scholarly journals Analyzing Social Media to Explore the Attitudes and Behaviors Following the Announcement of Successful COVID-19 Vaccine Trials: Infodemiology Study

10.2196/28800 ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. e28800
Author(s):  
Jean-Christophe Boucher ◽  
Kirsten Cornelson ◽  
Jamie L Benham ◽  
Madison M Fullerton ◽  
Theresa Tang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Content Analysis ◽  
Social Network ◽  
Multinational Corporations ◽  
Vaccine Hesitancy ◽  
Media Content ◽  
Media Content Analysis ◽  
Vaccine Trials ◽  
The Government

Background The rollout of COVID-19 vaccines has brought vaccine hesitancy to the forefront in managing this pandemic. COVID-19 vaccine hesitancy is fundamentally different from that of other vaccines due to the new technologies being used, rapid development, and widespread global distribution. Attitudes on vaccines are largely driven by online information, particularly information on social media. The first step toward influencing attitudes about immunization is understanding the current patterns of communication that characterize the immunization debate on social media platforms. Objective We aimed to evaluate societal attitudes, communication trends, and barriers to COVID-19 vaccine uptake through social media content analysis to inform communication strategies promoting vaccine acceptance. Methods Social network analysis (SNA) and unsupervised machine learning were used to characterize COVID-19 vaccine content on Twitter globally. Tweets published in English and French were collected through the Twitter application programming interface between November 19 and 26, 2020, just following the announcement of initial COVID-19 vaccine trials. SNA was used to identify social media clusters expressing mistrustful opinions on COVID-19 vaccination. Based on the SNA results, an unsupervised machine learning approach to natural language processing using a sentence-level algorithm transfer function to detect semantic textual similarity was performed in order to identify the main themes of vaccine hesitancy. Results The tweets (n=636,516) identified that the main themes driving the vaccine hesitancy conversation were concerns of safety, efficacy, and freedom, and mistrust in institutions (either the government or multinational corporations). A main theme was the safety and efficacy of mRNA technology and side effects. The conversation around efficacy was that vaccines were unlikely to completely rid the population of COVID-19, polymerase chain reaction testing is flawed, and there is no indication of long-term T-cell immunity for COVID-19. Nearly one-third (45,628/146,191, 31.2%) of the conversations on COVID-19 vaccine hesitancy clusters expressed concerns for freedom or mistrust of institutions (either the government or multinational corporations) and nearly a quarter (34,756/146,191, 23.8%) expressed criticism toward the government’s handling of the pandemic. Conclusions Social media content analysis combined with social network analysis provides insights into the themes of the vaccination conversation on Twitter. The themes of safety, efficacy, and trust in institutions will need to be considered, as targeted outreach programs and intervention strategies are deployed on Twitter to improve the uptake of COVID-19 vaccination.

scholarly journals Analyzing Social Media to Explore the Attitudes and Behaviors Following the Announcement of Successful COVID-19 Vaccine Trials: Infodemiology Study (Preprint)

2021 ◽  
Author(s):  
Jean-Christophe Boucher ◽  
Kirsten Cornelson ◽  
Jamie L Benham ◽  
Madison M Fullerton ◽  
Theresa Tang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Content Analysis ◽  
Social Network ◽  
Multinational Corporations ◽  
Vaccine Hesitancy ◽  
Media Content ◽  
Media Content Analysis ◽  
Vaccine Trials ◽  
The Government

BACKGROUND The rollout of COVID-19 vaccines has brought vaccine hesitancy to the forefront in managing this pandemic. COVID-19 vaccine hesitancy is fundamentally different from that of other vaccines due to the new technologies being used, rapid development, and widespread global distribution. Attitudes on vaccines are largely driven by online information, particularly information on social media. The first step toward influencing attitudes about immunization is understanding the current patterns of communication that characterize the immunization debate on social media platforms. OBJECTIVE We aimed to evaluate societal attitudes, communication trends, and barriers to COVID-19 vaccine uptake through social media content analysis to inform communication strategies promoting vaccine acceptance. METHODS Social network analysis (SNA) and unsupervised machine learning were used to characterize COVID-19 vaccine content on Twitter globally. Tweets published in English and French were collected through the Twitter application programming interface between November 19 and 26, 2020, just following the announcement of initial COVID-19 vaccine trials. SNA was used to identify social media clusters expressing mistrustful opinions on COVID-19 vaccination. Based on the SNA results, an unsupervised machine learning approach to natural language processing using a sentence-level algorithm transfer function to detect semantic textual similarity was performed in order to identify the main themes of vaccine hesitancy. RESULTS The tweets (n=636,516) identified that the main themes driving the vaccine hesitancy conversation were concerns of safety, efficacy, and freedom, and mistrust in institutions (either the government or multinational corporations). A main theme was the safety and efficacy of mRNA technology and side effects. The conversation around efficacy was that vaccines were unlikely to completely rid the population of COVID-19, polymerase chain reaction testing is flawed, and there is no indication of long-term T-cell immunity for COVID-19. Nearly one-third (45,628/146,191, 31.2%) of the conversations on COVID-19 vaccine hesitancy clusters expressed concerns for freedom or mistrust of institutions (either the government or multinational corporations) and nearly a quarter (34,756/146,191, 23.8%) expressed criticism toward the government’s handling of the pandemic. CONCLUSIONS Social media content analysis combined with social network analysis provides insights into the themes of the vaccination conversation on Twitter. The themes of safety, efficacy, and trust in institutions will need to be considered, as targeted outreach programs and intervention strategies are deployed on Twitter to improve the uptake of COVID-19 vaccination.

scholarly journals Examining Perceived and Projected Destination Image: A Social Media Content Analysis

2021 ◽  
Vol 13 (6) ◽  
pp. 3354
Author(s):  
Wei Sun ◽  
Shoulian Tang ◽  
Fang Liu
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Destination Image ◽  
Media Content ◽  
Media Content Analysis ◽  
Poor Understanding ◽  
Practical Implications

Destination image has been extensively studied in tourism and marketing, but the questions surrounding the discrepancy between the projected (perceptions from the National Tourism Organizations) and perceived destination image (perceptions from tourists) as well as how the discrepancy may influence sustainable experience remain unclear. Poor understanding of the discrepancy may cause tourist confusion and misuse of resources. The aim of this study is to empirically investigate if the perceived (by tourists) and projected (by NTOs) destination image are significantly different in both cognitive and affective aspects. Through a comprehensive social media content analysis of the NTO-generated and tourist-generated-contents (TGC), the current study identifies numerous gaps between the projected and perceived destination image, which offers some important theoretical and practical implications on destination management and marketing.

Marijuana and Glaucoma: A Social Media Content Analysis

2020 ◽  
Author(s):  
Jing (Sasha) Jia ◽  
Nikki Mehran ◽  
Robert Purgert ◽  
Qiang (Ed) Zhang ◽  
Daniel Lee ◽  
...  
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Media Content ◽  
Media Content Analysis

Social Media Content Analysis

10.1142/10535 ◽  
2017 ◽  
Author(s):  
Kam-Fai Wong ◽  
Wei Gao ◽  
Ruifeng Xu ◽  
Wenjie Li
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Media Content ◽  
Media Content Analysis

scholarly journals Incorporating Social Role Theory into Topic Models for Social Media Content Analysis

2015 ◽  
Vol 27 (4) ◽  
pp. 1032-1044 ◽  
Author(s):  
Wayne Xin Zhao ◽  
Jinpeng Wang ◽  
Yulan He ◽  
Jian-Yun Nie ◽  
Ji-Rong Wen ◽  
...  
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Role Theory ◽  
Social Role ◽  
Topic Models ◽  
Media Content ◽  
Media Content Analysis ◽  
Social Role Theory

scholarly journals Ultra-Right Radicalization: a Method of Automated Threat Detection with the Use of Web Mining

2021 ◽  
pp. 30-43
Author(s):  
Anna Karpova ◽  
Aleksey Savel'ev ◽  
Aleksandr Vil'nin ◽  
Anastasiya Kayda ◽  
Sergey Kuznecov ◽  
...  
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Web Mining ◽  
Social Science Research ◽  
Science Research ◽  
Automated Detection ◽  
Media Content ◽  
Threat Detection ◽  
Media Content Analysis ◽  
Current Trends

The paper provides a brief review of current trends in studying ultra-right radicalization risks both in Russia and globally. Since the scientific interpretations in studying the notion of radicalization are differentiated, the authors prefer the following one: the ultra-rightists represent communities and movements that accept the idea that violence is necessary to achieve any goal (political, ideological, economic, social or personal). The ultra-rightists justify and promote this idea, expressing their willingness to act violently. They also make a moral commitment to defend those who promote the idea. The authors present the results of the work of the TPU’s cross-subject project team to create a prototype and a method for automated detection of ultra-rightists’ threats in social media. The paper describes the main challenges the researches face when applying smart social media content analysis as a tool for automating social science research.

scholarly journals Relational Environmentalism in Coastal Recreation and Tourism

2019 ◽  
Vol 11 (21) ◽  
pp. 6011 ◽  
Author(s):  
Anne-Mette Hjalager ◽  
Grzegorz Kwiatkowski
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Primary Data ◽  
Media Content ◽  
Natural Environments ◽  
Media Content Analysis ◽  
Marine Litter ◽  
Theoretical Contribution ◽  
Recreation And Tourism ◽  
Coastal Recreation

Given the extensive challenge of marine litter faced by coastal ecosystems, this article aims to illuminate an innovative form of environmental caretaking that builds upon a newly established concept of relational environmentalism. Relational environmentalism is a movement of individuals who purposefully interact with each other and with external bodies in a variety of dynamically developing ways to affect the perceptions, motivations and practical actions for the caretaking of endangered natural environments. As a theoretical contribution, the article conceptualizes eight categories of relational environmentalism: inviting, informing, coaching, norm enforcing, politicizing, mobilizing, intergeneralizing, and bridging. By means of a social media content analysis and primary data from the “Marine Environment Patrol” Facebook site, the article provides the first evidence on what relational environmentalism is and how it is institutionalized in the case of leisure- and tourism-based volunteering to collect marine litter. Furthermore, the article shows that successful campaigning and environmental patrolling in coastal recreation and tourism is a matter of building alliances and exchanging logics across a variety of boundaries and that it depends on a gradual intensification and diversification of communicative and mobilizing measures.

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