Conclusion

This chapter discusses lessons the candlelight vigils and other similar cases offer for our understanding of how collective actions co-adapt with information ecosystems. In particular, the author discusses how empirical data analysis informs the agent-affordance framework by illustrating ways in which information generation and distribution mechanisms involve diverse agents within the information ecosystem. This chapter also discusses how insights offered in this book might be applicable to citizens’ calls for major political changes in other democratic countries. The chapter concludes by summarizing the scholarly and policy contributions of the book and suggesting a need for specific research to examine challenges for democratic governance posed by the rapidly growing volume of information available in the public sphere.

‘Good food’ in an Instagram age: Rethinking hierarchies of culture, criticism and taste

Through historical, economic and technological contextualisation and empirical data analysis, this article explores the cultural purchase the image-sharing app Instagram and the printed Michelin Guide have on contemporary food criticism. Both platforms contribute to popular understandings of ‘good food’. Yet, there are important functional and discursive distinctions in how culinary criticism is done in Instagram vis-à-vis Michelin. To that end, this article focuses on London’s restaurant scene and proposes the concept of the Instagram gaze as a means of understanding the representational repertoires and knowledge claims advanced by foodies on visual social media platforms. The Instagram gaze also facilitates insight into the relationship between Instagrammers’ culinary judgements and Michelin’ s.

Leasing as an effective tool for agricultural financing: within the example of Armenia

Through the decades’ Armenian agriculture doesn’t have sufficient growth. Technologies and methodology used by farmers are not sufficient for modern production creation. Financing system of rural areas are less effective as lack of stable income and low-price collateral doesn’t let banking institutions to actively finance the sector. The main purpose of this study is to define problems of agricultural financing and determine leasing as an effective tool for agricultural financing. Methodology of this study is based on quantitative analysis among farmers and other borrowers to take out leasing role in financing process and other relevant issues. Empirical data analysis of different countries’ experience and survey among local rural habitants were made to identify the most effective tools of agricultural financing. As a result, lack of effective financial and technological assistance has identified as an obstacle of agricultural development in Armenia. The importance of agriculture in the development of the country's economy was substantiated. The importance of public awareness in increasing the applicability of leasing has been identified. As a scientific novelty, the thesis of leasing as an effective financial tool for agricultural lending has been proven. The specifics of leasing efficiency for agricultural lending are explained. Absence of collateral as the only advantage of leasing was rejected.

Forecasting COVID-19 Confirmed Cases Using Empirical Data Analysis in Korea

From November to December 2020, the third wave of COVID-19 cases in Korea is ongoing. The government increased Seoul’s social distancing to the 2.5 level, and the number of confirmed cases is increasing daily. Due to a shortage of hospital beds, treatment is difficult. Furthermore, gatherings at the end of the year and the beginning of next year are expected to worsen the effects. The purpose of this paper is to emphasize the importance of prediction timing rather than prediction of the number of confirmed cases. Thus, in this study, five groups were set according to minimum, maximum, and high variability. Through empirical data analysis, the groups were subdivided into a total of 19 cases. The cumulative number of COVID-19 confirmed cases is predicted using the auto regressive integrated moving average (ARIMA) model and compared with the actual number of confirmed cases. Through group and case-by-case prediction, forecasts can accurately determine decreasing and increasing trends. To prevent further spread of COVID-19, urgent and strong government restrictions are needed. This study will help the government and the Korea Disease Control and Prevention Agency (KDCA) to respond systematically to a future surge in confirmed cases.

Research on the Impact of Green Credit on Profitability of Commercial Banks in China

This paper reviews the literature on green credit, and has a further understanding of the basic concepts of green credit and profitability of commercial banks, as well as the development status of green credit in China in recent years. Based on empirical data analysis, supplemented by theoretical analysis, this paper makes an empirical analysis of the impact of green credit on the profitability of China’s commercial banks. Finally, according to the results of the empirical analysis, the corresponding opinions and suggestions are given.

Stance Taking in Social Media: the Analysis of the Comments About Us Presidential Candidates on Facebook and Twitter

[full article and abstract in English] The subject of the paper is the analysis of the expression of stance taking in an online environment, mainly in the comments of users of social networks such as Facebook and Twitter about the presidential candidates of the American Presidential Election in 2016. The empirical data analysis was carried out following the ideas of J. W. Du Bois (2007), D. Barton & C. Lee (2013) and R. Englebretson (2007) on stance taking and J. W. Du Bois’ (2007) model of stance triangle, i.e. grouping instances of stance-taking into one of these groups: evaluation, affect or epistemicity, which served as the main framework of this study. The work of linguists D. Barton & C. Lee (2013) on the expression of stance-taking in an online environment were also taken into consideration. Having in mind the fact that stance identification is a challenging task , i.e. it could be implicitly as well as explicitly expressed and that it should be inferred from different modes of its expression and interpreted with reference to many contextual and intertextual factors, in the current analysis the authors focused on interpretation of linguistic as well as other multimodal means of the expression of stance that were used by users of social networks in their writing spaces on the topic of the Presidential Election in the United States in 2016. It should also be mentioned that the analysis presented in this article offers only one of the many possible interpretations of the data. Moreover, the current paper concentrates mainly on the presentation of the empirical data of the expression of affective stance. However, it should be indicated that in some cases stance types overlap, i.e. one instance could be treated as both taking an affective and an evaluative stance, as judgements and evaluation (i.e. evaluative stance) are often based on feelings (i.e. affective stance). The main source of the empirical data were the instances of stance taking taken from comments found on Donald Trump’s and Hillary Clinton’s verified Facebook and Twitter pages during their presidential campaigns in 2016. All in all, 147 examples of posts and comments from the social networks Facebook and Twitter were collected: 72 comments incorporating stance taking on Donald Trump‘s posts, and 75 comments including stance taking on Hillary Clinton‘s posts. The results of the empirical data analysis showed that the affective stance was expressed by linguistic as well as multimodal means.