# Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example
## 1 Introduction  
The massive influx of media news, particularly for dynamic and fast-evolving situations such as armed conflicts, presents a significant obstacle for conflict analysts who must quickly comprehend, classify, and extract actionable information for intelligence. Manual classification is not only time-consuming but could also be impractical, given the limited timeframe and available resources during crisis situations.  
Effectively classifying news content is vital for multiple applications, including conflict monitoring, humanitarian aid, and timely decision-making by policymakers. Accurate classification supports high-quality information retrieval, while efficient methods can enhance processing speed, both of which are essential for timely and informed responses to emergency events. However, news classification is inherently challenging, given the complexities of language, the presence of overlapping semantic categories, and the frequent occurrence of ambiguous or incomplete information in media data [1].  
The rapid growth in digital media has intensified the challenges associated with effectively classifying news content. Traditional news classification methods, including machine learning and early deep learning architectures, have evolved significantly. Transformer-based models such as BERT and GPT offer advanced capabilities by capturing nuanced contextual information more effectively [2; 3; 4].  
In natural language processing (NLP), classification plays a critical role in organizing, analyzing, and deriving insights from unstructured text data, a task that has become increasingly challenging due to the rapid expansion of digital media and the sheer volume of news articles published daily [5; 6; 7; 8; 9]. Given the technological evolution and the complexities in conflict-related news classification, this study evaluated and compared the effectiveness of state-of-the-art NLP approaches, specifically BERT and GPT, in classifying conflict-related articles related to the Sudan Conflict. This research included testing different methodologies, such as Zero-Shot prompting, Retrieval-Augmented Generation (RAG), and RAG with In-Context Learning (ICL) for GPT models, alongside both base and large versions of BERT, each evaluated in standard and hyperparameter-tuned configurations, to identify the optimal approach for Single-Label classification from Multi-Label Ground Truth (SL-MLG) and Multi-Label Classification (MLC). By systematically assessing these methods, this study provides insights into their applicability, reliability, and limitations, ultimately informing the best practices for applying NLP-based classification methods to media news classification in rapidly evolving conflicts and other scenarios.  
Humanitarian responders and crisis managers rely on rapid, accurate classification of news incidents to allocate aid, coordinate evacuations, and minimize casualties. Mislabeling or delays can misdirect resources and jeopardize lives. While prior research has demonstrated how transformer-based models like BERT or GPT perform well in standard news classification tasks [10; 11; 12; 13], existing studies often assume clean, balanced datasets and focus on structured or routine topics. For example, [PERSON] [10] discussed mathematical models for AI-based news analytics, and [PERSON] et al. [11] applied BERT-CNN hybrids for long-text news classification. However, crisis news reporting poses distinct challenges, including fast-changing events, ambiguous wording, overlapping categories, and sensitive content. Conflict-related datasets are often small, highly imbalanced, and require expert annotation, which is time-consuming and resource-intensive. To address this gap, we systematically compared BERT and GPT in crisis news classification within these real-world constraints, using the Sudan Conflict as an example and analyzed how different prompting and retrieval strategies affect their classification performance.