Repository logoGCRIS

Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

Browse

Filters

2025 - 20263

Settings

Has files: NoSubject: search.filters.subject.Natural Language Processing

Search Results

Now showing 1 - 3 of 3
  • 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, Elif
    As 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: 2
    NLP-Driven Fake News Detection: A Machine Learning Perspective
    (IEEE, 2025-05-23) Coban, Mert Korkut; Bakal, Gokhan
    The 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.
  • Conference Object
    Multi-Method Text Summarization: Evaluating Extractive and BART-Based Approaches on CNN/Daily Mail
    (Institute of Electrical and Electronics Engineers Inc., 2025-06-27) Inal, Yasin; Bakal, Gokhan; Esit, Muhammed
    With the exponential growth of digital content, efficient text summarization has become increasingly crucial for managing information overload. This paper presents a comprehensive approach to text summarization using both extractive and abstractive methods, implemented on the CNN/Daily Mail dataset. We leverage pre-trained BART (Bidirectional and AutoRegressive Transformers) models and fine-tuning techniques to generate high-quality summaries. Our approach demonstrates significant improvements, with our best model trained on 287 k samples achieving ROUGE-1 F1 scores of 0.4174, ROUGE-2 F1 scores of 0.1932, and ROUGE-L F1 scores of 0.2910. We provide detailed comparisons between extractive methods and various BART model configurations, analyzing the impact of training dataset size and model architecture on summarization quality. Additionally, we share our implementation through an opensource NLP toolkit to facilitate further research and practical applications in the field. © 2025 Elsevier B.V., All rights reserved.