The assessment of Twitter discourse on the new COVID-19 variant, XBB.1.5, through social network analysis

Ikhwan Yuda Kusuma; Hening Pratiwi; Shafa Fitri Khairunnisa; Dian Ayu Eka Pitaloka; Arie Arizandi Kurnianto

doi:10.1016/j.jvacx.2023.100322

The assessment of Twitter discourse on the new COVID-19 variant, XBB.1.5, through social network analysis

Vaccine X. 2023 Aug:14:100322. doi: 10.1016/j.jvacx.2023.100322. Epub 2023 Jun 7.

Authors

Ikhwan Yuda Kusuma¹, Hening Pratiwi², Shafa Fitri Khairunnisa^{3

4}, Dian Ayu Eka Pitaloka^{3

4}, Arie Arizandi Kurnianto⁵

Affiliations

¹ Pharmacy Study Program, Faculty of Health, Universitas Harapan Bangsa, Purwokerto 53182, Indonesia.
² Department of Pharmacy, Faculty of Health Sciences, Jenderal Soedirman University, Purwokerto 53122, Indonesia.
³ Center of Excellence in Higher Education for Pharmaceutical Care Innovation, Universitas Padjadjaran, Sumedang 45363, Indonesia.
⁴ Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang 45363, Indonesia.
⁵ Doctoral School of Health Sciences, Faculty of Health Sciences, University of Pécs, Pécs, Hungary.

Abstract

Background: XBB.1.5 is a new subvariant of the SARS-CoV-2 Omicron variant with increased transmissibility and immune escape potential. Twitter has been used to share information and assess this subvariant.

Objectives: This study aims to investigate the channel graph, key influencers, top sources, most trends, and pattern discussion, as well as sentiment measures related to Covid-19 XBB.1.5 variant, by using social network analysis (SNA).

Methods: This experiment involved the collection of Twitter data through the keywords, "XBB.1.5″, and NodeXL, with the obtained information subsequently cleaned to remove duplication and irrelevant tweets. SNA was also performed by using analytical metrics to identify influential users and understand the patterns of connectivity among those discussing XBB.1.5. on Twitter. Moreover, the results were visualized through Gephi software, with sentiment analysis performed by using Azure Machine Learning to categorize tweets into three categories, namely positive, negative, and neutral.

Results: A total of 43,394 XBB.1.5-based tweets were identified, with five key users observed with the highest betweenness centrality score (BCS), namely "ojimakohei"(red), mikito_777 (blue), "nagunagumomo" (green), "erictopol" (orange), w2skwn3 (yellow). The other hand, the in-degree, out-degree, betweenness, closeness, and eigenvector centrality scores of the top 10 Twitter users to explain various patterns and trends and "ojimakohei" was highly central in the network. Most of the top domains (sources) used in XBB.1.5 discourse originated from Twitter, Japanese websites (co.jp and or.jp), and scientific analysis links (biorxiv.org and cdc.gov). This analysis indicated that most of the tweets (61.35 %) were positively classified, accompanied by neutral (22.44 %) and negative (16.20 %) sentiments.

Conclusion: Japan was actively engaged in evaluating the XBB.1.5 variant, with influential users playing a crucial role. The preference for sharing verified sources and the positive sentiment demonstrated a commitment to health awareness. We recommend fostering collaborations between health organizations, the government, and Twitter influencers to address COVID-19-related misinformation and its variants.

Keywords: Covid-19; Sentiments; Social network; Twitter; Variant; XBB.1.5.