Analyzing Textual Data for Fatality Classification in Afghanistan's
  Armed Conflicts: A BERT Approach
Hikmatullah Mohammadi Ziaullah Momand Parwin Habibi Nazifa Ramaki Bibi Storay Fazli Sayed Zobair Rohany Iqbal Samsoor
Afghanistan has witnessed many armed conflicts throughout history, especially
in the past 20 years; these events have had a significant impact on human
lives, including military and civilians, with potential fatalities. In this
research, we aim to leverage state-of-the-art machine learning techniques to
classify the outcomes of Afghanistan armed conflicts to either fatal or
non-fatal based on their textual descriptions provided by the Armed Conflict
Location & Event Data Project (ACLED) dataset. The dataset contains
comprehensive descriptions of armed conflicts in Afghanistan that took place
from August 2021 to March 2023. The proposed approach leverages the power of
BERT (Bidirectional Encoder Representations from Transformers), a cutting-edge
language representation model in natural language processing. The classifier
utilizes the raw textual description of an event to estimate the likelihood of
the event resulting in a fatality. The model achieved impressive performance on
the test set with an accuracy of 98.8%, recall of 98.05%, precision of 99.6%,
and an F1 score of 98.82%. These results highlight the model's robustness and
indicate its potential impact in various areas such as resource allocation,
policymaking, and humanitarian aid efforts in Afghanistan. The model indicates
a machine learning-based text classification approach using the ACLED dataset
to accurately classify fatality in Afghanistan armed conflicts, achieving
robust performance with the BERT model and paving the way for future endeavors
in predicting event severity in Afghanistan.