Waheed M. A. Altohami

This study investigates the speech act of complimenting on Facebook within the Egyptian context, exploring how gender is performed and negotiated through these linguistic exchanges. It employs a mixed-methods, digital ethnographic approach to analyze a corpus of 240 compliment-response sequences collected from the public Facebook profiles of young Egyptians. Theoretically, the study integrates Politeness Theory with Theory of Gender Performativity, proposing a critical-performative framework in which politeness strategies function as citational practices that constitute gender identity. The findings revealed distinct gendered patterns in both verbal and nonverbal complimenting behaviors. Men predominantly offer appearance-based compliments, often employing metaphorical language, while women’s compliments are more varied, covering performance and personality. Nonverbal compliments, particularly through Facebook’s ‘Like’ and ‘Love’ reactions, emerge as frequent but ambiguous politeness strategies. Compliment responses also showed gendered variation, with women often employing deflection or amendment strategies. The findings also suggested that online compliment exchanges should not be approached as simple phatic expressions. Instead, they are effective performative acts through which Egyptian youth cite, reproduce, and occasionally subvert traditional gender ideologies. This study offers a novel, empirically grounded analysis of the dynamic interplay between language, gender, and technology in a contemporary Middle Eastern context.

This study compares three plagiarism detection systems (Rabin-Karp, KNN, and Word2Vec) to measure their effectiveness in detecting cross-lingual plagiarism in Arabic literary texts translated from English. The dataset consisted of an Arabic translation of Daly Walker’s ‘I am the Grass’ (2012) conducted by the authors and evaluated by three translators with experience of more than ten years. It is divided into 60 percent directly translated, 30 percent paraphrased, and 10 percent original content. Findings showed that KNN achieved the highest precision in detecting cross-lingual plagiarism (26.7%), while Word2Vec performed best with paraphrased content (16.7%). Additionally, Rabin-Karp was most reliable in detecting original content (80% precision); however, all three systems demonstrated low overall accuracy (23–26%). These findings highlight the limitations of current systems when applied to Arabic texts, primarily due to the language’s morpho-syntactic and lexical complexities. Given the limited scope of the study, as it analyzes a single text, it recommends expanding to multiple genres for broader generalizability. Furthermore, the study recommends the development of more sophisticated, hybrid plagiarism detection systems and the development of rich Arabic corpora to enhance their performance.

This study investigates the way re-branding traditional Egyptian food-related terms is strategically used by an emerging socio-economic class to assert their class identity in digital milieus. Specifically, it analyzes the strategies employed for the Englishization and hybridization of culinary culture and examines the underlying socio-cultural motivations and dynamics driving such an innovative linguistic practice. Drawing on insights from sociolinguistics and sociology, we analyzed two data sets collected by means of an online survey and semi-structured interviews. Findings revealed that there is an emerging exclusive socio-economic class that manifests itself through specific lexical preferences based on diverse types of lexical borrowings. Additionally, the Englishization of traditional Egyptian food-related terms partially drove the emerging class’s rejection of social equality and solidarity with collective socio-cultural Egyptian identity. Such a class division, primarily initiated on digital platforms, was found to be motivated by complex factors at play, including age, gender, educational level, lifestyle, technology usage, etc. The findings of this study have implications for understanding the intersection of language, class identity, and technology in contemporary Egyptian society.

This study aimed at assessing the performance and efficacy of the retrieval information (IR) systems implemented in three widely used search engines (Google, Bing, and Yahoo), specifically with regard to the challenge of word sense disambiguation in Arabic texts. Such a challenge has been confirmed to negatively influence the retrieval of the most relevant documents. Therefore, we extended the paradigm of using computational methods and natural language processing (NLP) tools, primarily tailored for processing English texts, to explore morphosyntactic as well as lexical issues disturbing the accuracy of Arabic IR systems. Findings revealed striking disparities in the efficacy of IR systems integrated into these search engines, which can be attributed to four principal challenges: (a) the intricate morpho-syntactic structures inherent in Arabic; (b) the idiosyncratic orthographical system of the Arabic script; (c) the multifaceted semantic flexibility of certain lexical elements; and (d) the intriguing diaglossic nature of Arabic, allowing for the coexistence of multiple linguistic varieties within a single discourse situation. Drawing from these findings, a series of solutions rooted in supervised machine learning techniques, including clustering models and adaptations based on geographic locations, are proposed. Moreover, the study advocates for the capacity of search engines to interpret queries across all Arabic varieties, encompassing vernacular dialects. Furthermore, the importance of search engines accommodating queries irrespective of the specific language adopted by users is underscored. While the research primarily centers on Arabic, its implications resonate beyond this language alone. By applying computational methodologies originally designed for English to Arabic, the study not only addresses the challenges specific to Arabic IR systems but also contributes valuable insights that transcend linguistic boundaries. Through a comparative lens, issues like word sense disambiguation between Arabic and English are juxtaposed, extracting lessons that can inform advancements in information retrieval for both languages.

This study compared the salient features of the three basic types of automatic text summarisation methods (ATSMs)—extractive, abstractive, and real-time—along with the available approaches used for each type. The data set comprised 12 reports on the current issues on automatic text summarisation methods and techniques across languages, with a special focus on Arabic whose structure has been largely claimed to be problematic in most ATSMs. Three main summarizers were compared: TAAM, OTExtSum, and OntoRealSumm. Further to this, a humanoid version of the summary of the data set was prepared, and then compared to the automatically generated summary. A 10-item questionnaire was built to help with the assessment of the target ATSMs. Also, Rouge analysis was performed to assess the efficacy of all techniques in minimising the redundancy of the data set. Findings showed that the precision of the target summarizers differed considerably, as 80% of the data set has been proven to be aware of the problems underlying ATSMS. The remaining parameters were in the normal range (65–75%). In light of the equations-based assessment of ATSMS, the highest range was noted with the removal of stop word, the least range was noted with POS tagging, stem weight, and stem collection. Regarding Arabic, the statistical analysis has been proven to be the most effective summarisation method (accuracy = 57.59%; reminiscence = 58.79%; F-Value = 57.99%). Further research is required to explore how the lexicogrammatical nature of languages and generic text structure would affect the text summarisation process.

The present study conducts a cognitive-semantic analysis of etymologically-related lexical pairs in the language of the Qur’an. Based on objective selection criteria, the lexical pair rīĥ (a singular form literally meaning wind) and riyāĥ (a plural form literally meaning winds) has been claimed to be representative of the final data set, which includes 20 lexical pairs. Building on the theoretical underpinnings of descriptive semantics and frame semantics, the analysis sought to fulfill three main objectives: (a) identifying the referential range of the pair rīĥ and riyāĥ and its central reference point; (b) explaining the way the historical and theological context helps to mark the connotations associated with each sense in the referential range; and (c) sorting the discourse functions communicated through the cognitive frame(s) evoked by such a lexical pair. Findings showed that the central cognitive frame of both lexical items is that of moving air. However, rīĥ has developed, through metaphorical extension, two other senses that are not applicable to riyāĥ: power/predominance and smell. Finally, both lexical items have been proven to have positive, negative, and neutral connotations that have been divinely manipulated to perform various discourse functions.

Increased attention has been directed toward utilizing visual modes because linguistic modes cannot accurately depict L2 learners’ metaphorical conceptions of their language-acquisition experiences. The goal of this study was to investigate the mental images that Saudi EFL students may have in mind while learning a foreign language. To this end, both verbal and nonverbal metaphorical conceptions were investigated. An experimental group of 12 students was randomly selected. Both the learners’ written responses and drawn descriptions were evaluated to tap into the learners’ mental representations of foreign language learning. The multimodality-based analysis of the upper-intermediate learners’ metaphors demonstrated that the EFL learners’ metaphorical verbal and non-verbal representations had the same perspectives on learning a foreign language. The learners’ positive attitudes were also revealed by the extracted metaphorical concepts. Most of the metaphorical representations showed language learning as an exciting and uniquely personal experience. Few metaphorical representations showed that learning English was an awful experience.