Sentiment Analysis of Covid-19 Vaccination Posts on Facebook in Indonesia With Crowdtangle

Abstract

COVID-19 was declared a pandemic by the World Health Organization (WHO) due to the virus's uncontrolled spread around the world, including in Indonesia. The Indonesian government has made a number of steps to stop COVID-19 from spreading, one of which is COVID-19 vaccinations. However, not everyone thinks the vaccination is a good idea. Just like in many other countries, Indonesian people responded in different ways to COVID-19 vaccination posts on social media, be it from government officials/agencies or news portals. Their responses can be used to help the government decide on a better vaccination strategy that will help minimize the virus spread and end the pandemic in Indonesia. Using the lexicon method in determining the content sentiment in COVID-19 vaccination posts on Facebook, this research found that unlike news portals that tend to post a more balanced content (36% positive, 20% negative, and 44% neutral out of 23,623 posts with min score = -19, max score = 24, mean score = 0.25, and SD = = 1.43), government accounts posted much more positive content, both in terms of quality (min score = -15, max score = 40, mean score = 4.16, SD = 6.76 ) and quantity (69% classified as positive) than they did the neutral (15%) and the negative content (16%) out of 723 posts. Subsequent analysis with Two-Way ANOVA tests discovered that COVID-19 vaccination posts by the news portal accounts can elicit more varied reactions from the public than government accounts that tended to elicit only positive reactions. It is also confirmed that both the content sentiment of COVID-19 vaccination posts in Indonesia and the account types making the posts as well as their interaction terms do have an impact on how the public responses to them.

U.S. Fortune 500’s stakeholders engagement during the COVID-19 pandemic: Evidence for proactive approaches

“…CrowdTangle is Facebook’s internal data archive that Facebook shares with the academic community. CrowdTangle represents one of the most comprehensive datasets on historical Facebook data (Boberg et al, 2021; Silverman, 2019 ; Rianto & Pratama, 2021 ).…”

Analisa Sentimen Efektifitas Vaksin terhadap Varian COVID 19 Omicron Berbasis Leksikon

“…Adapun analisa sentimen terkait isu tentang virus covid juga sudah banyak dilakukan (Laurensz & Eko Sediyono, 2021) (Trisnawati et al, 2021) dan sentimen terhadap penggunaan vaksin untuk mencegah viruc covid 19 (Laurensz & Eko Sediyono, 2021) (Rianto & Pratama, 2021) (Ansari & Khan, 2021). Penelitian tentang klasifikasi teks/pelabelan (positif, negatif) menggunakan pendekatan berbasis Leksikon menggunakan TextBlob dan VADER telah banyak digunakan seperti dalam penelitian V.Bonta dkk dimana hasilnya yaitu VADER mengungguli metode pelabelan berbasis leksikon lainnya (Bonta et al, 2019).…”

COVID-19 mass media coverage in English and public reactions: a West-East comparison via Facebook posts

“…Considering the double-edged sword effect mass media can generate, it is important to examine how different media portray the outbreak differently. While past studies have initiated such efforts, they mostly focused on either certain news outlets e.g ., ( Hossain, Wahab & Khan, 2022 ; Ogbodo et al, 2020 ), specific locations sharing similar cultural backgrounds e.g ., ( Mach et al, 2021 ; Rianto & Pratama, 2021 ), or relatively small number of different countries ( Wu, 2021 ). It is arguably difficult to infer a larger direct comparison, such as the differences between West and East mass media portrayals of the pandemic, let alone examine public reactions to such portrayals, due to these scopes.…”