Mapping the Mpox discourse: A network and sentiment analysis
Mpox, a zoonotic disease re-emerging from animals to humans, poses a risk of evolving into a global pandemic due to its high infectivity and potential asymptomatic transmission. This study maps the structure and configuration of mpox-related discussions on Twitter/X, identifies key influencers and t...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Exploratory Research in Clinical and Social Pharmacy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667276624001185 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850168477095034880 |
|---|---|
| author | Ikhwan Yuda Kusuma Ádám Visnyovszki Muh Akbar Bahar |
| author_facet | Ikhwan Yuda Kusuma Ádám Visnyovszki Muh Akbar Bahar |
| author_sort | Ikhwan Yuda Kusuma |
| collection | DOAJ |
| description | Mpox, a zoonotic disease re-emerging from animals to humans, poses a risk of evolving into a global pandemic due to its high infectivity and potential asymptomatic transmission. This study maps the structure and configuration of mpox-related discussions on Twitter/X, identifies key influencers and top hashtags, and analyzes public sentiment. Data were collected using NodeXL Pro from May 7, 2022, to January 15, 2023, with the keyword “Monkeypox” and visualized using Gephi. Social network analysis ranked nodes by betweenness centrality scores to highlight key communicators, and the YifanHu layout algorithm visualized the network. Influential users, source topics, and hashtags were identified, and sentiment analysis was conducted using Azure Machine Learning tools. The analysis identified 11,397 mpox-related tweets. The network structure resembled a community with diverse participants. Influential users included health and science journalists, writers, academics, medical doctors, and public figures. News media and organizational websites were the top information sources, emphasizing the need for accessible scientific information. “Monkeypox” and “Mpox” were the most prevalent hashtags. Negative sentiments dominated the discussion. This analysis provides insights into network structure, key influencers, information sources, and public sentiment, aiding tailored health initiatives to address misinformation and advocate valid health information and emergency responses. |
| format | Article |
| id | doaj-art-b17ea4f7a03345c3bc1caf1ee03e2894 |
| institution | OA Journals |
| issn | 2667-2766 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Exploratory Research in Clinical and Social Pharmacy |
| spelling | doaj-art-b17ea4f7a03345c3bc1caf1ee03e28942025-08-20T02:20:57ZengElsevierExploratory Research in Clinical and Social Pharmacy2667-27662024-12-011610052110.1016/j.rcsop.2024.100521Mapping the Mpox discourse: A network and sentiment analysisIkhwan Yuda Kusuma0Ádám Visnyovszki1Muh Akbar Bahar2Institute of Clinical Pharmacy, University of Szeged, 6725 Szeged, Hungary; Pharmacy Study Program, Faculty of Health, Universitas Harapan Bangsa, 53182 Purwokerto, IndonesiaInstitute of Clinical Pharmacy, University of Szeged, 6725 Szeged, Hungary; Department of Pediatric Hematology and Stem Cell Transplantation, South-Pest Central Hospital National Institute of Hematology and Infectious Diseases, 1097 Szeged, HungaryDepartment of Pharmacy, Faculty of Pharmacy, Universitas Hasanuddin, 90245 Makassar, Indonesia; Corresponding author.Mpox, a zoonotic disease re-emerging from animals to humans, poses a risk of evolving into a global pandemic due to its high infectivity and potential asymptomatic transmission. This study maps the structure and configuration of mpox-related discussions on Twitter/X, identifies key influencers and top hashtags, and analyzes public sentiment. Data were collected using NodeXL Pro from May 7, 2022, to January 15, 2023, with the keyword “Monkeypox” and visualized using Gephi. Social network analysis ranked nodes by betweenness centrality scores to highlight key communicators, and the YifanHu layout algorithm visualized the network. Influential users, source topics, and hashtags were identified, and sentiment analysis was conducted using Azure Machine Learning tools. The analysis identified 11,397 mpox-related tweets. The network structure resembled a community with diverse participants. Influential users included health and science journalists, writers, academics, medical doctors, and public figures. News media and organizational websites were the top information sources, emphasizing the need for accessible scientific information. “Monkeypox” and “Mpox” were the most prevalent hashtags. Negative sentiments dominated the discussion. This analysis provides insights into network structure, key influencers, information sources, and public sentiment, aiding tailored health initiatives to address misinformation and advocate valid health information and emergency responses.http://www.sciencedirect.com/science/article/pii/S2667276624001185MpoxMonkeypoxSocial networkSentimentTwitter/X |
| spellingShingle | Ikhwan Yuda Kusuma Ádám Visnyovszki Muh Akbar Bahar Mapping the Mpox discourse: A network and sentiment analysis Exploratory Research in Clinical and Social Pharmacy Mpox Monkeypox Social network Sentiment Twitter/X |
| title | Mapping the Mpox discourse: A network and sentiment analysis |
| title_full | Mapping the Mpox discourse: A network and sentiment analysis |
| title_fullStr | Mapping the Mpox discourse: A network and sentiment analysis |
| title_full_unstemmed | Mapping the Mpox discourse: A network and sentiment analysis |
| title_short | Mapping the Mpox discourse: A network and sentiment analysis |
| title_sort | mapping the mpox discourse a network and sentiment analysis |
| topic | Mpox Monkeypox Social network Sentiment Twitter/X |
| url | http://www.sciencedirect.com/science/article/pii/S2667276624001185 |
| work_keys_str_mv | AT ikhwanyudakusuma mappingthempoxdiscourseanetworkandsentimentanalysis AT adamvisnyovszki mappingthempoxdiscourseanetworkandsentimentanalysis AT muhakbarbahar mappingthempoxdiscourseanetworkandsentimentanalysis |