This study addresses the growing concern of digital game addiction (DGA) by developing an information system for detection and early intervention, to predict an individual’s propensity toward DGA. The study employed machine learning models to process unstructured data collected from online game platforms between May 1, 2023, and June 1, 2023. A total of 660 comments were automatically extracted. Following an expert review process, 245 ambiguous or inconsistent entries were removed, resulting in a final dataset of 415 textual comments for model training. The comments were manually annotated into two categories: addiction-related (1) and non-addiction-related (0) to ensure data consistency and reliability. Data augmentation techniques, such as BERT-based augmentation, deep translation, and synonym replacement, were applied to increase dataset diversity and enhance the model’s generalization capability. After extensive preprocessing, we trained and evaluated six machine learning models: XGBoost, Support Vector Machines (SVM), Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, and Random Forests. XGBoost consistently outperformed all other models, achieving the highest accuracy of 93.06% on the binary-labeled dataset and 92.49% on the expanded dataset. Random Forests and Logistic Regression also exhibited strong classification performance, making them viable alternatives depending on the requirements. In contrast, SVM and Decision Trees showed lower predictive reliability, indicating potential challenges in classifying text-based addiction indicators effectively. As a practical outcome, we developed an interface using the best-performing model, capable of performing preliminary diagnoses based on user-provided sentences. This innovative approach introduces a new dimension in addiction studies by presenting a practical tool for early detection and intervention in DGA.

The large amounts of Facebook social network data which are generated and collected need to be analyzed for valuable decision making information about shopping firms in Turkey. In addition, analyzing social network data from outside the firms becomes a critical business need for the firms which actively use Facebook. To have a competitive advantage, firms must translate social media texts into something more quantitative to extract information. In this study, web text mining techniques are used to determine popular online shopping firms’ Facebook patterns. For this purpose, 200 popular Turkish companies’ web URLs are used. Web text mining through natural language processing techniques is examined. Similarity analysis and clustering are done. Consequently, the clusters of the Facebook websites and their relationships and similarities of the firms are obtained.

Across digital platforms, large-scale assessment of customer sentiment has become integral to brand management, service recovery, and data-driven marketing in e-commerce. Still, most studies center on single-language settings, with bilingual and culturally diverse environments receiving comparatively limited attention. In this study, a bilingual sentiment analysis of consumer feedback on X (formerly Twitter) was conducted for three global quick-service restaurant (QSR) brands—McDonald’s, Burger King, and KFC—using 145,550 English tweets and 15,537 Turkish tweets. After pre-processing and leakage-safe augmentation for low-resource Turkish data, both traditional machine learning models (Naïve Bayes, Support Vector Machines, Logistic Regression, Random Forest) and a transformer-based deep learning model, BERT (Bidirectional Encoder Representations from Transformers), were evaluated. BERT achieved the highest performance (macro-F1 ≈ 0.88 in Turkish; ≈0.39 in temporally split English), while Random Forest emerged as the strongest ML baseline. An apparent discrepancy was observed between pseudo-label agreement (Accuracy > 0.95) and human-label accuracy (EN: 0.75; TR: 0.49), indicating the limitations of lexicon-derived labels and the necessity of human validation. Beyond methodological benchmarking, linguistic contrasts were identified: English tweets were more polarized (positive/negative), whereas Turkish tweets were overwhelmingly neutral. These differences reflect cultural patterns of online expression and suggest direct managerial implications. The findings indicate that bilingual sentiment analysis yields brand-level insights that can inform strategic and operational decisions.

Learning objectives: The most important problem of dynamic societies is waste management (WM). For a healthy, happy and sustainable life, WM in cities should be carried out accurately, quickly and effectively with artificial intelligence-based systems. The most important negative output of developed societies is waste, the more production and consumption is made, the more waste will be generated. Based on the principle of Green energy, where and what kind of work should be done in WM will be put forward by practices.