# Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models
## 1 Introduction  
On 30 January 2020, the World Health Organization declared COVID-19 a pandemic after the massive spread of the virus SARS-CoV-2 in many countries all over the world [1]. Up to the time of this study, the number of confirmed cases is over 252 million [2]. Such a problem has encouraged many governments to initiate plans of crisis management. One of the most essential rules to reduce the spread of the virus is social distancing. This process encourages individuals to rely more on social network communications. Twitter is arguably one of the most famous social media sites, thanks to its simplicityand ease of knowledge sharing. The social interactions represent a valuable resource for monitoring and analyzing the impacts of disasters such as COVID-19. Locations of widespread infections, people's reactions to quarantine procedures, symptoms of the new variants of the virus, and the psychological effects can be highlighted from the text analysis of social media data to support real-time disaster operation and management [3; 4; 5; 6]. In [7], the author states that the ministry of health of Saudi Arabia has used several accounts on Twitter to populate many health-related hashtags to provide governmental pieces of advice to Saudi Arabian citizens. This is evidence that decision-makers are considering social media as important channels for communicating with people. So, any accurate tool that can provide the policymakers with the response of their specific citizens would be of special importance. And this information can be extracted from the dialectical analysis of social media texts. Furthermore, the identification of Arabic dialects is considered as one of the first pre-processing components in any reliable Arabic Natural Language Processing (NLP) application [8]. This identification is significant for several NLP problems such as information retrieval, automatic translation, sentiment analysis, and event detection. For example, when new cases of spreading disease start to appear in a specific region, the information about such cases most probably begins to emerge on social media. Detecting such events as soon as possible can be very critical for the containment of the disease. And this vital process can be handled using a robust NLP solution. In this work, we introduce our efforts for analyzing COVID-19 Arabic conversations on the Twitter network. We used a large dataset of Arabic tweets related to the topic of COVID-19 [7]. It has been crawled in the period from January to April 2020. The total number of tweets in the dataset is around 3.8 million. The dataset consists of different Arabic dialects such as Egyptian, Gulf, Levantine, along with Modern Standard Arabic (MSA). The dataset is primarily from hashtags populated from Saudi Arabia, which can be utilized in other Arabic-speaking countries. The dataset includes several types of conversations discussing different aspects of COVID-19 such as social solidarity and preventive measures announced by governments. It is worth noting that a word in the Arabic language may refer to a wide collection of meanings in different dialects. Officials usually use MSA, the formal version of the language, in educational organizations and pan-Arab news broadcasting, which is different from the varieties that are spoken in daily communications by native speakers [9; 10; 11]. These daily varieties constitute the dialects of Arabic that can be classified based on some common linguistic features of geographical locations. The most common Arabic dialects categorization is:  
* Egyptian (EGY): An Egyptian dialect spoken in Egypt and is also commonly spoken because of the historical influence of Egyptian media. * Gulf (GLF): A dialect spoken mostly in Saudi Arabia, the United Arab Emirates, Kuwait, Oman, Bahrain, and Qatar. * Iraqi: The widely-spoken Arabic dialect by the people of Iraq. * Levantine (LEV): A dialect spoken mainly by people from the Levant (in other words, people of Palestine, Jordan, Lebanon, and Syria).