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

Kusuma, Ikhwan Yuda; Pratiwi, Hening; Khairunnisa, Shafira; Pitaloka, Dian Ayu Eka; Kurnianto, Arie Arizandi

doi:10.1016/j.jvacx.2023.100322

Cited by 2 publications

(2 citation statements)

References 39 publications

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“…This indicates that individuals were inclined to share tweets, retweets, and replies from users they considered credible within their professional domains (Kothari et al, 2022 ). Similar research has been used for the identification of users involved in spreading the conspiracy related to the COVID-19 vaccine, election-based networks, and COVID-19 variants (Ahmed et al, 2020 ; Chakraborty and Mukherjee, 2023 ; Yuda Kusuma et al, 2023 ). Emerging and effective machine learning techniques, including the modified DeepWalk method for link prediction and deep attributed clustering with high-order proximity preservation, leverage both network structure and nodal attributes for prediction and clustering.…”

Section: Discussionmentioning

confidence: 99%

Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India

Ramamoorthy,

Kulothungan,

Mappillairaju

2024

Front. Artif. Intell.

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IntroductionThe utilization of social media presents a promising avenue for the prevention and management of diabetes. To effectively cater to the diabetes-related knowledge, support, and intervention needs of the community, it is imperative to attain a deeper understanding of the extent and content of discussions pertaining to this health issue. This study aims to assess and compare various topic modeling techniques to determine the most effective model for identifying the core themes in diabetes-related tweets, the sources responsible for disseminating this information, the reach of these themes, and the influential individuals within the Twitter community in India.MethodsTwitter messages from India, dated between 7 November 2022 and 28 February 2023, were collected using the Twitter API. The unsupervised machine learning topic models, namely, Latent Dirichlet Allocation (LDA), non-negative matrix factorization (NMF), BERTopic, and Top2Vec, were compared, and the best-performing model was used to identify common diabetes-related topics. Influential users were identified through social network analysis.ResultsThe NMF model outperformed the LDA model, whereas BERTopic performed better than Top2Vec. Diabetes-related conversations revolved around eight topics, namely, promotion, management, drug and personal story, consequences, risk factors and research, raising awareness and providing support, diet, and opinion and lifestyle changes. The influential nodes identified were mainly health professionals and healthcare organizations.DiscussionThe study identified important topics of discussion along with health professionals and healthcare organizations involved in sharing diabetes-related information with the public. Collaborations among influential healthcare organizations, health professionals, and the government can foster awareness and prevent noncommunicable diseases.

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Section: Discussionmentioning

confidence: 99%

Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India

Ramamoorthy,

Kulothungan,

Mappillairaju

2024

Front. Artif. Intell.

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show abstract

“…Social media analysis has been used in many areas of research interest, such as the COVID-19 [ 106 ], food [ 107 ], corporate social responsibility [ 95 ], greenwashing [ 108 ], zero waste [ 109 ], nursing education [ 110 ], burnout syndrome [ 111 ] and others.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Work-life balance Twitter insights: A social media analysis before and after COVID-19 pandemic

Kuralová,

Zychová,

Kvasničková Stanislavská

et al. 2024

Heliyon

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

Cited by 2 publications

References 39 publications

Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India

Topic modeling and social network analysis approach to explore diabetes discourse on Twitter in India

Work-life balance Twitter insights: A social media analysis before and after COVID-19 pandemic

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