Exploring public perceptions of the COVID-19 vaccine online from a cultural perspective: Semantic network analysis of two social media platforms in the United States and China

Luo, Chen; Chen, Anfan; Cui, Botao; Liao, Wang

doi:10.1016/j.tele.2021.101712

Cited by 102 publications

(89 citation statements)

References 71 publications

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“…The U.S. Food and Drug Administration (2020) issued the Pfizer-BioNTech COVID-19 vaccine emergency-use authorization on December 11, 2020, implementing the COVID-19 vaccine as an essential measure to combat the COVID-19 epidemic. Discussions on the vaccine controversy have subsequently become one of the foci of online discussion [26].…”

Section: Vaccine Content On Social Mediamentioning

confidence: 99%

Social Bots’ Involvement in the COVID-19 Vaccine Discussions on Twitter

Zhang

Qin

Chen

et al. 2022

IJERPH

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During the COVID-19 pandemic, social media served as an important channel for the public to obtain health information and disseminate opinions when offline communication was severely hindered. Yet the emergence of social bots influencing social media conversations about public health threats will require researchers and practitioners to develop new communication strategies considering their influence. So far, little is known as to what extent social bots have been involved in COVID-19 vaccine-related discussions and debates on social media. This work selected a period of nearly 9 months after the approval of the first Covid-19 vaccines to detect social bots and performed high-frequency word analysis for both social bot-generated and human-generated tweets, thus working out the extent to which social bots participated in the discussion on the COVID-19 vaccine on Twitter and their participation features. Then, a textual analysis was performed on the content of tweets. The findings revealed that 8.87% of the users were social bots, with 11% of tweets in the corpus. Besides, social bots remained active over three periods. High-frequency words in the discussions of social bots and human users on vaccine topics were similar within the three peaks of discourse.

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Section: Vaccine Content On Social Mediamentioning

confidence: 99%

Social Bots’ Involvement in the COVID-19 Vaccine Discussions on Twitter

Zhang

Qin

Chen

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…Contrastingly, Weibo users in China comply with the authorities. Additionally, Chinese social media users expressed more positive feelings about the COVID-19 vaccine, compared with their US counterparts [ 4 ]. In Bruine de Bruin’s study, age differences in risk perceptions and mental health in the early stages of the COVID-19 outbreak in the United States were examined.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on the Cultural Dimensions and Health Perception of the COVID-19 Pandemic between China and the United States

2022

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When a public crisis such as COVID-19 occurs, factors that affect health-related behaviors, such as compliance with safety precautions, health professionals, and directives from government agencies will become more obvious. This research explores the differences between the people of the United States and China regarding preventive behavioral intentions, perceptions of personal and social risks, seriousness, and other cultural characteristics in the context of the COVID-19 health crisis. The purpose is to provide insights that can be used when global public health events occur in the future. A total of 536 people who lived in the US and China from 12 July to 7 September 2020 were recruited in the survey. Through a web-based survey, differences in the attitudes and perceptions of COVID-19 between the two countries were identified. Overall, the people of China scored higher than Americans on several measures regarding personal risk perception, social risk perception, and seriousness. Chinese citizens also had higher preventive behavioral intentions than their US counterparts. In addition, the relationships between cultural dimensions and health-related variables were also different.

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“…On the other hand, the semantic modeling of the information content published in the OSN is gaining attention. For instance, semantic analysis of social networks weibo and twitter based on single word topics has been applied to study the public perception on vaccines against COVID-19 [ 46 ]. It has been shown that semantic modeling of user contents allows for improved community detection [ 28 , 82 ].…”

Section: Related Workmentioning

confidence: 99%

Neuro-semantic prediction of user decisions to contribute content to online social networks

Cleveland

Ríos

Aguilera

et al. 2022

Neural Comput & Applic

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Understanding at microscopic level the generation of contents in an online social network (OSN) is highly desirable for an improved management of the OSN and the prevention of undesirable phenomena, such as online harassment. Content generation, i.e., the decision to post a contributed content in the OSN, can be modeled by neurophysiological approaches on the basis of unbiased semantic analysis of the contents already published in the OSN. This paper proposes a neuro-semantic model composed of (1) an extended leaky competing accumulator (ELCA) as the neural architecture implementing the user concurrent decision process to generate content in a conversation thread of a virtual community of practice, and (2) a semantic modeling based on the topic analysis carried out by a latent Dirichlet allocation (LDA) of both users and conversation threads. We use the similarity between the user and thread semantic representations to built up the model of the interest of the user in the thread contents as the stimulus to contribute content in the thread. The semantic interest of users in discussion threads are the external inputs for the ELCA, i.e., the external value assigned to each choice.. We demonstrate the approach on a dataset extracted from a real life web forum devoted to fans of tinkering with musical instruments and related devices. The neuro-semantic model achieves high performance predicting the content posting decisions (average F score 0.61) improving greatly over well known machine learning approaches, namely random forest and support vector machines (average F scores 0.19 and 0.21).

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Exploring public perceptions of the COVID-19 vaccine online from a cultural perspective: Semantic network analysis of two social media platforms in the United States and China

Cited by 102 publications

References 71 publications

Social Bots’ Involvement in the COVID-19 Vaccine Discussions on Twitter

Social Bots’ Involvement in the COVID-19 Vaccine Discussions on Twitter

A Comparative Study on the Cultural Dimensions and Health Perception of the COVID-19 Pandemic between China and the United States

Neuro-semantic prediction of user decisions to contribute content to online social networks

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