بررسی الگوی رفتاری نسل‌های ایکس، وای، زد در مواجهه با محتوای کرونا در شبکه اجتماعی توییتر

Objective
This research aims to uncover the behavioral patterns of Persian-speaking Twitter users centered on the COVID-19 disease. Two hypotheses, namely the difference in the emotional content of tweets published by each generation and the examination of their emotional reactions to specific COVID-19 news, were studied during the first year of the pandemic in Iran. this research focuses on the news published about COVID-19 on Twitter due to the prevalence of the COVID-19 topic among all audiences in different generations and its high percentage of risk at the micro (psychophysical health, etc.) and macro (economy, etc.) levels, with a high influence among the audience. Unlike similar previous crises, this pandemic had a serious distinction, that is, it was the pervasive social networks worldwide, with a 25% growth in Twitter users during the spread of this disease. Therefore, accurate and comprehensive information can be expected to be obtained by studying the opinions of users on social networks from a public perspective around a topic. Since the unknown dimensions of this disease and its transmission require citizens' cooperation with the health policies of governments, the perception of media audiences from COVID-19-related content has been focused on more than ever by the media and health policymakers. Despite extensive research on the analysis of users' emotions in cyberspace, a few studies have analyzed the emotions of different generations in the face of crises. The results indicate different behaviors of generations in dealing with COVID-19 content on Twitter, and this generational distinction can help policymakers plan for these groups according to the emotions expressed in each generation.
Uses and Gratifications theory and the theory of reception were used to explain the positions of the audience in the communication process and their perception manner of the message. According to this view, audiences are active elements that are capable of selecting and interpreting published content based on their backgrounds and characteristics. In this research, these effective backgrounds in interpretation are considered generational components.
According to the literature review in this field, the categorization of generations in this research is as follows. Generation X (X) comprises those born from the 1920s to the 1960s, whose socialization is related to before the Islamic Revolution, and their main mass media were radio, television, newspapers, and books. Socialization in Generation Y (Y) dates back to the early years of the revolution and war to the early presidency of Mohammad Khatami and the beginning of deep political-cultural changes in Iran. Accordingly, people aged 23-40 years fall into this category, who became especially familiar with the satellite and the first generation of computer communications, in addition to using the media of the previous generation. Blogs, video games, and Web 1 are the most important media features of this generation. Generation Z (Z) refers to a group aged between 11 and 23 years, who are colloquially referred to as digital natives or the network generation. Unlike previous generations, this generation has never seen a world without digital communication technology and interactive virtual social networks. Microblogs are the most important media feature of this generation.
Research Methodology
Subsequently, different generations of Persian users of this platform were categorized through data mining techniques. Then, their tweets were collected manually, categorized, and modeled with advanced search tools and keywords. About 500 tweets were collected monthly using the total counting method due to the limited number of accounts with an image for age estimation. The age and gender of the user were estimated using two methods. Initially, age was estimated based on the user’s profile picture using the facenet network. In the second method, age was estimated based on the user’s interest vector using the support vector machine model. There were two parts in the sentiment analysis: feature extraction and classification. The former was performed with the wordtovector model, which was trained using a sample of the Hamshahri newspaper (a rich sample of the Persian language). In the second part, these tweets were classified and an emotion type was attributed to them with the LSTM model using 80% of the data to train the machine and 20% for the test.
Findings
In the sample group, three fear, humor, and criticism emotions showed the most manifestations to describe the emotional content of tweets. Ultimately, the behavioral pattern of Generation Z on Twitter was accompanied by the publication of humorous content, while it presented a lower critical aspect. This pattern reverses as it moves toward Generations X and Y whereas the fear aspect had a roughly similar trend and volume in the themes of all users.
Discussion & Conclusion
Between 20 and 30% of all COVID-19-centered messages produced by the audience contained fearful content. This rate was the same for each age group, and this similarity could be seen in the interpretation of generational convergence. However, this convergence was not seen in the other two emerging emotions, namely the feeling of criticism and humor. Generation Z often had a humorous attitude in dealing with the limitations resulting from COVID-19 and its widespread consequences. It seems that they present completely contradictory perceptions, interpretations, and, consequently, feedback on the COVID-19 phenomenon, which is initially a scary and dangerous subject for human personal and social life, is. This difference can be categorized in the proposition of the generational gap or conflict.