U krainian Resilience: A Dataset for Detection of Help-Seeking Signals Amidst the Chaos of War (2024)

Abstract: We propose a novel dataset “Ukrainian Resilience” that brings together a collection of social media posts in the Ukrainian language for the detection of help-seeking posts in the Russia-Ukraine war. It is designed to help us analyze and categorize subtle signals in these posts that indicate people are asking for help during times of war. We are using advanced language processing and machine learning techniques to pick up on the nuances of language that show distress or urgency. The dataset is the binary classification of the social media posts that required help and did not require help in the war. The dataset could significantly improve humanitarian efforts, allowing for quicker and more targeted help for those facing the challenges of war. Moreover, the baseline models are implemented and GPT 3.5 achieved an accuracy of 81.15%.

## introduction war, particularly the ongoing conflict between ukraine and russia, has profound and devastating effects on innocent civilians. in times of war, civilians often find themselves caught in the crossfire, facing the harsh consequences of violence, displacement, and the breakdown of essential services. the ukraine-russia war is no exception, as it has led to a humanitarian crisis with significant repercussions for the ukrainian population. innocent lives are disrupted, families are torn apart, and communities are shattered as a result of the conflict. one of the most immediate and visible impacts is the displacement of civilians. many ukrainians have been forced to flee their homes in search of safety, facing the challenges of finding shelter, food, and medical care. the psychological toll on individuals, especially children, is severe, with the trauma of war leaving lasting scars on the mental well-being of those affected. the destruction of infrastructure, including schools and hospitals, further exacerbates the suffering of the innocent, limiting access to education and healthcare. moreover, the war has economic ramifications, with livelihoods disrupted and economic instability compounding the difficulties faced by civilians. the struggle for resources, combined with the breakdown of social structures, can lead to a desperate situation for many ukrainians, particularly vulnerable populations such as the elderly and children. in the digital age, social media plays a crucial role in conveying the human stories behind conflicts. a dataset designed for binary classification to distinguish social media posts that signal a need for help versus those that do not can be invaluable in crisis response efforts. such a dataset could utilize natural language processing (nlp) techniques to analyze the content of posts, identifying key indicators of distress, urgency, or specific requests for assistance. this approach not only leverages technological advancements but also demonstrates the potential for data science to contribute to humanitarian efforts, offering a more efficient means of identifying and addressing the needs of those affected by war. it emphasizes the importance of harnessing technology to provide timely and targeted assistance to vulnerable populations in crisis situations. motivation: the war impacts the lives of innocents some leave their country, some are wounded, a few can become orphans, and most lose their jobs and property. in these critical situations, social media can help in addressing their problem and can request help. detection of the help-seeking posts during the time of war could help refugees or migrants. governments and non-governmental organizations (ngos) can utilize and help them through social media. it might be helpful to provide jobs, shelter, food, etc. the key contributions of this work are as follows: 1. as of our knowledge, we are the first to develop a dataset that helps the innocents in the russian-ukraine war through social media in the ukrainian language. sufi (2022) presented an ai-based software, ai-socialdisaster, designed for identifying and analyzing natural disasters from social media. this software might offer a comprehensive solution for extracting actionable insights from social media during disasters. @xcite explored the application of satellite data and ai in efficient agricultural disaster financing. this work likely focuses on using advanced technologies to optimize financial strategies in the agricultural sector during disasters. @xcite provided an overview of the applications of ai and machine learning in disasters and public health emergencies. this work is likely to offer insights into the broader spectrum of ai applications in disaster management. recent literature has made significant contributions in enhancing disaster management by leveraging social media. this progress has led to the identification of various applications that can be instrumental in aiding people during times of cri2 methodology ## experimental results and discussion in the realm of predictive modeling for identifying help-seeking posts, a comprehensive evaluation of different algorithms is implemented for classifying text. the error analysis: upon closer inspection, a recurring theme among false positives emerges. many of these misclassifications seem to stem from posts that contain emotionally charged language or expressions of distress, but are, in fact, providing updates on non-emergency situations. the model might struggle to distinguish between urgent pleas for help and general expressions of concern. additionally, false positives seem to be more prevalent in posts that contain ambiguous or sarcastic language, as the model may struggle with nuanced linguistic cues. conversely, false negatives present a different set of challenges. a notable observation is that the model tends to overlook subtleties in posts where the urgency is not explicitly stated. for instance, posts that discuss longer-term issues or structural problems related to the conflict may be incorrectly classified as not needing immediate help. furthermore, false negatives are more common in posts that use local dialects or slang, which the model might not have adequately captured during training. although the machine learning models make errors the model can be deployed for real-time monitoring and detection for the russia-ukraine war. once deployed for real-time monitoring of social media posts the machine learning models can detect help-seeking posts in ukrainian language during the war. the ngos and governments can provide the shelter for people who require shelter, can provide jobs for jobless, an orphan can join an orphanage, etc. ## conclusion and future work we present a novel dataset for the detection of help seeking posts at the time of war. based on the conducted experiments, it has been established that the proposed dataset has significant potential to assist governments and non-governmental organizations (ngos) in aiding innocent individuals affected by war who are in dire need of support. this assistance spans various necessities, including but not limited to employment opportunities, food, shelter, and the search for missing family members. to extend the scope of this work, future efforts will involve the creation of datasets specifically tailored for the israel-palestine conflict and civil wars. additionally, there is a recognition of the importance of developing a universal dataset in the english language. this broader dataset aims to streamline response efforts by collecting and curating social media posts in english. this approach ensures that in the event of a sudden declaration of war, organizations can promptly leverage the dataset from the onset, creating a versatile resource applicable across languages and countries.