scholarly journals China Public Psychology Analysis About COVID-19 Under Considering Sina Weibo Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Wei Pan ◽  
Ren-jie Wang ◽  
Wan-qiang Dai ◽  
Ge Huang ◽  
Cheng Hu ◽  
...  
Keyword(s):  
Large Scale ◽  
Positive Emotions ◽  
Negative Emotions ◽  
Classification Model ◽  
Test Results ◽  
Data Set ◽  
Decay Period ◽  
Auc Value ◽  
The Government ◽  
And Control

COVID-19 not only poses a huge threat to public health, but also affects people’s mental health. Take scientific and effective psychological crisis intervention to prevent large-scale negative emotional contagion is an important task for epidemic prevention and control. This paper established a sentiment classification model to make sentiment annotation (positive and negative) about the 105,536 epidemic comments in 86 days on the official Weibo of People’s Daily, the test results showed that the accuracy of the model reached 88%, and the AUC value was greater than 0.9. Based on the marked data set, we explored the potential law between the changes in Internet public opinion and epidemic situation in China. First of all, we found that most of the Weibo users showed positive emotions, and the negative emotions were mainly caused by the fear and concern about the epidemic itself and the doubts about the work of the government. Secondly, there is a strong correlation between the changes of epidemic situation and people’s emotion. Also, we divided the epidemic into three period. The proportion of people’s negative emotions showed a similar trend with the number of newly confirmed cases in the growth and decay period, and the extinction period. In addition, we also found that women have more positive emotional performance than men, and the high-impact groups is also more positive than the low-impact groups. We hope that these conclusions can help China and other countries experiencing severe epidemics to guide publics respond.

scholarly journals Bulan Ramadan dan Kebahagiaan Seorang Muslim

2019 ◽  
Vol 2 (2) ◽  
pp. 51-62
Author(s):  
Royanulloh Royanulloh ◽  
Komari Komari
Keyword(s):  
Quantitative Research ◽  
Positive Emotions ◽  
Negative Emotions ◽  
Test Results ◽  
Correlation Test ◽  
Positive Correlation

The Islamic and the Nusantara tradition shows that Ramadan is always welcomed with joy. Therefore, Ramadan is related to happiness. This quantitative research analyzes how changes in happiness occur as the coming of Ramadan. The respondents are 117 muslims adult who have received pesantren education. The results showed that significant differences in positive emotions between weeks 3, 2, and 1 before the coming of Ramadan. Meanwhile, negative emotions did not show a significant decrease. Then, the correlation test results show there is a positive correlation between the arrival of Ramadan with positive emotions. Meanwhile, the correlation test was negative with negative emotions. This research proves the coming of the month of Ramadan associated with increasing happiness of a muslim. 

Who is Setting Emotions? A Comparative Study on Emotion Diffusion in Social Media among Public, Government and Media during the COVID-19 Pandemic in China (Preprint)

2021 ◽  
Author(s):  
Shuhuan Zhou ◽  
Yi Wang
Keyword(s):  
Social Media ◽  
Information Exchange ◽  
Positive Emotions ◽  
Negative Emotions ◽  
Chinese Government ◽  
The Public ◽  
The Media ◽  
The Government ◽  
The Impact ◽  
Time Point

BACKGROUND During the COVID-19 outbreak, social media served as the main platform for information exchange, through which the Chinese government, media and public would spread information. At the same time, a variety of emotions interweave, and the public emotions would also be affected by the government and media. OBJECTIVE This study aims to investigate the types, trends and relationships of emotional diffusion in Chinese social media among the public, the government and the media under the pandemic of COVID-19 (December 30,2019, to July 1,2020) . METHODS In this paper, Python 3.7.0 and its data crawling framework Scrapy 1.5.1 are used to write a web crawler program to search for super topics related to COVID-19 on Sina Weibo platform of different keywords . Then, we used emotional lexicon to analyze the types and trends of the public, government and media emotions on social media. Finally cross-lagged regression was applied to build the relationships of different subjects’ emotions. RESULTS The highlights of our study are threefold: (1) The public, the government and the media mainly diffuse positive emotions during the COVID-19 pandemic in China; (2) Emotional diffusion shows a certain change over time, and negative emotions are obvious in the initial phase of the pandemic, with the development of the pandemic, positive emotions surpass negative emotions and remain stable. (3)The impact among the three main emotions with the period as the time point is weak, while the impact of emotion with the day as the time point is relatively obvious. The emotions of the public and the government impact each other, and the media emotions can guide the public emotions. CONCLUSIONS This is the first study of comparing pubic, government and media emotions on the social media during COVID-19 pandemic in China. The pubic, the government and the media mainly diffuse positive emotions during the pandemic. And the government and the media have better effect on short-term emotional guidance. Therefore, when the pandemic suddenly occurs, the government and the media should intervene in time to solve problems and conflicts and diffuse positive and neutral emotions. In this regard, the government and the media can play important roles through social media in the major outbreaks. At the theoretical level, this paper takes China's epidemic environment and social media as the background to provide one of the explanatory perspectives for the spread of emotions on social media. At the some time, because of this special background, it can provide comparison and reference for the research on internet emotions in other countries.

scholarly journals Chinese Public's Attention to the COVID-19 Epidemic on Social Media: Observational Descriptive Study

10.2196/18825 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e18825 ◽  
Author(s):  
Yuxin Zhao ◽  
Sixiang Cheng ◽  
Xiaoyan Yu ◽  
Huilan Xu
Keyword(s):  
Public Health ◽  
Social Media ◽  
Positive Emotions ◽  
Public Concern ◽  
Public Attention ◽  
Mining System ◽  
The Public ◽  
Source Of Infection ◽  
The Government ◽  
And Control

Background Since the coronavirus disease (COVID-19) epidemic in China in December 2019, information and discussions about COVID-19 have spread rapidly on the internet and have quickly become the focus of worldwide attention, especially on social media. Objective This study aims to investigate and analyze the public’s attention to events related to COVID-19 in China at the beginning of the COVID-19 epidemic (December 31, 2019, to February 20, 2020) through the Sina Microblog hot search list. Methods We collected topics related to the COVID-19 epidemic on the Sina Microblog hot search list from December 31, 2019, to February 20, 2020, and described the trend of public attention on COVID-19 epidemic-related topics. ROST Content Mining System version 6.0 was used to analyze the collected text for word segmentation, word frequency, and sentiment analysis. We further described the hot topic keywords and sentiment trends of public attention. We used VOSviewer to implement a visual cluster analysis of hot keywords and build a social network of public opinion content. Results The study has four main findings. First, we analyzed the changing trend of the public’s attention to the COVID-19 epidemic, which can be divided into three stages. Second, the hot topic keywords of public attention at each stage were slightly different. Third, the emotional tendency of the public toward the COVID-19 epidemic-related hot topics changed from negative to neutral, with negative emotions weakening and positive emotions increasing as a whole. Fourth, we divided the COVID-19 topics with the most public concern into five categories: the situation of the new cases of COVID-19 and its impact, frontline reporting of the epidemic and the measures of prevention and control, expert interpretation and discussion on the source of infection, medical services on the frontline of the epidemic, and focus on the worldwide epidemic and the search for suspected cases. Conclusions Our study found that social media (eg, Sina Microblog) can be used to measure public attention toward public health emergencies. During the epidemic of the novel coronavirus, a large amount of information about the COVID-19 epidemic was disseminated on Sina Microblog and received widespread public attention. We have learned about the hotspots of public concern regarding the COVID-19 epidemic. These findings can help the government and health departments better communicate with the public on health and translate public health needs into practice to create targeted measures to prevent and control the spread of COVID-19.

International Land Deals and Agricultural Investment in Ethiopia

2015 ◽  
pp. 167-193
Author(s):  
Kassa T. Alemu
Keyword(s):  
Large Scale ◽  
Sustainable Use ◽  
Control Mechanisms ◽  
Monitoring And Control ◽  
Agricultural Investment ◽  
Secondary Sources ◽  
The Government ◽  
Large Scale Land ◽  
And Control ◽  
Agricultural Investments

This chapter investigates land deals processes and the effects on livelihoods in Gambella and Benishangul-Gumuz. It applies quantitative and qualitative data from primary and secondary sources. It describes the land deals, actors involved, and the effect of the deal on villagers' land rights, food security, job creation, technology transfer, and sustainable use of natural resources. The study concludes that the government effort towards large-scale land deals and agricultural investment is promising. However, there is a gap regarding making the deals a win-win situation for stakeholders. Therefore, it is recommended that the governance of land deals need to be improved, and the capacity of the three actors—the government, investors, and local communities—need to be developed to play their respective roles in the deals. It is also recommended that effective monitoring and control mechanisms related to large-scale agricultural investments should be put in place and properly implemented.

scholarly journals To Procrastinate or Not to Procrastinate: A Retrospective Study of the Optimal Timing of Containing the Global Spread of the COVID-19 Pandemic

2021 ◽  
Vol 9 ◽  
Author(s):  
Jun Li ◽  
Lingjian Ye ◽  
Yimin Zhou ◽  
Joy Y. Zhang ◽  
Zhuo Chen
Keyword(s):  
Large Scale ◽  
Early Stage ◽  
Control Policy ◽  
Optimal Timing ◽  
Policy Response ◽  
Global Public Health ◽  
Positive Effects ◽  
Global Pandemic ◽  
The Government ◽  
And Control

As global public health is under threat by the 2019-nCoV and a potential new wave of large-scale epidemic outbreak and spread is looming, an imminent question to ask is what the optimal strategy of epidemic prevention and control (P&C) measures would be, especially in terms of the timing of enforcing aggressive policy response so as to maximize health efficacy and to contain pandemic spread. Based on the current global pandemic statistic data, here we developed a logistic probability function configured SEIR model to analyse the COVID-19 outbreak and estimate its transmission pattern under different “anticipate- or delay-to-activate” policy response scenarios in containing the pandemic. We found that the potential positive effects of stringent pandemic P&C measures would be almost canceled out in case of significantly delayed action, whereas a partially procrastinatory wait-and-see control policy may still be able to contribute to containing the degree of epidemic spread although its effectiveness may be significantly compromised compared to a scenario of early intervention coupled with stringent P&C measures. A laissez-faire policy adopted by the government and health authority to tackling the uncertainly of COVID19-type pandemic development during the early stage of the outbreak turns out to be a high risk strategy from optimal control perspective, as significant damages would be produced as a consequence.

scholarly journals Chinese Public Attention to COVID-19 Epidemic: Based on Social Media

2020 ◽  
Author(s):  
Yuxin Zhao ◽  
Huilan Xu
Keyword(s):  
Public Health ◽  
Social Media ◽  
Positive Emotions ◽  
Control Measures ◽  
Public Concern ◽  
Public Attention ◽  
Mining System ◽  
The Public ◽  
The Government ◽  
And Control

AbstractBackgroundSince the new coronavirus epidemic in China in December 2019, information and discussions about COVID-19 have spread rapidly on the Internet and have quickly become the focus of worldwide attention, especially on social media.ObjectiveThis study aims to investigate and analyze the public’s attention to COVID-19-related events in China at the beginning of the COVID-19 epidemic in China (December 31, 2019, to February 20, 2020) through the Sina Microblog hot search list.MethodsWe collected topics related to the COVID-19 epidemic on the Sina Microblog hot search list from December 31, 2019, to February 20, 2020 and described the trend of public attention on COVID-19 epidemic-related topics. ROST CM6.0 (ROST Content Mining System Version 6.0) was used to analyze the collected text for word segmentation, word frequency, and sentiment analysis. We further described the hot topic keywords and sentiment trends of public attention. We used VOSviewer to implement a visual cluster analysis of hot keywords and build a social network of public opinion content.ResultsThe study has four main findings. First, we analyzed the changing trend of the public’s attention to the COVID-19 epidemic, which can be divided into three stages. Second, the hot topic keywords of public attention at each stage are slightly different. In addition, the emotional tendency of the public toward the COVID-19 epidemic-related hot topics has changed from negative to neutral, with negative emotions weakening and positive emotions increasing as a whole. Finally, we divided the COVID-19 topics with the most public concern into five categories: new COVID-19 epidemics and their impact; (2) frontline reporting of the epidemic and prevention and control measures; (3) expert interpretation and discussion on the source of infection; (4) medical services on the frontline of the epidemic; and (5) focus on the global epidemic and the search for suspected cases.ConclusionsThis is the first study of public attention on the COVID-19 epidemic using a Chinese social media platform (i.e., Sina Microblog). Our study found that social media (e.g., Sina Microblog) can be used to measure public attention to public health emergencies. During the epidemic of the novel coronavirus, a large amount of information about the COVID-19 epidemic was disseminated on Sina Microblog and received widespread public attention. We have learned about the hotspots of public concern regarding the COVID-19 epidemic. These findings can help the government and health departments better communicate with the public on health and translate public health needs into practice to create targeted measures to prevent and control the spread of COVID-19.

scholarly journals Analysis of the spatial effects of inter-provincial air pollution in China

2020 ◽  
Vol 194 ◽  
pp. 04013
Author(s):  
Zhang Yanyan
Keyword(s):  
Air Pollution ◽  
Spatial Correlation ◽  
Prevention And Control ◽  
Large Scale ◽  
Climatic Conditions ◽  
Spatial Effects ◽  
Haze Pollution ◽  
Regional Problem ◽  
The Government ◽  
And Control

Air pollution is a hot environmental issue that people have continued to pay attention to in recent years, and the government has spared no effort to strengthen its prevention. Haze is the result of the interaction between specific climatic conditions and human activities. Economic development and population agglomeration are important causes of smog on a large scale. Based on China’s provincial haze pollution PM2.5 data from 2003 to 2017, Moran’s I index and LISA scatter plot were used to analyze the spatial correlation of haze pollution. The results show that there is a positive spatial correlation between China’s provincial haze pollution. Therefore, the problem of air pollution is a regional problem, and it is necessary for each region to strengthen regional joint prevention and control according to location conditions, natural conditions and economic conditions, and contribute to the prevention and control of smog.

scholarly journals Predictive Analysis of Real-Time Strategy using Face book’s Prophet Model on Covid-19 Dataset of India

2021 ◽  
pp. 305-312
Author(s):  
Pankaj Kumar ◽  
Renuka Sharma ◽  
S. K. Singh
Keyword(s):  
Large Scale ◽  
Time Series Data ◽  
Predictive Analytics ◽  
Series Data ◽  
The Novel ◽  
Healthcare Facilities ◽  
Global Epidemic ◽  
The Government ◽  
Novel Coronavirus ◽  
And Control

The global epidemic of the novel coronavirus (COVID-19) called SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) has infected millions and killed millions. The prevalence of the virus is of paramount importance in identifying future infections and preparing healthcare facilities to avoid death. Accurately predicting the spread of COVID-19 is a challenging analytical and practical task for the research community. We can learn to use predictive analytics to predict the positive outcomes of these risks. These predictive analytics can look at the risks of past successes and failures. In this paper, the Facebook prophet model discusses the number of large-scale cases and deaths in India based on daily time-series data from 30 January 2020 to 30 April 2021, for forecasting and visualization. The covid-19 pandemic could end prematurely if social distancing and safety measures are required to stabilize and control is required to achieve treatment in India. This paper suggests that the Prophet Model is more effective in predicting COVID-19 cases. The forecast results will help the government plan strategies to prevent the spread of the coronavirus.

scholarly journals Variability in sentence comprehension in aphasia in German

2020 ◽  
Author(s):  
Dorothea Pregla ◽  
Paula Lisson ◽  
Shravan Vasishth ◽  
Frank Burchert ◽  
Nicole Stadie
Keyword(s):  
Sentence Processing ◽  
Large Scale ◽  
Sentence Comprehension ◽  
Control Group ◽  
Data Set ◽  
Control Structures ◽  
Individual Level ◽  
Competing Models ◽  
Test Retest Reliability ◽  
And Control

An important property of aphasia is the variability in the performance between and within individual patients. However, there have been only a few systematic large-scale studies in a range of syntactic constructions and tasks that make it possible to investigate variability and to evaluate the quantitative predictions of competing models of sentence comprehension in aphasia (Lissón et al., under review). This is the first comprehensive investigation of variability in sentence comprehension in German, testing 18 individuals with aphasia and a control group and involving (a) several construction (canonical / non-canonical declarative sentences, subject / object relative clauses, subject / object control structures, near / distant antecedents of pronouns), (b) three tasks (object manipulation, sentence-picture matching with / without self-paced listening), and (c) two test phases (to investigate test-retest reliability). This data-set provides a detailed investigation of individual-level variation in individuals with aphasia and control participants along several dimensions of sentence processing difficulty.

scholarly journals A Strategic Analytics Using Convolutional Neural Networks for Weed Identification in Sugar Beet Fields

2021 ◽  
Vol 13 (1) ◽  
pp. 49-57
Author(s):  
Brahim Jabir ◽  
Noureddine Falih ◽  
Asmaa Sarih ◽  
Adil Tannouche
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Sugar Beet ◽  
Convolutional Neural Networks ◽  
Precision Agriculture ◽  
Large Scale ◽  
Aerial Images ◽  
Data Set ◽  
High Level ◽  
And Control

Researchers in precision agriculture regularly use deep learning that will help growers and farmers control and monitor crops during the growing season; these tools help to extract meaningful information from large-scale aerial images received from the field using several techniques in order to create a strategic analytics for making a decision. The information result of the operation could be exploited for many reasons, such as sub-plot specific weed control. Our focus in this paper is on weed identification and control in sugar beet fields, particularly the creation and optimization of a Convolutional Neural Networks model and train it according to our data set to predict and identify the most popular weed strains in the region of Beni Mellal, Morocco. All that could help select herbicides that work on the identified weeds, we explore the way of transfer learning approach to design the networks, and the famous library Tensorflow for deep learning models, and Keras which is a high-level API built on Tensorflow.

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