2021
DOI: 10.2196/23593
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Concerns Discussed on Chinese and French Social Media During the COVID-19 Lockdown: Comparative Infodemiology Study Based on Topic Modeling

Abstract: Background During the COVID-19 pandemic, numerous countries, including China and France, have implemented lockdown measures that have been effective in controlling the epidemic. However, little is known about the impact of these measures on the population as expressed on social media from different cultural contexts. Objective This study aims to assess and compare the evolution of the topics discussed on Chinese and French social media during the COVID-… Show more

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Cited by 20 publications
(14 citation statements)
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“…In contrast, the science of COVID-19 and its related topics ( Caulfield et al, 2021 ; O'Connor et al, 2021 ; Scheufele et al, 2021 ; Schück et al, 2021 ), such as pseudo-science and misinformation or fact-checking and correction were not central to COVID-19 representations. During the analysis period, the topic of science on COVID-19 constituted around 10% of the total data (stage 1: 11.32%, stage 2: 6.34%, stage 3: 6.78%).…”
Section: Discussionmentioning
confidence: 91%
See 1 more Smart Citation
“…In contrast, the science of COVID-19 and its related topics ( Caulfield et al, 2021 ; O'Connor et al, 2021 ; Scheufele et al, 2021 ; Schück et al, 2021 ), such as pseudo-science and misinformation or fact-checking and correction were not central to COVID-19 representations. During the analysis period, the topic of science on COVID-19 constituted around 10% of the total data (stage 1: 11.32%, stage 2: 6.34%, stage 3: 6.78%).…”
Section: Discussionmentioning
confidence: 91%
“…Primarily, multiple representations of the object will emerge, and their relative prominence will shift during the progression of the crises. Several recent studies have applied the topic modeling of social media data and revealed that the topics focused on specific keyword combinations (e.g., ‘home, stay, lockdown’ or ‘vaccine, cure, disease’), which were largely interpreted against the corresponding events and evolving pandemic policies ( Schück et al, 2021 ). However, keyword combinations only suggest possible topics that represent public attention or opinion; more nuanced interpretations of the topics were lacking and relationships between topics (informed by the dynamic processes of dispersion and focalization across multiple social groups) have not been considered in the literature so far.…”
Section: Literature Reviewmentioning
confidence: 99%
“…First, the cross-sectional design does not allow causal inferences about relationships between variables to be determined. Furthermore, missing data precluded the investigation of EPPM appraisal in the total study sample, and some novel measures such as “location tracking” [ 19 ] or “COVID-19 passport” were omitted. Second, personality variables such as anxiety trait and pessimism may have a pivotal influence on appraisals and were not assessed.…”
Section: Discussionmentioning
confidence: 99%
“…As a result, protests against restrictions emerged and rule violations increased, provoking peaks in new positive cases [ 17 ], forcing authorities to impose fines to slow down the spread of COVID-19 [ 18 ]. Due to these challenges, subsequent implementations of nonpharmaceutical measures in response to COVID-19 recurrences or other pandemics could present difficulties for decision makers [ 19 ]. A study examining acceptance of different scenarios showed that lockdown length affected respondents’ reactions much more strongly than intensity or flexibility [ 20 ].…”
Section: Introductionmentioning
confidence: 99%
“…Recently, topic modeling on data collected via social network services (SNSs), such as Twitter, and web portals is widely used for the survey of public perceptions and attitudes toward the COVID-19 outbreak [ 6 , 7 ], containment strategies [ 8 , 9 ], treatment interventions [ 6 ], and vaccines [ 10 , 11 ]. Topic modeling on SNS data is useful for examining issues that change quickly over time [ 12 ].…”
Section: Introductionmentioning
confidence: 99%