Sentiment Analysis of Tweets in Saudi Arabia Regarding Governmental Preventive Measures to Contain COVID-19

Abstract

Background: Countries around the world are facing extraordinary challenges in implementing various measures to slow down the spread of the novel coronavirus (COVID-19). Guided by international recommendations, Saudi Arabia has implemented a series of infection control measures after the detection of the first confirmed case in the country. However, in order for these measures to be effective, public attitudes and compliance must be conducive as perceived risk is strongly associated with health behaviors. The primary objective of this study is to assess Saudis’ attitudes towards COVID-19 preventive measures to guide future health communication content. Methods: Naïve Bayes machine learning model was used to run Arabic sentiment analysis of Twitter posts through the Natural Language Toolkit (NLTK) library in Python. Tweets containing hashtags pertaining to seven public health measures imposed by the government were collected and analyzed. Results: A total of 53,127 tweets were analyzed. All measures, except one, showed more positive tweets than negative. Measures that pertain to religious practices showed the most positive sentiment. Discussion: Saudi Twitter users showed support and positive attitudes towards the infection control measures to combat COVID-19. It is postulated that this conducive public response is reflective of the overarching, longstanding popular confidence in the government. Religious notions may also play a positive role in preparing believers at times of crises. Findings of this study broadened our understanding to develop proper public health messages and promote stronger compliance with control measures to control COVID-19.

Physical Distancing and Hand Washing During the COVID-19 Pandemic: The role of fear, perceived seriousness, and health knowledge

“…Arabia, which is also consistent with reports that the infection control measures in Saudi Arabia are generally well perceived (Alhajji et al 2020).…”

Awareness of the preventive measures against Covid -19 spread among small shop owners

“…It is clear that the majority of the participants are aware of the preventive measures against Covid virus (Agrawal et al, 2020). 66% of the participants are aware that corona spread is caused due to the touch of goods and it is similar to the study of (Bourouiba, 2020), 58% of the respondents of this study are aware, rest of the participants are not aware of the fact that covid spread is caused by the touch of goods (Alhajji et al, 2020). Nearly 68% of the shopkeepers are aware and they are particular in asking their customers to wear masks when they come to their shops and make them stand a one-meter distance from each other.…”

“…In the future, the awareness of the preventive measures against covid 19 can be studied in detail. (Alhajji et al, 2020;Chen et al, 2020;Palati et al, 2020). In a study, the novel coronavirus was renamed as COVID-19 by WHO.…”

A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia

“…In the study presented by Alhajji et al [ 20 ], sentiment analysis of Arabic tweets was conducted by applying NB to run Arabic sentiment analysis of tweets through a Python library: Natural Language Toolkit (NLTK). They avoided laborious work by using a labeled dataset of Arabic tweets in which its sentiment labels are based on emoji lexicons.…”