Social Media Visuals and Users’ Attitudes Towards COVID-19 Vaccines: A Cluster Analysis Study | ||||
| المجلة العربية لبحوث الاعلام والاتصال | ||||
| Article 9, Volume 2021, Issue 35, December 2021, Page 82-98 PDF (294.51 K) | ||||
| Document Type: بحوث علمیة متخصصة فی مجال الاعلام والاتصال. | ||||
| DOI: 10.21608/jkom.2021.222479 | ||||
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| Author | ||||
| Nesrin N. El-Sherbini* | ||||
| Faculty of Mass Communication, MSA University | ||||
| Abstract | ||||
| Social media have rapidly grown as a main source of news in the last decade, and perhaps recently it has overtaken the traditional news sources such as TV news channels, and newspapers. Social media visuals provide easier access and faster processing of information. When this information is taken in a comical context in the form of memes, videos, posters, images, and emojis, they become more persuasive and remembered for longer period than text posts. Health care information is one of the most sought areas and as a result has become a common topic in social media. With people’s engagement on social media, people relied on online information in making informed decisions about healthcare providers, hospitals, and any other health-related concerns. With the emergence of the Covid-19 and its vaccinations, information about the COVID-19 Vaccines has become amongst the most trending searches. Consequently, visual messaging on social media may create a significant association with people’s intents to get vaccinated — not only against COVID-19, but also for other immunizations. Within the framework of the theory of Reasoned Action and Reasoned Actions Approach, this study intended to investigate the relation between visuals on social media and users’ attitudes towards the COVID-19 Vaccines, using online survey. The study employed the Data mining technique of K-mean Clustering for the obtained responses which revealed the determinants of intents of users that affect their attitudes towards the vaccines. | ||||
| Keywords | ||||
| Social Media Visuals; Theory of Reasoned Actions; Data Mining; K-mean Clustering | ||||
| References | ||||
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