Machine Learning Identification on Twitter Towards Combating Covid-19 (SARS-CoV-2): Pandemic Attacks and Urban Resilience in Indonesia

Abstract

The purpose of this study is to detect the topic and discourse of the development of covid-19 and its resistance on social media Twitter in Indonesia. This research method is surveyed through Machine Learning to filter, track, and predict the spread of Covid-19 (SARS-CoV-2) on Twitter, and is assisted by Nvivo 12 pro and Ncapture analysis tools to collect data from big data on Twitter. The results of this study show ML (Machine Learning) plays a role in fighting the virus, especially looking at it from the perspective of screening, forecasting, and vaccines spread across various social media accounts on Twitter. A comprehensive survey of ML algorithms and models used in covid-19 (SARS-CoV-2) development expeditions on Twitter can help fight the virus. This research shows that the hashtag #lawancovid19 (Fight Covid-19) has relevance to several new hashtags that are still relevant in campaigning against covid-19 on Twitter to combat community and urban vulnerabilities in Indonesia. Collectively, characteristics such as Tagar #ayovaksin (let's get vaccinated), #jagajarak (keep your distance), and #ayopakaimasker (let's wear masks) support the resistance to covid-19 in Indonesia the account (@Username) that is most often @mention in the covid-19 cloud l action is the account of the @jokowi (President of Indonesia). The social media movement (Twitter) in encouraging community resilience and urban resilience through digital communication against Covid-19 (SARS-CoV-2) has predominantly succeeded in shaping understanding, behavior, and prudence in interacting directly in public spaces. This is important in the context of assessing messages and communications within the overall social media (Twitter) communication activities in response to online resistance measures in support of public health as well as to digitally address the global pandemic. This research contributes to providing insight into the dynamics of covid-19 (SARS-CoV-2) combat communication on Social-Media (Twitter) and supporting public health measures.