Tracking And Understanding Public Reaction During COVID-19: Saudi Arabia As A Use Case

Abstract

The coronavirus disease of 2019 (COVID-19) has a huge impact on economies and societies around the world. While governments are taking extreme measures to reduce the spread of the virus, people are getting affected by these new measures. With restrictions like lockdown and social distancing, it became important to understand the emotional response of the public towards the pandemic. In this paper, we study the reaction of Saudi Arabia citizens towards the pandemic. We utilize a collection of Arabic tweets that were sent during 2020, primarily through hashtags that were originated from Saudi Arabia. Our results showed that people had kept a positive reaction towards the pandemic. This positive reaction was at its highest at the beginning of the COVID-19 crisis and started to decline as time passes. Overall, the results showed that people were so supportive of each other through this pandemic. This research can help researchers and policymakers in understanding the emotional effect of a pandemic on societies.

Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function

“…Few approaches have been proposed for sentiment analysis of Arabic COVID-19 tweets [9,42,43]. Two of these approaches employed classical machine learning algorithms to classify the tweets into positive, negative, and neutral sentiments [42,43].…”

“…Few approaches have been proposed for sentiment analysis of Arabic COVID-19 tweets [9,42,43]. Two of these approaches employed classical machine learning algorithms to classify the tweets into positive, negative, and neutral sentiments [42,43]. Aljameel et al [42] trained their model on 10,623 tweets labeled manually with positive, negative, and neutral sentiments and an F1-score of 0.84 was reported using SVM with Bigram term frequencyinverse document frequency (TF-IDF).…”

“…Aljameel et al [42] trained their model on 10,623 tweets labeled manually with positive, negative, and neutral sentiments and an F1-score of 0.84 was reported using SVM with Bigram term frequencyinverse document frequency (TF-IDF). Addawood et al [43] built an Arabic sentiment lexicon to assign a sentiment value for each tweet in a dataset consisting of 129,391 Arabic tweets. Then, a SVM classifier is applied to report an accuracy and F1-score of 0.98 and 0.98, respectively.…”

Social Media Monitoring of the COVID-19 Pandemic and Influenza Epidemic With Adaptation for Informal Language in Arabic Twitter Data: Qualitative Study

“…However, they only included statistical analysis and clustering to generate summaries with some suggestion of future work. Yet, there are some studies with specific goals, such as analysis of the reaction of citizens during a pandemic [ 21 ] and identification of the most frequent unigrams, bigrams, and trigrams of tweets related to COVID-19 [ 22 ]. In addition, considering the study by Alanazi et al [ 23 ] that identified the symptoms of COVID-19 from Arabic tweets, the authors noted the limitation that they used modern standard Arabic keywords only, and it would be important to consider dialectical keywords in order to better catch tweets on COVID-19 symptoms written in Arabic, because some Arab users post on social media in their own local dialect.…”

Perception of the COVID-19 Pandemic Among Members of Saudi Society: Solidarity, Humility, and Connectivity

“…However, this effect began to decline over time. A recent study showed how people supported each other during the pandemic through social media by raising trendy hashtags on the Twitter platform [ 13 ]. Furthermore, the Saudi government designated March 2 of every year as "Health Martyr Day" as a sign of solidarity and appreciation to all healthcare workers who have lost their lives on the frontline against COVID-19 [ 14 ].…”