected, balanced and tokenized afterwards using BERT in Arabic. The trained model detects hate speech in Arabic and various Arabic based dialects, by classifying the texts into two classes: Neutral and Abusive. The above-mentioned model is evaluated using precision metrics, recall and f1 score, it has reached an accuracy of 83%.

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Nurcholis, Ahmad, Muhamad Asngad Rudisunhaji, Timbul Timbul, Heri Efendi, and Siti Marpuah. "Social Reality-Based Arabic Language Learning in Islamic Senior High School." Arabiyat : Jurnal Pendidikan Bahasa Arab dan Kebahasaaraban 9, no. 1 (June 30, 2022): 16–29. http://dx.doi.org/10.15408/a.v9i1.25374.

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Social reality is an interesting issue in Arabic learning in madrasas because students can connect the subject matter with the phenomenon in the community. The purpose of this study was to analyze the success of MA Sirojut Tholibin Bacem Sutojayan Blitar in implementing social reality-based Arabic learning. It is qualitative research with an exploratory approach and phenomenological analysis. This study used the theory of social reality initiated by Paul Ricoeur (in 1973). The principal of Paul Ricoeur said that human actions in social reality are symbols of language, while these symbols invite certain thoughts and interpretations. The results of this study indicated that MA Sirojut Tholibin had succeeded in implementing social reality-based Arabic learning. The students’ Arabic language competence is increasing through social reality approaches such as learning ethics, juvenile delinquency, and the influence of social media. The learning media used included news articles, newspapers, websites, and scientific articles.

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Gemilang, Damar, and Hastuti Listiana. "Teaching Media in the Teaching of Arabic Language/ Media Pembelajaran dalam Pembelajaran Bahasa Arab." ATHLA : Journal of Arabic Teaching, Linguistic and Literature 1, no. 1 (December 23, 2020): 49–64. http://dx.doi.org/10.22515/athla.v1i1.3048.

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This article discusses the media of learning Arabic language, through library studies that focus on distributing material effectively to students without making them boring. The limited creations and variations in learning as well as the low ability of Arabic language maharah of students make the role of the media so important. The selection of media correctly, can improve the mastery of material skills, motivate, and stimulate students. Through library studies from journals, papers, and books on learning media for the Arabic curriculum which are then analyzed and concluded data will be obtained about learning media for the Arabic curriculum which is useful for teachers. There are several rules in determining the media that will be used by taking into account the direction and objectives of learning, types of learning strategies, understanding the characteristics of the media by the teacher, in terms of cost suitability, media readiness, quality, and environment to operate the media. In terms of functions related to sensing devices, the media are divided into visual media, audio media, and audiovisual media. Meanwhile, viewed from the viewpoint in Arabic and that maharah, the media can be grouped into learning media mufrodat, nahwu-shorof, and Arabic language skills consisting of media istima', qiro'ah, kitabah, and kalam. This can help academics in applying media correctly in the learning of the Arabic language curriculum.One of the elements of learning Arabic is the student's Arabic learning strategy. But there are still some students who have not found the right strategy for learning Arabic, because of a lack of knowledge about Arabic learning strategies, especially the Oxford model of language learning strategies. The purpose of this study was to describe the implementation of the Arabic language learning strategy for students in the Oxford model of Madrasah Ibitidaiyah Al Islam Kartasura students.This research is descriptive qualitative, as for the research site at Madrasah Ibitidaiyah Al Islam Kartasura. This research was conducted from March to June 2020. The subjects in this study were students of class V A at Madrasah Ibitidaiyah Al Islam Kartasura. The informants in this study were the fifth grade Arabic teachers at Madrasah Ibitidaiyah Al Islam Kartasura. Data collection methods used were interviews, observation and documentation.The conclusion of this study is that students use Arabic learning strategies in the Oxford model because there are indications that students are using their learning. The strategies used are memory strategies, cognitive strategies, compensation strategies, metacognitive strategies, affective strategies, and social strategies. The strategy that is often used by students is the memorial strategy.

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Ismail, Muhammad Marwan, Farah Nadia Harun, Wan Moharani Muhamad, Nurhasma Muhamad Saad, and Zulkipli Md Isa. "THE ARAB SPRING ONLINE NEWS COVERAGE: CORPUS-BASED ANALYSIS OF THE TUNISIAN AND EGYPTIAN REVOLUTION KEYWORDS." International Journal of Humanities, Philosophy and Language 4, no. 14 (June 15, 2021): 52–70. http://dx.doi.org/10.35631/ijhpl.414004.

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In 2011, the Arab world had become the centre of attention once again after the emergence of the so-called ‘Arab Spring’ in December 2010. This historical event in the modern history of the Arab region has brought significant social and political reform to the Arab world. The wave of Arab uprising begins in Tunisia at the end of 2010, rapidly separated into other neighbouring countries such as Egypt, Libya, Morocco, Syria, Bahrain, and Sudan. Since the early stage of protest, which mainly participated by locals, mass media has comprehensively reported this historical event, which brought down many Arab leaders in power for decades. Thus, Arab Spring has become the headline of many international media outlets, and the media are still discussing the significant impact of the event until now. Hence, the main objective of the study is to examine the event's Arabic online news discourse by focusing on the keywords and prominent social actors in the news reports surrounding the Arab Spring. This will indicate what has been included and excluded or highlighted and marginalised in the news coverage. The data is consist of Modern Standard Arabic (MSA) online news published by four prominent news outlets namely with different origin and background: Al-Arabia of Saudi Arabia, Al-Jazeera of Qatar, BBC Arabic of the UK and CNN of the USA. These well-established news outlets were selected for their comprehensive international coverage aims at various Arabic readers worldwide. The study employs corpus linguistics analytical tools by using corpus data mining software ‘AntConc 3.4’. Then, the quantitative results of corpus data will be analysed using a qualitative approach based on the textual-oriented Critical Discourse Analysis (CDA) of Fairclough (1992) and Wodak (2001). The result shows that news coverage of the event has highlighted several keywords that indicate the main social actors and main social events of the Arab Spring. These keywords are the shared command features among the news outlets, although each outlet portraying them is significantly different. Finally, the article presents suggestions for other related studies in the future.

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Riqza, Meidiana Sahara, and M. Muassomah. "Media Sosial untuk Pembelajaran Bahasa Arab pada Masa Pandemi: Kajian Kualitatif Penggunaan WhatsApp pada Sekolah Dasar di Indonesia." Alsina : Journal of Arabic Studies 2, no. 1 (July 17, 2020): 71. http://dx.doi.org/10.21580/alsina.2.1.5946.

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Learning in the era of 5.0 society has experienced rapid mediation, especially in educational program. Teachers began to innovate using various media, especially using WhatsApp in learning Arabic Language. Arabic language has been considered a very difficult and tedious subject for islamic elementary school children. Efforts and high creative power continue to be done to foster fun learning of Arabic language. The purpose of this study besides want to see how the process and application of WhatsApp social media in learning Arabic language is also to increase knowledge and insight for Arabic teachers in utilizing the development of technology and information at the islamic elementary school level. This research method uses qualitative research by using descriptive analysis. Data collection techniques in this study using observations, interviews, and document notes. The results of this research concluded that WhatsApp social media can facilitate learning and long distance communication between teachers and students when they are in a pandemic, create fun learning, train students' independence, and also have more values, an environmentally friendly. This research suggests that there is more supervision of students in applying WhatsApp social media to learning Arabic language at the islamic elementary school level.

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Elsaka, Tarek, Imad Afyouni, Ibrahim Hashem, and Zaher Al Aghbari. "Spatio-Temporal Sentiment Mining of COVID-19 Arabic Social Media." ISPRS International Journal of Geo-Information 11, no. 9 (September 2, 2022): 476. http://dx.doi.org/10.3390/ijgi11090476.

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Since the recent outbreak of COVID-19, many scientists have started working on distinct challenges related to mining the available large datasets from social media as an effective asset to understand people’s responses to the pandemic. This study presents a comprehensive social data mining approach to provide in-depth insights related to the COVID-19 pandemic and applied to the Arabic language. We first developed a technique to infer geospatial information from non-geotagged Arabic tweets. Secondly, a sentiment analysis mechanism at various levels of spatial granularities and separate topic scales is introduced. We applied sentiment-based classifications at various location resolutions (regions/countries) and separate topic abstraction levels (subtopics and main topics). In addition, a correlation-based analysis of Arabic tweets and the official health providers’ data will be presented. Moreover, we implemented several mechanisms of topic-based analysis using occurrence-based and statistical correlation approaches. Finally, we conducted a set of experiments and visualized our results based on a combined geo-social dataset, official health records, and lockdown data worldwide. Our results show that the total percentage of location-enabled tweets has increased from 2% to 46% (about 2.5M tweets). A positive correlation between top topics (lockdown and vaccine) and the COVID-19 new cases has also been recorded, while negative feelings of Arab Twitter users were generally raised during this pandemic, on topics related to lockdown, closure, and law enforcement.

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Jariyah, Ainun, and Imam Asrori. "Penggunaan Media Pembelajaran Bahasa Arab Daring dan Luring bagi Siswa Madrasah Tsanawiyah Kabupaten Malang." JoLLA: Journal of Language, Literature, and Arts 2, no. 8 (September 13, 2022): 1159–72. http://dx.doi.org/10.17977/um064v2i82022p1159-1172.

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Abstract: The purpose of this study is to describe the types of online and offline Arabic learning media, the use of online and offline Arabic learning media, and the advantages and disadvantages of using the learning media. This type of research is qualitative research with a descriptive approach based on field facts to determine the use of online and offline Arabic learning media. The results of the research and discussion show that there is data in the field that shows the types of online Arabic learning media, namely social media and e-learning platforms, social media includes WhatsApp, while the E-Learning platform includes Google Meet, Zoom Meeting, and school e-learning. Offline Arabic learning media include audio, visual, and audio-visual, visual media include pictures, whiteboards, and textbooks, while audio-visual media include videos and Power Points application. There is also data in the field that shows the use of online and offline Arabic learning media including for learning Arabic including presentation of mufrodat and its meaning, reading and explaining the text, delivering tarkib material, maharah kitabah, assignments, and reviews of work assignments. Keywords: learning media; Arabic; online; offline; Abstrak: Tujuan dari penelitian ini untuk mendeskripsikan jenis media pembelajaran daring (dalam jaringan) dan luring (luar jaringan) Bahasa Arab, penggunaan media pembelajaran daring dan luring bahasa Arab serta kelebihan dan kekurangan penggunaan media. Jenis penelitian ini adalah penelitian kualitatif dengan pendekatan deskriptif berdasarkan fakta lapangan untuk mengetahui penggunaan media pembelajaran Bahasa Arab daring dan luring. Hasil penelitian dan pembahasan menunjukkan bahwa terdapat data di lapangan yang menunjukkan jenis media pembelajaran bahasa Arab daring yaitu media sosial dan platform e-learning, media sosial meliputi WhatsApp, sedangkan platform e-learning meliputi Google Meet, Zoom Meeting, dan e-learning sekolah. Media pembelajaran bahasa Arab luring di antaranya media audio, media visual dan, media audio visual meliputi gambar, papan tulis dan buku ajar, sedangkan media audio visual meliputi video dan aplikasi Power Point. Terdapat juga data di lapangan yang menunjukkan penggunaan media pembelajaran bahasa Arab daring dan luring di antaranya untuk pembelajaran bahasa Arab meliputi penyajian mufrodat dan maknanya, pembacaan dan penjelasan teks, penyampaian materi tarkib, maharah kitabah, penugasan dan penjelasan pengerjaan tugas. Kata kunci: media pembelajaran; bahasa Arab; daring; luring

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Al-Rawi, Ahmed, and Jacob Groshek. "Jihadist Propaganda on Social Media." International Journal of Cyber Warfare and Terrorism 8, no. 4 (October 2018): 1–15. http://dx.doi.org/10.4018/ijcwt.2018100101.

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This article focuses on ISIS followers on Twitter in an effort to understand the nature of their social media propaganda. The research study provides unique insight into one of the largest data sets that investigates ISIS propaganda efforts on Twitter by examining over 50 million tweets posted by more than 8 million unique users that referenced the keywords “ISIS” or “ISIL.” The authors then searched this corpus for eight keywords in Arabic that included terms of support for ISIS and the names of different Al-Qaeda leaders. A mixed research method was used, and the findings indicate that ISIS activity on Twitter witnessed a gradual decline, but the group was still able to post different types of tweets to maintain its online presence. Also, the feud between ISIS and Al-Qaeda was intense, ongoing, and prevalent in online interactions among ISIS followers. The study provides an understanding of using big data to better grasp the propaganda activities of terrorist groups.

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Boulouard, Zakaria, Mariya Ouaissa, Mariyam Ouaissa, Moez Krichen, Mutiq Almutiq, and Karim Gasmi. "Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning." Applied Sciences 12, no. 24 (December 14, 2022): 12823. http://dx.doi.org/10.3390/app122412823.

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The democratization of access to internet and social media has given an opportunity for every individual to openly express his or her ideas and feelings. Unfortunately, this has also created room for extremist, racist, misogynist, and offensive opinions expressed either as articles, posts, or comments. While controlling offensive speech in English-, Spanish-, and French- speaking social media communities and websites has reached a mature level, it is much less the case for their counterparts in Arabic-speaking countries. This paper presents a transfer learning solution to detect hateful and offensive speech on Arabic websites and social media platforms. This paper will compare the performance of different BERT-based models trained to classify comments as either abusive or neutral. The training dataset contains comments in standard Arabic as well as four dialects. We will also use their English translations for comparative purposes. The models were evaluated based on five metrics: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix.

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Dwairej, Doa’a Abdullah, Hala Mahmoud Obeidat, Eqbal Mohammad Alfarajat, and Lubna Abdullah Dwairej. "Translation and Psychometric Testing of the Arabic Version of the Problematic Media Use Measure Short Form for Children." Human Behavior and Emerging Technologies 2022 (May 29, 2022): 1–8. http://dx.doi.org/10.1155/2022/4034602.

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Background. There is growing concerns that excessive use of media among children will become problematic. Research on the management of children’s problematic use of media would be improved if screening tools are widely applied. Problematic Media Use Measure Short Form (PMUM-SF) was developed to screen problematic use of media among 4- to 11-year-old US children. Purpose. The study is aimed at developing and validating a cross-cultural version of the PMUM-SF for use in Arabic-talking parents of children from age 3 to 13 years. Design. Cross-cultural adaptation and cross-sectional psychometric testing study. Methods. Using the World Health Organization (WHO) framework for instrument translation and adaptation, the instrument was translated, back translated, pretested, and reviewed by a committee. The PMUM-SF was tested in 180 parents of children aging from 3 to 13 years. Results. The results demonstrated that the Arabic version of the PMUM-SF had high reliability (Cronbach’s alpha was 0.90); it has good convergent and predictive validity. The factor structure of the Arabic version of PMUM-SF was confirmed through exploratory and confirmatory factor analysis (comparative fit index CFI = 0.93 ; goodness of fit index GFI = 0.90 ; incremental fit index IFI = 0.93 ). Conclusion. Because the Arabic version of the PMUM-SF seems to be reliable and valid in assessing problematic use of media of children in Arabic-speaking parents, the use of this translated version is recommended in future research.

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Abdulaziz AlArfaj, Abeer, Nada Ali Hakami, and Hanan Ahmed Hosni Mahmoud. "Predicting Violence-Induced Stress in an Arabic Social Media Forum." Intelligent Automation & Soft Computing 35, no. 2 (2023): 1423–39. http://dx.doi.org/10.32604/iasc.2023.028067.

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Davidaviciene, Vida, and Fadwa Chalfoun. "SOCIAL MEDIA NETWORKS USE IN COMMUNICATION ANALYSES OF ARABIC COUNTRIES." Marketing and Digital Technologies 2, no. 2 (June 17, 2018): 10–20. http://dx.doi.org/10.15276/mdt.2.2.2018.1.

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Elimam, Somaia, and Mohamed Bougeussa. "An evaluation dataset for depression detection in Arabic social media." International Journal of Knowledge Engineering and Data Mining 7, no. 1/2 (2021): 113. http://dx.doi.org/10.1504/ijkedm.2021.119888.

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Elimam, Somaia, and Mohamed Bouguessa. "An Evaluation Dataset for Depression Detection in Arabic Social Media." International Journal of Knowledge Engineering and Data Mining 7, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijkedm.2021.10042777.

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Al-Kabi, Mohammed N., Heider A. Wahsheh, and Izzat M. Alsmadi. "Polarity Classification of Arabic Sentiments." International Journal of Information Technology and Web Engineering 11, no. 3 (July 2016): 32–49. http://dx.doi.org/10.4018/ijitwe.2016070103.

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Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

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Al-Shlool, Safaa. "(Im) Politeness and Gender in the Arabic Discourse of Social Media Network Websites: Facebook as a Norm." International Journal of Linguistics 8, no. 3 (June 13, 2016): 31. http://dx.doi.org/10.5296/ijl.v8i3.9301.

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The present study aims to investigate the differences and similarities in the ways men and women use (im)politeness strategies in communicating “online” in the Arabic discourse of social media network websites like Facebook as well as the role of the topic the interlocutors talk about in the use of (im)politeness strategies. In addition, the study investigates the differences between the men-men, women-women, women-men communication in the Arabic discourse of social media network website, Facebook. For the purposes of this study, a corpus of online Arabic texts were collected from some public web pages of the most popular TV show programs on some of the most well-liked social media network websites such as Facebook over a period of four months (from September 2012- December 2012). The obtained data were studied quantitatively and qualitatively. Many studies have been conducted on cross-gender differences especially in the computer mediated communication CMC, but none so far has focused on the gender differences and (im)politeness in the Arabic discourse of social media network websites although there is a huge number of Arabic users of such websites. The present study, therefore, attempts to fill in the gap in the literature.

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Wibowo, Budi Santoso. "Pedagogical Competence of Arabic Language Lecturers at Islamic University." Lughawiyyat: Jurnal Pendidikan Bahasa dan Sastra Arab 4, no. 1 (September 2, 2021): 59–73. http://dx.doi.org/10.38073/lughawiyyat.v4i1.431.

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Abstract This study aims to explore more deeply about the pedagogical competence of Arabic language lecturers at Islamic University. This study used a descriptive qualitative approach with case study type of research. For data collection, the researcher used three data collection techniques, namely: (1) observation, (2) in-depth interview, and (3) documentation study. The results of this study lead to a conclusion that the pedagogical competencies of Arabic language lecturers are: firstly, the ability to understand the characteristics of students. The strategies are: (1) introduction at the beginning of the lecture, (2) daily in-depth observations, and (3) placement tests (ikhtibār al-tashnīf). This understanding of student characteristics helps lecturers in optimizing the achievement of learning objectives, planning, implementing and evaluating learning. Secondly, the ability to utilize technology in learning. Two of the technology-based infrastructure used by lecturers are the Arabic Language Laboratory and the Computer and Multimedia Laboratory. In addition, lecturers also utilize technology in the form of social media as an alternative media in learning Arabic, namely: (1) Instagram, (2) Youtube, (3) Facebook, (4) Zoom Cloud Meeting, and (5) WhatsApp Group. The benefits of utilizing technology for learning Arabic are : assisting and simplifying the learning process, making learning more flexible both in terms of time and place, making students think creatively, growing self-confidence for students, assisting to improve the four language skills, and making students able to wisely utilize technology, especially technology in the form of social media. Researchers hope that with this research, Arabic language lecturers can understand better the characteristics of their students and can utilize technology in learning, including alternative media in learning Arabic, that is technology in the form of social media. Keywords: Pedagogical Competence, Arabic Lecturer, Islamic University

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Lian, Chaoqun. "Arabic language learning anxiety in Chinese social media: a study of discursive habitus and language symbolism." Onomázein Revista de lingüística filología y traducción, no. 9 (2021): 88–104. http://dx.doi.org/10.7764/onomazein.ne9.06.

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Arabic language learning anxiety (AA) is common among Arabic learners in China. Its causes lie beyond the language and its structural features per se but elsewhere in the sociopolitical world. Analyzing discussions on Arabic learning on the Chinese social media site Zhihu and identifying the discursive habitus, i.e., statements recurrently made and strategies recurrently used in these discussions, this paper shows that AA is both a symptom of and a reaction to the entanglement of Arabic via language symbolism in three longue dureé sociopolitical circumstances: the building of the modern Chinese nation, the redefining of the Muslim constituents of the Chinese national identity, and power negotiation in the modern world-system of knowledge.

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Al-Menayes, Jamal. "Psychometric Properties and Validation of the Arabic Social Media Addiction Scale." Journal of Addiction 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/291743.

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This study investigated the psychometric properties of the Arabic version of the SMAS. SMAS is a variant of IAT customized to measure addiction to social media instead of the Internet as a whole. Using a self-report instrument on a cross-sectional sample of undergraduate students, the results revealed the following. First, the exploratory factor analysis showed that a three-factor model fits the data well. Second, concurrent validity analysis showed the SMAS to be a valid measure of social media addiction. However, further studies and data should verify the hypothesized model. Finally, this study showed that the Arabic version of the SMAS is a valid and reliable instrument for use in measuring social media addiction in the Arab world.

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Muhammad Ridwan, Ghita Lusiana Dewi,. "PEMILIHAN DAN PENGGUNAAN BAHASA ARAB OLEH MAHASISWA UNIVERSITAS CANAL SUEZ MESIR." Jurnal CMES 9, no. 1 (June 14, 2017): 22. http://dx.doi.org/10.20961/cmes.9.1.11722.

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This research aims to describe students preference and the use of Arabic language at the academia, social interactions and media. The data were taken from 24 respondents who are still studying in Suez Canal University in Egypt. The main instrument used in this research named Discourse Completion Test (DCT), in the form of three questionnaires distributed randomly to the selected students as the sample. This research used indirect method with Likert scale to measure the language attitude. The preference and the use of Arabic by students in the field of academic, social interaction, and the media showed a varying result. In field of academic and media, students tend to choose and use BAF, while in the field of social interactions students tend to choose and use BAA.The departments that have the highest percentage on the election and use of the Arabic in the academic are Math, Chemistry, Hospitality, Arabic, and History. Then, in the social interaction field, the percentage of preference and use of BAA by male students is higher than women. These results indicate that departments and gender had a role in the preference and use of the Arabic by students.

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Journal articles on the topic 'Arabic social media'

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