Language, Technology, and Social Media

Social Media Addiction and Adolescent Mental Health: The Mediating Role of Cognitive Interference in Digital Communication

Kavita Sharma — 2025-04-20

This study investigates the relationships among social media addiction, cognitive interference, and mental health outcomes within the context of digital communication among adolescents aged 13–19 in Chandigarh, India. Utilizing a cross-sectional design, data were collected from 541 participants who completed the Bergen Social Media Addiction Scale (BSMAS), a computerized Stroop Test to assess cognitive interference, and the Depression, Anxiety, and Stress Scale (DASS-21). Results indicated moderately high levels of social media addiction (M = 4.2, SD = 1.1), substantial cognitive interference (incongruent trials: M = 850 ms), and moderate psychological distress across all mental health indicators. Correlation analyses revealed that social media addiction was positively associated with cognitive interference (r = 0.45, p < 0.01) and negatively associated with mental health outcomes (r = -0.38, p < 0.01). Cognitive interference was also significantly and negatively correlated with mental health (r = -0.42, p < 0.01), indicating that increased cognitive disruption due to digital engagement exacerbates emotional distress. Mediation analysis confirmed that cognitive interference partially mediated the relationship between social media addiction and mental health, accounting for approximately 35% of the total effect. These findings underscore cognitive interference as a critical psychological mechanism linking problematic social media use to adverse mental health outcomes among adolescents. The study contributes region-specific empirical evidence to the global discourse on digital media effects and highlights the urgency of implementing targeted interventions such as cognitive training and digital literacy programs to mitigate the cognitive and emotional risks associated with excessive digital communication in adolescent populations.

Effect of Social Media Language on Students’ Intention to Engage in Academic Use Behavior at Leading Universities in Nepal

Gunja Sah — 2025-06-09

Social media plays a central role in students' daily academic activities across the globe. This study investigates how social media language influences students’ intention to engage in academic use behavior at leading universities in Nepal. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), a structured questionnaire was distributed to 235 students from Tribhuvan University, Kathmandu University, Purbanchal University, and Pokhara University using stratified sampling. Structural Equation Modeling (SEM) was employed to analyze the data. Results reveal that performance expectancy significantly influences both behavioral intention and actual use behavior. Effort expectancy and social influence significantly affect use behavior but not behavioral intention, while facilitating conditions show no significant influence on either. Behavioral intention mediates the relationship between UTAUT constructs and use behavior. The findings highlight the importance of social media language in shaping students' academic engagement, offering practical insights for educators, policymakers, and platform developers to enhance social media’s educational utility.

From Isolation to Interaction: The Role of Language and Digital Communication Tools in Empowering Adolescent Virtual Collaboration

Shazia Hamid — 2025-07-04

This qualitative study explores the impact of collaborative learning on adolescent learners' motivation and feelings of isolation in virtual learning environments, with a focus on the role of language-mediated interactions and digital tools. Using exploratory questionnaires and interviews, the study examines how virtual collaborative tasks influence students' engagement. The findings indicate that collaborative learning significantly enhances motivation, particularly when tasks are meaningful and peer interactions are supportive. Survey data revealed that 42.4% of students often found collaborative tasks productive, with positive peer influence acting as a key motivator. Additionally, 70% of participants reported a reduction in isolation due to peer interaction. Autonomy in collaborative tasks was also strongly linked to higher intrinsic motivation, supporting Self-Determination Theory. Synchronous tools such as video conferencing and shared documents were crucial in alleviating isolation. The study highlights how digital tools mediate language use, fostering social connection and engagement in virtual settings. It contributes new insights into the integration of digital tools for language learning, collaboration, and social engagement, offering practical recommendations for virtual education practices, curriculum design, and teacher training, with implications for policymakers to enhance online learning strategies.

English Language and Literature Teachers on the Integration of Artificial Intelligence in the Philippine Classroom Context

Jaliyah Ashirae S. Peneyra — 2025-07-05

Artificial intelligence (AI) in language education has brought significant changes to the academic system, reshaping the landscape of language teaching and learning, especially how language teachers plan, deliver, and assess their students’ academic journey. This study determined the academic and personal experiences of eight English language and literature teachers on technology integration such as AI and explored the positive and negative influence of integrating AI in teaching and learning. Utilizing a phenomenological qualitative approach, participants were purposively selected and interviewed, guided by 10 open-ended questions. Data were coded and analyzed thematically, revealing key findings: AI for improved teaching and enhanced learning; encountered challenges and risks; ethical concerns; and shifts in teaching strategies and adaptation. Integration of AI in the classroom resulted in a positive impact on teaching and learning English language and literature, depending on critical engagement, responsible use, and teaching strategies to ensure the preservation of essential skills in English language and literature and academic integrity. As AI continues to influence educational practices, teachers play a crucial role in maintaining balance and guiding students to engage critically with AI-generated contents.

Morphological Productivity and Lexical Innovation in Swahili: Digital Communication and Language Transformation in Social Media Spaces

Prisca Boniphace Makulilo — 2025-07-09

Swahili, as a major lingua franca in East Africa, is undergoing significant transformation through digital communication, particularly on social media platforms. This study explores the morphological and lexical innovations emerging in digital Swahili, with a focus on affixation, compounding, and reduplication. Employing a qualitative approach, the research draws data from 150 social media posts, interviews, and group discussions involving 15 Swahili speakers from urban and rural contexts. The findings reveal increased morphological productivity, with speakers integrating foreign lexical items into Swahili through creative adaptation. Code-mixing and hybrid expressions are also prevalent, reflecting both linguistic innovation and sociocultural identity negotiation. While these developments enhance the expressiveness of Swahili in digital settings, they raise challenges related to language standardization and intergenerational comprehension. This study offers new insights into Swahili’s adaptation to digital environments, highlighting its potential as both a technological and cultural medium. The findings inform ongoing discussions in language policy, digital literacy, and the preservation of African languages in globalized communication.

Mapping The Ethical Discourse in Generative Artificial Intelligence: A Topic Modeling Analysis of Scholarly Communication

Georgios P. Georgiou — 2025-07-11

As generative artificial intelligence (GenAI) continues to reshape numerous sectors, the ethical implications of its deployment have become a critical area of society. This study employs a topic modeling technique to systematically analyze a large corpus of peer-reviewed literature related to ethics in GenAI, aiming to uncover prevailing themes and conceptual patterns. Using a natural language processing method, the analysis identified ten distinct topics that were subsequently synthesized into six overarching macrotopics related to ethics in GenAI: (1) education and learning, (2) bias, frameworks, and legal compliance, (3) cybersecurity, governance, and risks, (4) social impact and sustainability, (5) digital transformation and emerging tech, and (6) academic research and writing. Each macrotopic is examined through the lens of existing scholarly literature, providing an overview of current debates in the area. The findings demonstrate the pressing need for comprehensive governance frameworks that prioritize transparency, fairness, and accountability in the development and deployment of GenAI systems. The relevance of the emerging macrotopics extends beyond technical or policy considerations, situating them within broader ethical discourse around language, communication, and media technologies, where questions of authorship, representation, and discursive power are increasingly mediated by GenAI. By mapping the evolving ethical landscape of GenAI, this study contributes to a more informed and critical understanding of how to align technological innovation with societal values and legal norms.

The Price of Fame on X: A Systematic Review of Social and Cultural Consequences of Cyberbullying of Celebrities on X

Portia Motshware — 2025-08-09

This systematic review examines the socio-cultural consequences of cyberbullying targeting celebrities on X, addressing a gap in current research. The increasing prevalence of cyberbullying in the digital era, especially on social media platforms like X, has significant implications for public figures. The review synthesizes findings from 23 peer-reviewed studies published between 2020 and 2025, following the PRISMA framework and employing thematic analysis using Atlas.ti. The research identifies the psychological, personal, and professional impacts of cyberbullying on celebrities, particularly focusing on how the platform's design, including features such as anonymity and rapid information dissemination, exacerbates harmful behaviors. The findings highlight the critical role that X's structure plays in amplifying cyberbullying, with negative consequences such as anxiety, depression, and social isolation for victims. Additionally, the review reveals how celebrities' public personas and constant exposure to online criticism significantly affect their mental health and professional lives. The study also discusses the broader socio-cultural implications, including the normalization of hostility and the public's complex responses to celebrities' victimization. Based on these insights, the review recommends stricter platform regulations and increased support for affected individuals, including the involvement of celebrity managers and healthcare professionals. This research contributes to the understanding of cyberbullying's socio-cultural dynamics on X, offering new perspectives on how digital abuse affects public figures and emphasizing the need for safer online spaces.

Key Topics and Case Studies in Pragmatics: Examples of Corpus-based Research

Mohammad Amin Hawamdeh — 2025-08-12

This study examines the reliability and applicability of corpus-based methods for investigating pragmatic phenomena, with a focus on the Arabic–English language pair. Three case studies are presented: (1) the conventionality of language in Quranic translations, (2) conversational implicature in sacred text dialogue, and (3) hedging strategies in political discourse. Adopting a qualitative case study design supported by corpus-driven analysis, the research integrates thematic interpretation with quantitative frequency data to uncover context-sensitive patterns in meaning construction. The findings demonstrate that operationalizing stylistic conventions in sacred texts enables replicable annotation frameworks for both translation studies and digital religious discourse; that implicature analysis can reveal culturally nuanced meaning negotiation relevant to online communication; and that hierarchical mapping of hedging functions provides a transferable model for monitoring rhetorical strategies in both traditional and digital political messaging. This integration advances corpus pragmatics beyond descriptive analysis toward predictive and comparative modeling, offering methodological and theoretical contributions applicable across multilingual, cross-cultural, and technology-mediated contexts. The study underscores the value of corpus-based pragmatics as a unifying framework for understanding language use in hybrid communicative environments, from religious translation platforms to social media political discourse.

Culture in Advertisements: A Discursive and Semiotic Analysis of Maestro Pizza Advertisements in Saudi Arabia

Hajer Mohammed Albogami — 2025-08-30

Advertisements have long been acknowledged not merely as tools for product promotion but as powerful cultural agents that reflect and shape societal values. This study critically examines the cultural and ideological underpinnings embedded in Maestro Pizza advertisements aired on Saudi National Days 88, 89, and 91. Employing Fairclough’s Critical Discourse Analysis (CDA) and Barthes’ Semiotic Theory, this research investigates twelve selected visual frames from three advertisements to uncover how Saudi cultural identities, historical figures, and social norms are represented. The findings highlight the prominence of cultural symbols such as national landmarks, historical narratives, and the evolving image of Saudi women, illustrating how these advertisements align with national ideologies and social conventions. This study underscores the significance of advertising as a medium for cultural transmission, offering new insights into how advertisements can reflect and perpetuate national identity in the context of Saudi Arabia. The implications of these findings contribute to the broader field of media studies and cultural representation in advertising.

Advances and Challenges in Artificial Intelligence and Machine Learning for Multilingual Speech Disorder Diagnosis and Therapy: An Integrative Review

Husnain Mukhdoom — 2025-08-31

This study examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in the diagnosis, assessment, and management of speech disorders, focusing on enhancing clinical practices. A comprehensive review of existing studies was conducted, highlighting the application of AI technologies such as Automated Speech Recognition (ASR), Natural Language Processing (NLP), and deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The findings reveal that AI and ML techniques have significantly improved diagnostic precision, therapeutic interventions, and clinical efficiency, especially in underserved populations. However, challenges related to limited multilingual datasets, model generalizability, and the interpretability of deep learning models were identified. Despite the promising advancements, challenges such as data privacy, model bias, and ethical concerns need to be addressed for broader clinical integration. This study contributes by synthesizing current AI and ML applications in speech disorder management, identifying key challenges, and proposing future directions, including the development of multilingual datasets, Explainable AI (XAI), and the integration of multimodal data to further enhance diagnostic and therapeutic outcomes.