Exploring Occupation Differences in Reactions to COVID-19 Pandemic on Twitter

Zhao, Yi; Huang, Xi; Zhang, Chengzhi

doi:10.2478/dim-2020-0032

Cited by 9 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this analysis, the Python Gensim module was used [ 62 ]. LDA topic exploration has previously been used in studies of occupational differences in reactions to the COVID-19 pandemic [ 63 ], health informatics [ 64 ], the United States presidential election [ 65 ], and online food delivery [ 66 ].…”

Section: Methodsmentioning

confidence: 99%

Cultured Meat on the Social Network Twitter: Clean, Future and Sustainable Meats

Pilařová

Stanislavská

Pilař

et al. 2022

Foods

View full text Add to dashboard Cite

The rapid development of technologies for cultured meat production has led to new challenges for producers regarding appropriate communication with future customers in order to deliver products to a viable market. Communication analysis of social media enables the identification of the key characteristics of the monitored topic, as well as the main areas of communication by individual users based on active digital footprints. This study aimed to identify the key characteristics of cultured meat based on communication analysis of the social network Twitter. Communication analysis was performed based on 36,356 Tweets posted by 4128 individual users. This analysis identified the following main communicated characteristics: clean meat, future meat, and sustainable meat. Latent Dittrich allocation identified five communication topics: (1) clean and sustainable products, (2) comparisons with plant-based protein and the impact on agribusiness, (3) positive environmental aspects, (4) cultured meat as an alternative protein, and (5) the regulation of cultured meat.

show abstract

Section: Methodsmentioning

confidence: 99%

Cultured Meat on the Social Network Twitter: Clean, Future and Sustainable Meats

Pilařová

Stanislavská

Pilař

et al. 2022

Foods

View full text Add to dashboard Cite

show abstract

“…Moreover, national-level and individual-level cultural differences have been shown to profoundly differentially affect technology use and communication ( Lowry et al., 2011 ), and such difference were also shown in respect to willingness to use contact-tracing apps during COVID-19 ( Altmann et al., 2020 ). Moreover, indications are that there were profound differences in the public's reaction to the COVID-19 pandemic across countries and cultures ( Bajaj et al., 2021 ; Xie et al., 2021 ), and there were even differences in reactions on Twitter to the pandemic based on occupation ( Zhao et al., 2021 ). Thus, national- and individual-level cultural differences should also be investigated for the context of political communication style during crisis management on social media.…”

Section: Discussionmentioning

confidence: 99%

“Do as I say but not as I do”: Influence of political leaders’ populist communication styles on public adherence in a crisis using the global case of COVID-19 movement restrictions

Liu¹,

Mirkovski²,

Lowry³

et al. 2023

Data and Information Management

View full text Add to dashboard Cite

“…There was a significant feeling of fear when people discussed new COVID-19 cases and deaths compared to other topics. Zhao et al (2020) conducted topic and sentiment analysis from the perspective of occupation by leveraging Latent Dirichlet allocation (LDA) topic modeling and Valence Aware Dictionary and sEntiment Reasoning (VADER) model. They found significant topic preference differences between Twitter users with different occupations.…”

Section: Social Media Analytics In Health Crisismentioning

confidence: 99%

Using health belief model and social media analytics to develop insights from hospital-generated twitter messaging and community responses on the COVID-19 pandemic

Tian

et al. 2023

JEIM

View full text Add to dashboard Cite

PurposeThis study aims to examine how different hospitals utilize social media to communicate risk information about COVID-19 with the communities they serve, and how hospitals' social media messaging (firm-generated content and their local community's responses (user-generated content) evolved with the COVID-19 outbreak progression.Design/methodology/approachThis research proposes a healthcare-specific social media analytics framework and studied 68,136 tweets posted from November 2019 to November 2020 from a geographically diverse set of ten leading hospitals' social media messaging on COVID-19 and the public responses by using social media analytics techniques and the health belief model (HBM).FindingsThe study found correlations between some of the HBM variables and COVID-19 outbreak progression. The findings provide actionable insight for hospitals regarding risk communication, decision making, pandemic awareness and education campaigns and social media messaging strategy during a pandemic and help the public to be more prepared for information seeking in the case of future pandemics.Practical implicationsFor hospitals, the results provide valuable insights for risk communication practitioners and inform the way hospitals or health agencies manage crisis communication during the pandemic For patients and local community members, they are recommended to check out local hospital's social media sites for updates and advice.Originality/valueThe study demonstrates the role of social media analytics and health behavior models, such as the HBM, in identifying important and useful data and knowledge for public health risk communication, emergency responses and planning during a pandemic.

show abstract

Exploring Occupation Differences in Reactions to COVID-19 Pandemic on Twitter

Cited by 9 publications

References 29 publications

Cultured Meat on the Social Network Twitter: Clean, Future and Sustainable Meats

Cultured Meat on the Social Network Twitter: Clean, Future and Sustainable Meats

“Do as I say but not as I do”: Influence of political leaders’ populist communication styles on public adherence in a crisis using the global case of COVID-19 movement restrictions

Using health belief model and social media analytics to develop insights from hospital-generated twitter messaging and community responses on the COVID-19 pandemic

Contact Info

Product

Resources

About