Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models

Abstract

The recent surge of social media networks has provided a channel to gather and publish vital medical and health information. The focal role of these networks has become more prominent in periods of crisis, such as the recent pandemic of COVID-19. These social networks have been the leading platform for broadcasting health news updates, precaution instructions, and governmental procedures. They also provide an effective means for gathering public opinion and tracking breaking events and stories. To achieve location-based analysis for social media input, the location information of the users must be captured. Most of the time, this information is either missing or hidden. For some languages, such as Arabic, the users’ location can be predicted from their dialects. The Arabic language has many local dialects for most Arab countries. Natural Language Processing (NLP) techniques have provided several approaches for dialect identification. The recent advanced language models using contextual-based word representations in the continuous domain, such as BERT models, have provided significant improvement for many NLP applications. In this work, we present our efforts to use BERT-based models to improve the dialect identification of Arabic text. We show the results of the developed models to recognize the source of the Arabic country, or the Arabic region, from Twitter data. Our results show 3.4% absolute enhancement in dialect identification accuracy on the regional level over the state-of-the-art result. When we excluded the Modern Standard Arabic (MSA) set, which is formal Arabic language, we achieved 3% absolute gain in accuracy between the three major Arabic dialects over the state-of-the-art level. Finally, we applied the developed models on a recently collected resource for COVID-19 Arabic tweets to recognize the source country from the users’ tweets. We achieved a weighted average accuracy of 97.36%, which proposes a tool to be used by policymakers to support country-level disaster-related activities.

Influenza-like Illness Detection from Arabic Facebook Posts Based on Sentiment Analysis and 1D Convolutional Neural Network

“…An Arabic language dialect identification system is proposed in [36], aiming to analyze and classify COVID-19-related tweets into four Arabic dialects: Modern Standard Arabic (MSA), Egyptian, Gulf, and Levantine. In this study, BERT-based models were adopted to locate the source region of COVID-19 Arabic tweets, thus helping to monitor the epidemic outbreaks in the Arab world.…”

Designing Multimodal Interactive Dashboard of Disaster Management Systems

“…After applying feature extraction and topic modeling, a pre-trained BERT transformer is used for disaster classification of tweets. In the same project, with a focus on multilingual data, an Arabic dialect identification model was developed based on the BERT algorithm to classify Egyptian, Gulf, Iraqi, Levantine, and Maghrebi dialects by analyzing COVID-19 Arabic conversations on the Twitter network, as published in [6]. For emergency detection and identification from the visuals, in [13], the authors identify emergency needs and responses from visual information to support the humanitarian organizations in reaching out to the affected people and specific locations with their services.…”

“…This framework enables easy integration of the custom visual analytic elements needed according to the end user's requirements and type of crisis events. We have considered the preprocessed, clean, and classified disaster data (text, image, audio, and video) reported in the previous research articles [6,8,9,13] to develop the interactive dashboard.…”

“…In the past, several studies have been conducted on capturing useful disaster-related information from social media text [6], such as disaster topic classification [7][8][9][10], audio-video event detection [8], image detection, and classification algorithms for image-based crisis event detection [11][12][13]. The previous studies are interesting in that the social media data (text, images, audio, and video) were not only used to detect the disaster, but they were further preprocessed and modeled to classify the severity and nature of the event.…”

Automatic Classification of Eyewitness Messages for Disaster Events Using Linguistic Rules and ML/AI Approaches

“…However, identifying location-related keywords does not guarantee that a disaster event has occurred at the location mentioned in the tweets. Using users' location through GPS information attached to their posts is more reliable; however, the research reveals that only 1-3% of Tweets are geotagged, and relying solely on them may not provide enough data for decision-making [1,27].…”