**1. Introduction**

After a natural disaster, disaster response is essential. Its assessments present the current situation and summarize information that guides rescue forces and other immediate relief efforts to the site [1]. Currently, many scholars publish research on disaster response, focusing on implementing artificial intelligence to process big data. Examples of the former are: a platform to classify crisis-related communications [2], a model that learns damage assessment and proposes applications [1], an automated method to retrieve information for humanitarian crises [3], or an interface to assist

with fast decision-making during humanitarian health crises [4, 5]. In parallel, we observe a growing global presence of humanitarian organizations that publish humanitarian mission statements and declarations. There are more than 4000 organizations active in this area. Examples of large ones include the World Food Program (WFP), Cooperative Assistance and Relief Everywhere (CARE), the International Federation of Red Cross and Red Crescent Societies (IFRC), and Action Against Hunger (AAH) [6]. However, the two narratives do not overlap and often ignore each other's efforts. To achieve an understanding that leads to a unification of the knowledge produced, one must think of methods to analyze both academic literature and humanitarian mission statements to create a common ground.

Most scientific publications and mission statements of humanitarian organizations are published in text form, which is challenging to analyze using traditional statistical analysis. Nowadays, artificial intelligence algorithms are used to facilitate this process. The branch of research that concentrates on text analysis with these methods is called text mining––a specific branch of data mining. Text mining is the process of analyzing texts to obtain new information from them. It does this by identifying patterns or correlations between terms, thus making it possible to find information that is not explicit in the text. This new information makes it possible to create inferences in different tasks such as classification, clustering, or prediction of texts. Currently, novel studies strengthen their efforts to develop or improve methods for analyzing text data, such as the study by [7], which will serve as a reference for this experiment.

## **2. Methodology**

This article will focus on the late operations in disaster response: building safety assessments, temporary housing, and policy recommendations. The abovementioned tasks will serve as keywords to crawl academic publications and humanitarian organizations' mission statements to define a specific framework directed toward architectural practices to respond to natural disasters.

#### **2.1 Academic publications**

To collect academic publications, the pipeline follows the approach of [8]. In their article, the researchers proposed to work with abstracts. They argue that abstracts communicate information concisely and straightforwardly, avoiding unnecessary words. In contrast with full texts, which sometimes contain negative relationships, writing styles can include complex sentences that require different encoding methods than the ones proposed in the present research.

The abstracts of academic publications will be collected from the ScienceDirect database (https://www.sciencedirect.com), one of the most significant and extensive academic archives of journals and conference proceedings. Using a diverse database such as ScienceDirect is beneficial, as a substantial amount of scientific literature from many fields can be assembled to address architectural practices in disaster response. To start collecting the abstracts of academic publications, an API was used, which required keywords to filter data according to a specific interest. The API has two syntax rules. The first rule, +AND+, searches for pair words, the second, +, determines that the word must be contained in the text. By setting the keywords: Bui lding+AND + Safety+Assessments, Housing+and + Disaster+ANDResponse, and Poli

*The Use of Artificial Intelligence to Bridge Multiple Narratives in Disaster Response DOI: http://dx.doi.org/10.5772/intechopen.108196*

cy+AND + Recommendations+and + Disaster+AND + Response, 9000 abstracts were collected. The articles are from the last 10 years, from 2010 to 2020.

As suggested by Tsitoyana et al. [8], abstracts that contained in their titles the following keywords: "Foreword," "Prelude," "Commentary," "Workshop," "Conference," "Symposium," "Comment," "Retract," "Correction," "Erratum," and "Memorial" were removed. **Figure 1** shows a word cloud of the collected abstracts, where the main concepts are represented, and an overview of the general intention is graspable.

#### **2.2 Professional humanitarian organizations**

Professional Humanitarian Organizations have continuously expanded over the past decades. Worldwide more than 125 million people relying on humanitarian aid double the number 10 years ago [7]. Commonly, such organizations communicate with their stakeholders via platforms online, sharing their mission statements, aims, and goals. Two of the leading online platforms are Wikipedia and ReliefWeb. Hence, data on humanitarian organizations' mission statements will be collected from these two web sources.

From Wikipedia, 749 humanitarian mission statements were collected using Wikipedia API under the Category "Humanitarian aid organizations." The collected texts were translated into English. To collect the text from ReliefWeb, a twofold

**Figure 1.** *World cloud of 8364 abstracts from the field of disaster response.*

process was performed. The first step was to collect 1154 links of Humanitarian Organizations from the ReliefWeb web (https://reliefweb.int), a specialized digital service of the UN Office for the Coordination of Humanitarian Affairs (OCHA). Those links were organized under the tag "Organizations." The second step was to access each retrieved link and parse the source text, searching for tags: "about," "about us," "we are," "who we are," and "what we do." Having a particular focus on English texts. After the two-step process, a description of each organization and mission statement was found.

After joining both datasets (Wikipedia and ReliefWeb), an overlapping of 87 organizations was found. This points out the lacking of consistency regarding professional humanitarian aid on the web. Nevertheless, we worked with all the humanitarian organizations found, which sum up 1930 entries. **Figure 2** shows the word cloud of the overall dataset.
