Search results

The deployment of vaccines is the primary task in curbing the COVID-19 pandemic. The purpose of this paper is to understand the public’s opinions on vaccines and then design effective interventions to promote vaccination coverage.

This paper proposes a research framework based on the spatiotemporal perspective to analyse the public opinion evolution towards COVID-19 vaccine in China. The framework first obtains data through crawler tools. Then, with the help of data mining technologies, such as emotion computing and topic extraction, the evolution characteristics of discussion volume, emotions and topics are explored from spatiotemporal perspectives.

In the temporal perspective, the public emotion declines in the later stage, but overall emotion performance is positive and stabilizing. This decline in emotion is mainly associated with ambiguous information about the COVID-19 vaccine. The research progress of vaccines and the schedule of vaccination have driven the evolution of public discussion topics. In the spatial perspective, the public emotion tends to be positive in 31 regions, whereas local emotion increases and decreases in different stages. The dissemination of distinctive information and the local epidemic prevention and control status may be potential drivers of topic evolution in local regions.

The analysis results of media information can assist decision-makers to accurately grasp the subjective thoughts and emotional expressions of the public in terms of spatiotemporal perspective and provide decision support for macro-control response strategies and risk communication.

Nowadays, the breakout of the COVID-19 pandemic has caused an important change in teaching models. The emotional experience of this change has an important impact on online teaching. This paper aims to explore its time evolution characteristics and provide reference for the development of online teaching in the post epidemic era.

The article firstly crawls the online teaching-related comment text data on Zhihu platform and performs emotional calculation to obtain a one-dimensional time series of daily average emotional values. Then, by using non-linear time-series analysis, this paper reconstructs the daily average emotion value time series in high-dimensional phase space, calculates the maximum Lyapunov exponent and correlation dimension and finally, explores the feature patterns through recurrence plot and recurrence quantification analysis.

It was found that the sequence has typical non-linear chaotic characteristics; its correlation dimension indicates that it contains obvious fractal characteristics; the public emotional evolution shows a cyclical rise and fall. By text mining and temporal evolution analysis, this paper explores the evolution law over chronically of the daily average emotion value time series, provides feasible strategies to improve students' online learning experience and quality and continuously optimizes this new teaching model in the era of pandemic.

Based on social knowledge sharing platform of Q&A, this paper models and analyzes users interaction data under online teaching-related topics. This paper explores the evolution law over a long time period of the daily average emotion value time series using text mining and temporal evolution analysis. It then offers workable solutions to enhance the quality and experience of students' online learning, and it continuously improves this new teaching model in the age of pandemics.

The paper aims to construct a spatiotemporal situational awareness framework to sense the evolutionary situation of public opinion in social media, thus assisting relevant departments in formulating public opinion control measures for specific time and space contexts.

The spatiotemporal situational awareness framework comprises situational element extraction, situational understanding and situational projection. In situational element extraction, the data on the COVID-19 vaccine, including spatiotemporal tags and text contents, is extracted. In situational understanding, the bidirectional encoder representation from transformers – latent dirichlet allocation (BERT-LDA) and bidirectional encoder representation from transformers – bidirectional long short-term memory (BERT-BiLSTM) are used to discover the topics and emotional labels hidden in opinion texts. In situational projection, the situational evolution characteristics and patterns of online public opinion are uncovered from the perspective of time and space through multiple visualisation techniques.

From the temporal perspective, the evolution of online public opinion is closely related to the developmental dynamics of offline events. In comparison, public views and attitudes are more complex and diversified during the outbreak and diffusion periods. From the spatial perspective, the netizens in hotspot areas with higher discussion volume are more rational and prefer to track the whole process of event development, while the ones in coldspot areas with less discussion volume pay more attention to the expression of personal emotions. From the perspective of intertwined spatiotemporal, there are differences in the focus of attention and emotional state of netizens in different regions and time stages, caused by the specific situations they are in.

The situational awareness framework can shed light on the dynamic evolution of online public opinion from a multidimensional perspective, including temporal, spatial and spatiotemporal perspectives. It enables decision-makers to grasp the psychology and behavioural patterns of the public in different regions and time stages and provide targeted public opinion guidance measures and offline event governance strategies.

The outbreak of COVID-19 has become a major public health emergency worldwide. How to effectively guide public opinion and implement precise prevention and control is a hot topic in current research. Mining the spatiotemporal coupling between online public opinion and offline epidemics can provide decision support for the precise management and control of future emergencies.

This study focuses on analyzing the spatiotemporal coupling relationship between public opinion and the epidemic. First, based on Weibo information and confirmed case information, a field framework is constructed using field theory. Second, SnowNLP is used for sentiment mining and LDA is utilized for topic extraction to analyze the topic evolution and the sentiment evolution of public opinion in each coupling stage. Finally, the spatial model is used to explore the coupling relationship between public opinion and the epidemic in space.

The findings show that there is a certain coupling between online public opinion sentiment and offline epidemics, with a significant coupling relationship in the time dimension, while there is no remarkable coupling relationship in space. In addition, the core topics of public concern are different at different coupling stages.

This study deeply explores the spatiotemporal coupling relationship between online public opinion and offline epidemics, adding a new research perspective to related research. The result can help the government and relevant departments understand the dynamic development of epidemic events and achieve precise control while mastering the dynamics of online public opinion.

Internet-famous food (also known as “online celebrity” food) is very popular in the digital age. This study aims to investigate consumer attitudes and understand consumer behavior towards Internet-famous food.

The authors collected 136,835 online comments regarding “Internet-famous food” from Dianping platform between 2016 and 2019 using a web scraper. A sentiment lexicon for Internet-famous food was constructed, and sentiment analysis is further conducted to understand consumer attitudes. Additionally, the authors use topic analysis and time series analysis to study consumer behavior.

Sentiment analysis showed that the number of consumers' comments decreased over time with the attitudes being overall positive, and the Internet-famous food industry has a positive prospect; time series analysis showed that the consumption of Internet-famous food was not affected by the season; topic analysis showed that consumers' comments on Internet-famous food were rich with a large variety, covering food categories, brand, quality, service, environment and price.

To the authors’ knowledge, limited research has focused on public opinions regarding “Internet-famous food”. This is the first study on consumer behavior towards Internet-famous food. This article provides a unique insight into the purchasing behavior and attitude of Chinese Internet-famous food consumers through text mining.

In crises such as the coronavirus disease 2019 (COVID-19) pandemic, governments need to act in time to lead citizens toward rational reactions and disclose information effectively to the public. This paper aims to understand the content published by the government and identify how citizen engagement relates to content type and emotional valence.

The grounded theory approach was adopted and nine types of content posted by the government were observed. The data were obtained from “People's Daily”, an official Sina Weibo account representing the voice of the Chinese government, from January 3 to June 22 in 2020.

The analysis shows that information related to emotional support and social mobilization were the most reposted, while those mentioning immoral and illegal incidents were the most liked and commented. Also, it was found that positive posts tend to attract more likes, yet with fewer reposts than neutral posts.

The authors adopted thematic analysis and focused on the impact of post content and valence on user participation behavior. This study expands the existing literature. The government can improve crises management capability by learning about citizen engagement behaviors on social media.

The debate about the future of the death penalty often focuses on whether its supporters are animated by instrumental or expressive values, and if the latter, what values the penalty does in fact express, where those values originated and how deeply entrenched they are. In this chapter, I argue that a more explicit recognition of the emotional sources of support for and opposition to the death penalty will contribute to the clarity of the debate. The focus on emotional variables reveals that the boundary between instrumental and expressive values is porous; both types of values are informed (or uninformed) by fear, outrage, compassion, selective empathy and other emotional attitudes. More fundamentally, though history, culture and politics are essential aspects of the discussion, the resilience of the death penalty cannot be adequately understood when the affect is stripped from explanations for its support. Ultimately, the death penalty will not die without a societal change of heart.

This research aimed to define the factors that may impact the effectiveness of online architectural education during COVID-19 and to examine the degree of students' and instructors' satisfaction with these factors among Jordanian governmental universities. Further, the research examines the relationships/interrelationships between the degree of their satisfaction with these factors and the students' and instructors' age, gender, education level, home size, and family size.

Different research methods were used to achieve the research purposes, including semi-structured interviews, online questionnaires and reviewing recent literature. This research used descriptive statistics and Pearson product-moment correlation coefficient (Pearson r) to analyze the data.

This research found that online architectural education during COVID-19 was stressful. It was ineffective in teaching practical architectural courses like design and negatively affected the architectural learning process and outcomes. In contrast, the online teaching of architectural theoretical courses was reliable and convenient if the students and instructors had intentional, sufficient technological and appropriate resources. Online architectural education negatively affected the students' design ability and skills, peer review, intended learning outcomes (ILOs) achievements, the quality of feedback, workload, interaction mode, participation, collaboration, productivity and increased cheating on online exams. The home environment was unsuitable for online architectural education. Family interventions, privacy, home size and family size significantly influence online architectural education's effectiveness.

Students' and instructors' satisfaction determines the continuity of using online teaching mode, which depends on information quality, system quality, service quality, perceived ease of use, perceived usefulness, achieved intended learning outcomes, cognitive absorption, skills, motivation, engagement, implementing resources and strategies and positive emotions as hope and enjoyment. Students' and instructors' satisfaction reflects how they view their learning experience, which is crucial in assessing the effectiveness of online education quality that focuses on the context, input, process and product, which is still not clearly understood, particularly for developing countries.

Microblogging is an important channel used to disseminate online public opinion during an emergency. Analyzing the features and evolution mechanism of online public opinion during an emergency plays a significant role in crisis management.

This paper uses the event of Hurricane Irma and combines it with the life cycle of online public opinion evolution to understand the effect of different types of emotional (joy, anger, sadness, fear, disgust) microblogs (tweets) on information dissemination. The research was performed in the context of Hurricane Irma by using tweets associated with that event.

This paper demonstrates that negative emotional information has a greater communication effect, and further, the target audience that receives more exposure to negative emotional microblogs has a stronger tendency to retweet. Meanwhile, emotions expressed in tweets and the life cycle of public opinion evolution exert interactive effects on the retweeting behavior of the target audience.

For future research, a professional dictionary and the context should be taken into consideration to make the modeling in the text more normative and analyzable.

This paper aims to reveal how the emotions of a tweet affect its virality in terms of diffusion volume in the context of an emergency event.

The conclusion made in this paper can shed light on the real-time regulation and public opinion transmission, as well as for efficient intelligence service and emergency management.

In this study, Hurricane Irma is taken as an example to explore the factors influencing the information dissemination during emergencies on the social media environment. The relationship between the sentiment of a tweet and the life cycle of public opinion and its effect on tweet volume were investigated.

Emotions, understood as evolving mental states, are pivotal in shaping individuals“' decision-making, especially in ambiguous information evaluation, probability estimation of events, and causality analysis. Public–private partnership (PPP) projects represent a confluence of “economic–environmental–social” dimensions, wherein stakeholder behavior follows the sequential progression of “cognition–emotion–action.” Consequently, comprehending the effects of emotional shifts on stakeholder's decision-making processes is vital to fostering the sustainability of PPP projects.

The paper utilizes rank-dependent expected utility and evolutionary game theory to systematically examine the influence of emotional factors on stakeholders' behavior and decision-making processes within PPP projects. The paper integrates three emotional state functions—optimism, pessimism and rationality—into the PPP framework, highlighting the intricate interactions among the government, private sector, surrounding public and the media. Furthermore, the paper amalgamates the evolutionary pathways of environmental rights incidents with the media's role. Through equilibrium analysis and numerical simulation, the paper delves into the diverse interplay of emotions across different phases of the environmental rights incident, assessing the impact of these emotions on the evolutionary game's equilibrium results.

Emotions significantly influence the microlevel decisions of PPP stakeholders, adapting continually based on event dynamics and media influences. When the private sector demonstrates optimism and the surrounding public leans toward rationality or pessimism, the likelihood of the private sector engaging in speculative behavior escalates, while the surrounding public refrains from adopting a supervisory strategy. Conversely, when the private sector is pessimistic and the public is optimistic, the system fails to evolve a stable strategy. However, when government regulation intensifies, the private sector opts for a nonspeculative strategy, and the surrounding public adopts a supervisory strategy. Under these conditions, the system attains a relatively optimal state of equilibrium.

The paper develops a game model to examine the evolutionary dynamics between the surrounding public and private sectors concerning environmental rights protection in waste incineration PPP projects. It illuminates the nature of the conflicting interests among project participants, delves into the impact of emotional factors on their decision-making processes and offers crucial perspectives for the governance of such partnerships. Furthermore, this paper provides substantive recommendations for emotional oversight to enhance governance efficacy.