ABSTRACT
The study presents an attempt to analyse how social media netizens in Malaysia responded to the calls for “Social Distancing” and “Physical Distancing” as the newly recommended social norm was introduced to the world as a response to the COVID-19 global pandemic. The pandemic drove a sharp increase in social media platforms’ use as a public health communication platform since the first wave of the COVID-19 outbreak in Malaysia in April 2020. We analysed thousands of tweets posted by Malaysians daily between January 2020 and August 2021 to determine public perceptions and interactions patterns. The analysis focused on positive and negative reactions and the interchanges of uses of the recommended terminologies “social distancing” and “physical distancing”. Using linguistic analysis and natural language processing, findings dominantly indicate influences from the multilingual and multicultural values held by Malaysian netizens, as they embrace the concept of distancing as a measure of global public health safety. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
During the unprecedented of COVID-19 pandemic, numbers of research had been conducted on mental health in social media worldwide. Past research has shown interest in Twitter sentiment analysis by using keywords, geographical area, and range of ages. Up to the authors' analysis, there is no research conducted on mental health using keyword in the case of Malaysia. A Malay Tweet dataset was built for analysing mental health tweets during the first Movement Control Order period using unique keywords. Machine learning algorithms namely, Naïve Bayes classifier and Support Vector Machine were used to predict the sentiment of tweets. The classifiers were evaluated using 10-fold cross-validation, accuracy, precision, and F1-score. The data then visualized in charts and WordCloud. The results shows that Support Vector Machine performed better than Naïve Bayes classifier for both test set and 10-fold cross-validation in terms of performances in n-gram TF-IDF. The visualized data could provide insights to the authority pertaining the mental health issues, in which it relates to local news and situations during the periods. © 2021 IEEE.