{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T03:10:00Z","timestamp":1784949000832,"version":"3.55.0"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T00:00:00Z","timestamp":1761868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>China had been implementing stringent dynamic policies during the COVID-19 pandemic. In late 2022, China made a sudden policy shift from its three-year dynamic zero-COVID to the re-opening policy, which resulted in a divergence of online public opinions and varying sentiments. However, few research has been done to explore the public\u2019s sentiment changes toward this abrupt policy shift.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To better inform effective health communication regarding governments\u2019 change of policies for future initiatives, this study aims to analyze public\u2019s sentiment changes toward the launching of China\u2019s re-opening policy by using Weibo data. Our study examined 1, 423, 694 Weibo posts during the period from November 11, 2022 to January 11, 2023 to conduct a fine-grained emotion extraction. This study also used the LDA topic model to extract potential topics in Weibo posts to align topics and corresponding emotions for generating in-depth understanding.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Fluctuations of different emotions during these two months were profoundly analyzed and interpreted by taking cultural, social, and policy-related reasons into consideration. Notably, the average proportion of \u201cdisgust\u201d (24.0%) exceeded that of \u201clike\u201d (22.8%) after mid-December, while \u201chappiness\u201d exhibited a gradual increase to 12.0%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Results of this study will be essential to informing the government\u2019s effective health communication in the time of public health crisis, facilitating pandemic control and prevention, and enlightening on the maintenance of public\u2019s well-being.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2025.1692166","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T06:24:00Z","timestamp":1761891840000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Public sentiment dynamics in policy transitions: a sentiment analysis based on Weibo data"],"prefix":"10.3389","volume":"13","author":[{"given":"Xuan","family":"Ning","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruonan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dewei","family":"Lan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaofan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yupeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,31]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"e0236619","DOI":"10.1371\/journal.pone.0236619","article-title":"Enacting national social distancing policies corresponds with dramatic reduction in COVID19 infection rates","volume":"15","author":"McGrail","year":"2020","journal-title":"PLoS One"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1080\/14494035.2020.1787627","article-title":"Policy style, consistency and the effectiveness of the policy mix in China\u2019s fight against COVID-19","volume":"39","author":"Mei","year":"2020","journal-title":"Polic Soc"},{"key":"ref3","year":"2022"},{"key":"ref4","year":"2022"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1057\/palgrave.crr.1550049","article-title":"Protecting organization reputations during a crisis: the development and application of situational crisis communication theory","volume":"10","author":"Coombs","year":"2007","journal-title":"Corp Reput Rev"},{"key":"ref6","year":"2021"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"2032","DOI":"10.3390\/ijerph17062032","article-title":"The impact of COVID-19 epidemic declaration on psychological consequences: a study on active Weibo users","volume":"17","author":"Li","year":"2020","journal-title":"Int J Environ Res Public Health"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1057\/s41271-020-00247-w","article-title":"Leveraging media and health communication strategies to overcome the COVID-19 infodemic","volume":"41","author":"Mheidly","year":"2020","journal-title":"J Public Health Policy"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1086\/267990","article-title":"The agenda-setting function of mass media","volume":"36","author":"McCombs","year":"1972","journal-title":"Public Opin Q"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1080\/23808985.1992.11678816","article-title":"Risk communication: an emerging area of health communication research","volume":"15","author":"Covello","year":"1992","journal-title":"Ann Int Commun Assoc"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1016\/j.asej.2014.04.011","article-title":"Sentiment analysis algorithms and applications: a survey","volume":"5","author":"Medhat","year":"2014","journal-title":"Ain Shams Eng J"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s13278-021-00776-6","article-title":"A review on sentiment analysis and emotion detection from text","volume":"11","author":"Nandwani","year":"2021","journal-title":"Soc Netw Anal Min"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"5731","DOI":"10.1007\/s10462-022-10144-1","article-title":"A survey on sentiment analysis methods, applications, and challenges","volume":"55","author":"Wankhade","year":"2022","journal-title":"Artif Intell Rev"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1108\/JRIM-03-2020-0045","article-title":"Reading between the lines: untwining online user-generated content using sentiment analysis","volume":"15","author":"Rasool","year":"2021","journal-title":"J Res Interact Mark"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1108\/OIR-06-2020-0258","article-title":"The \u201cso-called\u201d UGC: an updated definition of user-generated content in the age of social media","volume":"46","author":"Santos","year":"2022","journal-title":"Online Inf Rev"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3572023","article-title":"Twitter sentiment analysis during covid19 outbreak","author":"Dubey","year":"2020","journal-title":"SSRN Electron J"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1057\/s41599-022-01181-w","article-title":"Online public opinion during the first epidemic wave of COVID-19 in China based on Weibo data","volume":"9","author":"Shi","year":"2022","journal-title":"Humanit Soc Sci Commun"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"102089","DOI":"10.1016\/j.ajp.2020.102089","article-title":"Sentiment analysis of nationwide lockdown due to COVID 19 outbreak: evidence from India","volume":"51","author":"Barkur","year":"2020","journal-title":"Asian J Psychiatr"},{"key":"ref19","doi-asserted-by":"crossref","DOI":"10.1109\/ICRAIE51050.2020.9358301","article-title":"Sentiment analysis on COVID-19 twitter data","author":"Vijay","year":"2020"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"15213","DOI":"10.1007\/s12144-023-05493-1","article-title":"An exploratory study of deep learning-based sentiment analysis among Weibo users in China","volume":"43","author":"Song","year":"2024","journal-title":"Curr Psychol"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"2230","DOI":"10.1186\/s12889-023-17160-y","article-title":"Analysis of the evolving factors of social media users\u2019 emotions and behaviors: a longitudinal study from China\u2019s COVID-19 opening policy period","volume":"23","author":"Zhang","year":"2023","journal-title":"BMC Public Health"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"5123","DOI":"10.1038\/s41598-024-55684-4","article-title":"Evaluating public opinions: informing public health policy adaptations in China amid the COVID-19 pandemic","volume":"14","author":"Wang","year":"2024","journal-title":"Sci Rep"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"3030","DOI":"10.1007\/s13132-024-02021-9","article-title":"Exploring the dynamic interplay of user characteristics and topic influence on Weibo: a comprehensive analysis","volume":"16","author":"Ruan","year":"2024","journal-title":"J Knowl Econ"},{"key":"ref24","article-title":"Demographic inequalities in digital spaces in China: the case of Weibo","author":"Qian","year":"2023"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"204","DOI":"10.5539\/mas.v13n1p204","article-title":"Study on the topic mining and dynamic visualization in view of LDA model","volume":"13","author":"Xie","year":"2018","journal-title":"Mod Appl Sci"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"15169","DOI":"10.1007\/s11042-018-6894-4","article-title":"Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey","volume":"78","author":"Jelodar","year":"2019","journal-title":"Multimed Tools Appl"},{"key":"ref27","first-page":"201","article-title":"A text mining research based on LDA topic modelling","author":"Tong","year":"2016"},{"key":"ref28","first-page":"341","article-title":"Normalized approach to find optimal number of topics in latent Dirichlet allocation (LDA)","author":"Hasan","year":"2021"},{"key":"ref29","author":"Huld","year":"2022"},{"key":"ref30","year":"2022"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"e06200","DOI":"10.1016\/j.heliyon.2021.e06200","article-title":"Socioeconomic factors analysis for COVID-19 US reopening sentiment with twitter and census data","volume":"7","author":"Rahman","year":"2021","journal-title":"Heliyon"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"142173","DOI":"10.1109\/ACCESS.2020.3013933","article-title":"Feeling positive about reopening? New Normal scenarios from COVID-19 US reopen sentiment analytics","volume":"8","author":"Samuel","year":"2020","journal-title":"IEEE Access"},{"key":"ref33","article-title":"How People respond to the COVID-19 pandemic on twitter: A comparative analysis of emotional Expressions from US and India","volume-title":"Arxiv","author":"Loh","year":"2023"},{"key":"ref34","volume-title":"Health communication: From theory to practice","author":"Schiavo","year":"2013"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"10","DOI":"10.17061\/phrp3022010","article-title":"Enhancing global health communication during crisis: lessons from the covid-19 pandemic","volume":"30","author":"Ratzan","year":"2020","journal-title":"Public Health Res Pract"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1016\/j.pec.2020.03.027","article-title":"Effective health communication\u2013a key factor in fighting the COVID-19 pandemic","volume":"103","author":"Finset","year":"2020","journal-title":"Patient Educ Couns"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"10453","DOI":"10.7759\/cureus.10453","article-title":"Role of mass media and public health communications in the COVID-19 pandemic","volume":"12","author":"Anwar","year":"2020","journal-title":"Cureus"},{"key":"ref38","volume-title":"Introduction to crisis and emergency risk communication","year":"2018"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1177\/0165551517703514","article-title":"Lexicon-based sentiment analysis: comparative evaluation of six sentiment lexicons","volume":"44","author":"Khoo","year":"2018","journal-title":"J Inf Sci"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1111\/padm.12845","article-title":"Effects of local government social media use on citizen compliance during a crisis: evidence from the COVID-19 crisis in China","volume":"101","author":"Jiang","year":"2023","journal-title":"Public Adm"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"101003","DOI":"10.1016\/j.jocs.2019.05.009","article-title":"Emotion and sentiment analysis from Twitter text","volume":"36","author":"Sailunaz","year":"2019","journal-title":"J. Comput. Sci"}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2025.1692166\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T06:24:01Z","timestamp":1761891841000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2025.1692166\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,31]]},"references-count":41,"alternative-id":["10.3389\/fpubh.2025.1692166"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2025.1692166","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,31]]},"article-number":"1692166"}}