New Model for Analyzing Global Conflicts

Every day, conflicts happen around the world, affecting many lives and political situations. Analyzing these conflicts quickly is vital for governments and organizations to respond effectively. This article presents a new model called the Classification-Aware Neural Topic Model Combined With Interpretable Analysis (CANTM-IA). This model aims to classify conflict events and discover topics related to these conflicts while being easy to understand.

#Background

Conflicts can have serious consequences, like the energy crisis seen during the Ukraine situation, which affected food production and many other sectors. By classifying and analyzing conflict information, institutions can better prepare and respond to these events. Therefore, creating a deep-learning model capable of categorizing conflict events and discovering relevant topics is essential.

#Text Classification and Topic Modeling

Text classification involves assigning categories to various texts, helping to distinguish between different types of information. Several methods, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), have been used for text classification. Recently, a model based on BERT has shown great success in this area.

Topic modeling, on the other hand, identifies different topics and keywords from a collection of documents. One well-known method is Latent Dirichlet Allocation (LDA), which is unsupervised and does not have a hierarchical structure. Many researchers have built on LDA to create more advanced models.

#Challenges with Neural Networks

Neural networks, while powerful, are often hard to interpret. This lack of transparency raises questions about their reliability, especially in high-stakes situations like conflict analysis. Some studies have explored ways to make these models more interpretable, allowing users to trust their decision-making processes.

#New Model: CANTM-IA

The CANTM model was designed to combine text classification and topic modeling, proving successful in understanding online messages during the COVID-19 pandemic. However, it had limitations that our new model, CANTM-IA, aims to address.

CANTM-IA focuses on enhancing interpretability and performance. This model uses a special method to highlight important parts of the input data. By concentrating on relevant information, CANTM-IA can classify text more accurately.

#Key Features of CANTM-IA

1. Focus on Important Information: By analyzing which parts of the text are essential, CANTM-IA reduces the impact of irrelevant words. This allows the model to concentrate on the critical elements related to the conflict, leading to better classification results.

2. Improved Architecture: The original CANTM had certain inefficiencies in its design. By optimizing the model, CANTM-IA enhances its computational efficiency while keeping its essential features.

3. Use of Rationales: CANTM-IA introduces rationales, which are parts of the text that are particularly informative. These rationales help explain the model's decisions, making it easier for users to understand the reasoning behind classifications.

#Experimental Setup

To test CANTM-IA, we used data from the Armed Conflict Location and Event Data Project (ACLED). This database includes various conflict types and has a significant amount of information. For balanced results, we selected a subset of data focusing on protests.

We compared the performance of CANTM-IA against two strong baseline models: BERT and the original CANTM. By conducting various experiments, we aimed to determine how changes to the model impacted its classification performance.

#Results

The results showed that CANTM-IA performed better than both BERT and the original CANTM model. The accuracy of CANTM-IA was notable, highlighting the effectiveness of using rationales and emphasizing important information during classification.

#Classification Performance

In our experiments, CANTM-IA achieved an impressive accuracy rating, surpassing previous models. This indicates its potential for practical use in various situations where quick and accurate analysis is needed.

#Topic Discovery

CANTM-IA also showed improvement in identifying relevant topics for each conflict category. By focusing on the essential words, the model reduced the presence of neutral or irrelevant terms that might confuse the results. As a result, the topics discovered were more closely related to the conflict types.

#Rationale Extraction

An analysis of the rationales extracted by CANTM-IA demonstrated its ability to pinpoint essential aspects of the texts. Compared to CANTM, which sometimes included irrelevant information, CANTM-IA was more focused on specific conflict-related terms.

#Conclusion

In this article, we introduced the Classification-Aware Neural Topic Model Combined With Interpretable Analysis (CANTM-IA) for analyzing conflict information. The model effectively classifies data and discovers relevant topics while being easy to understand. CANTM-IA combines interpretability with strong performance, making it a valuable tool for researchers and organizations looking to analyze conflicts swiftly and reliably.

#Future Work

Looking ahead, we plan to refine CANTM-IA further and adapt it for other data types. We believe that this model can be beneficial for various applications beyond conflict analysis, such as rumor verification and stance detection.

#Ethics and Broader Impact

This research relies solely on publicly available datasets, so no ethical approval was necessary. The implications of our findings can significantly impact various fields, including decision-making and policy development.

In summary, CANTM-IA is not only a powerful tool for classifying and analyzing conflict information but also a step toward creating more interpretable AI systems. With its strong performance and focus on essential information, it provides a framework that can be expanded to other areas of research and practice.