Search
25 articles for “social media trends”
-
The Impact of Social Media on Mental Health
Abstract: This study explores the multifaceted influence of social media on Generation Z, focusing on both its positive and negative impacts. On one hand, social media platforms provide Gen Z with essential tools for social connection, emotional support, self-expression, and the creation of online communities. These digital spaces often serve as outlets for creativity and identity exploration. On the other hand, excessive or unhealthy use of these platforms has been linked …
Published in Journal of Web Engineering & Technology · Vol. 12, Issue 2, 2025 · pp. 1–5 Read article
-
A Mathematical Perspective on Recent Cloud-Computing Trends for Scalable and Secure Social-Media Platforms
Abstract: Cloud computing underpins modern social-media platforms by providing elastic compute, storage, and data-processing pipelines capable of absorbing highly bursty workloads. This paper surveys recent cloud-native trends—serverless and event-driven design, container orchestration, edge/CDN offload, streaming analytics, and privacy-enhancing security controls—and formalizes their impact through a compact mathematical model. We express workload volatility using arrival-rate functions, use queueing-based capacity sizing to derive auto-scaling rules, and formulate an optimization objective that balances cost …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 35–40 Read article
-
Data Mining for E-Commerce and Social Media: Insights and Future Research Directions
Abstract: The fast expansion of e-commerce and social media has heralded a new era of data-rich settings, with enormous quantities of user interactions, preferences, and transactions generated on a daily basis. Data mining has developed as a critical strategy for leveraging big datasets, allowing businesses to gain concrete knowledge and drive decision-making. Data mining in e-commerce improves operational efficiency and user pleasure by allowing for personalized recommendations, consumer segmentation, fraud detection, …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 1, 2025 · pp. 14–23 Read article
-
Detecting Fake Accounts on Social Media Using Machine Learning
Abstract: The growing frequency of fake accounts on social media platforms underscores the critical necessity for effective detection methods. In response to this challenge, our study leverages state-of-the-art machine learning techniques to identify and counter deceptive entities effectively. By conducting a thorough analysis of social media data, our approach unveils intricate patterns indicative of fraudulent accounts, enabling proactive measures against them. Through the application of advanced algorithms, we present a comprehensive …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 11, Issue 2, 2024 · pp. 21–32 Read article
-
A Comparative Study of Deep Learning Methods for Depression Detection in Social Media Data
Abstract: With the rise of social media platforms like Twitter, Reddit, and Facebook, individuals increasingly share personal information about their moods, behaviors, and mental states. This trend provides a unique opportunity to leverage large-scale textual data for understanding and monitoring mental health conditions, particularly depression, a prevalent and challenging mental health issue. Traditional depression assessments are often confined to clinical environments and lack the capacity for real-time monitoring. In contrast, social …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 55–65 Read article
-
Social Media Analysis Using Big Data
Abstract: Social media has become an essential aspect of everyday life across different age groups, serving functions ranging from sharing personal updates to staying informed about social developments. Our objective is to harness big data to extract meaningful insights from the immense and ever-expanding volume of data generated on social media platforms. We will collect and analyze data from various social media sources, including text, images, and user interactions, using cutting-edge …
Published in Journal of Advanced Database Management & Systems · Vol. 11, Issue 3, 2024 · pp. 35–46 Read article
-
Phubbing, Social Anxiety and Perceived Control in Young Adults
Abstract: This study examines the complex interactions among ten empirical studies on social anxiety, phubbing (phone snubbing), and perceived control. Psychological research has focused a great deal of attention on social anxiety, which is characterized by the fear of being negatively evaluated in social situations. Phubbing, the practice of people prioritizing their phones over interpersonal interactions, has become more common in recent years due to the widespread use of smartphones. This …
Published in International Journal of Behavioral Sciences · Vol. 2, Issue 2, 2025 · pp. 18–25 Read article
-
Comparative Study of AI-Driven Fashion Trend Prediction System Using AI and ML: A Review
Abstract: To overcome the challenges in fashion trend forecasting, researchers have introduced several advanced and data-driven approaches. One such method uses a long short-term memory (LSTM) model combined with an encoder-decoder architecture to extract meaningful fashion content and recognize styles from product images. This model achieves higher accuracy in predicting upcoming fashion trends by incorporating varying price intervals and has shown impressive results when evaluated on the Amazon fashion dataset. Another …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 2, 2025 · pp. 35–41 Read article
-
Applying Text Analysis Methods for Emotion Recognition
Abstract: This article presents a comprehensive study of sentiment analysis, a vital task in the realms of natural language processing (NLP) and artificial intelligence (AI). Sentiment analysis involves the extraction and classification of subjective information from textual data, determining whether the sentiment expressed is positive or negative. This paper investigates different approaches and methodologies used in sentiment analysis, encompassing machine learning models as well. Additionally, it discusses the challenges faced in …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 11, Issue 2, 2024 · pp. 12–22 Read article
-
Aspect-Based Sentiment Analysis Using a Hybrid Approach with Dependency Parsing
Abstract: The rapid expansion of digital communication has resulted in an unprecedented volume of consumer-generated textual data across online reviews, social media platforms, forums, and e-commerce websites. Extracting meaningful insights from this data is increasingly important for organizations seeking to understand customer opinions, preferences, and behavioral trends. Despite significant advances in sentiment analysis, many existing approaches primarily focus on surface-level features and often overlook deeper syntactic and semantic relationships within text. …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 01–09 Read article
-
Study of Social Trends Prediction Using AI
Abstract: AI (Artificial Intelligence) has fundamentally changed the ability to analyze social trends by using large datasets to develop predictions about human behavior, public sentiment, and global events. Using methodologies such as Natural Language Processing (NLP), Time-Series Forecasting, and Graph-Based Social Network Analysis, AI is able to find hidden correlations in a variety of available datasets, from social media to economic indicators to public records, and fundamentally changes decision-making based on …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 19–29 Read article
-
The Social Media Misinformation Loop: An Analysis of Consumption and Sharing Behaviours among Medical Students
Abstract: Background: The contemporary information landscape is largely dominated by social media, which has emerged as a major source of information for medical students. This study aims to investigate the information consumption and sharing behaviours of medical students in Jammu and Kashmir, India, in the context of misinformation and its growing influence. Methods: A cross-sectional survey using a structured questionnaire was administered to 668 students from 13 medical colleges. The data …
Published in Research and Reviews: A Journal of Health Professions · Vol. 16, Issue 1, 2026 Read article
-
English Literature in the Digital Era: Transformations and Trends
Abstract: English literature context has been changed significantly with modernisation of the digital age that is creating a massive impact on creation, dissemination, and interpretation of literature. When one considers the relationship between literature and digital innovation, one may summarize these disruptive forces as both a challenge to and opportunity for literature — a hybridization of past forms. It explores how digital technologies have transformed reading practices, textual analysis, and the …
Published in Emerging Trends in Languages · Vol. 2, Issue 2, 2025 · pp. 27–32 Read article
-
Effectiveness of Online Advertising in Reaching Target Audiences
Abstract: This study examines the effectiveness of online advertising in reaching target audiences. With the rapid growth of digital platforms, understanding the efficacy of online advertising has become paramount for marketers. Utilizing a combination of quantitative analysis and case studies, this research investigates the various strategies and channels employed in online advertising to reach specific demographic segments. By analyzing metrics such as click-through rates, conversion rates, and audience engagement, this study …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 14, Issue 2, 2024 · pp. 8–12 Read article
-
Privacy Preservation Methods in Multimedia Applications: A Comprehensive Review
Abstract: This comprehensive review explores the current landscape of privacy preservation techniques in multimedia applications, offering a detailed examination of their effectiveness, limitations, and future directions. As the use of multimedia data continues to grow across diverse sectors such as healthcare, surveillance, social media, and entertainment, ensuring the confidentiality and integrity of this data has become a pressing concern. The study covers a broad spectrum of privacy-preserving approaches, from conventional cryptographic …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 12, Issue 2, 2025 · pp. 33–41 Read article
-
Stock Market Prediction Using Machine Learning: Techniques, Challenges, and Future Directions
Abstract: The continuous advancement of machine learning (ML) technologies has significantly transformed the field of financial forecasting, particularly in the area of stock market prediction. The ability to accurately forecast stock price movements and market trends plays a crucial role in supporting informed investment strategies and effective risk management. This paper provides a comprehensive review of recent developments in the application of ML techniques for predicting stock market behavior. It classifies …
Published in E-Commerce for Future & Trends · Vol. 13, Issue 1, 2026 · pp. 10–16 Read article
-
Evaluating Web Content and Design Trends: A Comprehensive Study of Forest Institute Library Websites of ICFRE
Abstract: This study evaluates the website content and design features of forest institute libraries under the Indian Council of Forestry Research and Education (ICFRE). A comprehensive checklist comprising eight categories and 60 parameters was developed to assess nine regional forest institute websites systematically. The findings reveal that all websites (100%) feature visible and contrasted colour schemes with clear and easily readable text. Moreover, every institute’s website includes webmail functionality, copyright information, …
Published in Journal of Advancements in Library Sciences · Vol. 12, Issue 2, 2025 · pp. 1–11 Read article
-
Designing an AI-Based Platform for Stock Market Prediction
Abstract: The AI-Based Platform for Stock Market Prediction is an advanced tool designed to forecast stock prices and market trends using artificial intelligence. This platform combines machine learning algorithms, real-time financial data, and sentiment analysis to provide investors with actionable insights. The platform uses advanced predictive techniques like Long Short-Term Memory (LSTM) networks and Gradient Boosting Machines to generate precise and reliable forecasts. Additionally, it incorporates interactive visualizations and portfolio optimization …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 3, 2025 · pp. 14–19 Read article
-
Using AIML to Enhance Demand Forecasting in Business
Abstract: Artificial intelligence machine learning (AIML) can play a significant role in enhancing demand forecasting in business. AIML is a programming language designed for creating chatbots and conversational agents, but its application extends beyond simple interactions. In the context of demand forecasting, AIML can be utilized to analyze historical data, customer interactions, and market trends. By implementing AIML algorithms, businesses can create intelligent models that learn from past demand patterns, customer …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 1, 2024 · pp. 35–40 Read article
-
Personal Portfolio Website: A Tool for Professionals to Present their Work and Skills
Abstract: This work delves into the burgeoning realm of private portfolio websites, exploring the motivations and aspirations behind the efforts of experts to curate their virtual presence. In an era where personal branding and online visibility are paramount, these websites function as more than mere repositories of work; they are dynamic systems for self-expression, professional growth, and networking. Through a comprehensive evaluation of design trends and user experiences, this study highlights …
Published in Recent Trends in Programming languages · Vol. 11, Issue 2, 2024 · pp. 1–9 Read article