Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Gender Equity, Internationalization, and the Quintuple Helix: Comparative NLP Analysis of University Strategies in Japan and Turkiye(Univ Louisiana Monroe, 2026-03-08) Rogler, Andreas; Morozumi, Akiko; Coymak, Ahmet; Bengu, ElifAs higher education institutions (HEIs) seek to align with Sustainable Development Goals (SDGs), integrating diversity, equity, and inclusion (DEI) into internationalization strategies has become increasingly central. In this study, we analyze 209 university strategic plans, 86 from Japan (2022-2027) and 123 from Turkiye (2019-2023), to examine how institutional discourse frames gender equity, with a particular focus on SDG 5, gender equality. We identify clear and distinct national patterns using natural language processing (NLP) techniques (e.g., keyword frequency analysis, named entity recognition, and syntactic parsing) and are guided by the quintuple helix model (QHM). Japanese universities tend to emphasize societal engagement and forward-looking commitments through abstract language. In contrast, Turkish universities adopt a more bureaucratic and retrospective tone, often referring explicitly to named target groups. We find that both countries show limited engagement with intersectional identities and marginalized populations such as female faculty, migrants, and refugees, and both underutilize the civil society and environmental dimensions of the QHM. Although inclusive values frequently appear, strategic plans rarely include clear details on how to reach these goals. Based on our analysis, we propose a scalable, reproducible framework for evaluating inclusive internationalization. Our findings underscore the importance of moving beyond symbolic discourse and calling for more accountable, stakeholder-driven planning processes that embed DEI into the structural, curricular, and governance systems of HEIs.Conference Object Citation - Scopus: 2NLP-Driven Fake News Detection: A Machine Learning Perspective(IEEE, 2025-05-23) Coban, Mert Korkut; Bakal, GokhanThe rapid spread of fake news poses a significant challenge, impacting public opinion, decision-making, and societal trust. This study explores the application of Natural Language Processing (NLP) and Machine Learning (ML) techniques for robust fake news detection. Using datasets such as ISOT Fake News, WELFake, and Football Fake News, the project employs advanced preprocessing methods and feature extraction techniques, including TF-IDF, Word2Vec, and GloVe. A comprehensive evaluation of machine learning models-Random Forest, Support Vector Machines (SVM), and Neural Networks-was conducted to identify the optimal configuration. Results demonstrate that Random Forest with TF-IDF excels in in-domain detection, achieving an F1-score of 99.70%, while Neural Networks paired with Word2Vec and GloVe embeddings outperform in cross-dataset scenarios. The study highlights the importance of dataset size, domain relevance, and feature representation in achieving high generalizability. These findings provide a scalable framework for combating misinformation on digital platforms.