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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.

The development of social media has led to large numbers of internet users now producing massive amounts of user-generated content (UGC). UGC, which shows users’ opinions about events directly, is valuable for monitoring public opinion. Current researches have focused on analysing topic evolutions in UGC. However, few researches pay attention to emotion evolutions of sub-topics about popular events. Important details about users’ opinions might be missed, as users’ emotions are ignored. This paper aims to extract sub-topics about a popular event from UGC and investigate the emotion evolutions of each sub-topic.

This paper first collects UGC about a popular event as experimental data and conducts subjectivity classification on the data to get subjective corpus. Second, the subjective corpus is classified into different emotion categories using supervised emotion classification. Meanwhile, a topic model is used to extract sub-topics about the event from the subjective corpora. Finally, the authors use the results of emotion classification and sub-topic extraction to analyze emotion evolutions over time.

Experimental results show that specific primary emotions exist in each sub-topic and undergo evolutions differently. Moreover, the authors find that performance of emotion classifier is optimal with term frequency and relevance frequency as the feature-weighting method.

To the best of the authors’ knowledge, this is the first research to mine emotion evolutions of sub-topics about an event with UGC. It mines users’ opinions about sub-topics of event, which may offer more details that are useful for analysing users’ emotions in preparation for decision-making.

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.

This paper aims to construct a spatio-temporal emotional framework (STEF) for digital humanities from a quantitative perspective, applying knowledge extraction and mining technology to promote innovation of humanities research paradigm and method.

The proposed STEF uses methods of information extraction, sentiment analysis and geographic information system to achieve knowledge extraction and mining. STEF integrates time, space and emotional elements to visualize the spatial and temporal evolution of emotions, which thus enriches the analytical paradigm in digital humanities.

The case study shows that STEF can effectively extract knowledge from unstructured texts in the field of Chinese Qing Dynasty novels. First, STEF introduces the knowledge extraction tools – MARKUS and DocuSky – to profile character entities and perform plots extraction. Second, STEF extracts the characters' emotional evolutionary trajectory from the temporal and spatial perspective. Finally, the study draws a spatio-temporal emotional path figure of the leading characters and integrates the corresponding plots to analyze the causes of emotion fluctuations.

The STEF is constructed based on the “spatio-temporal narrative theory” and “emotional narrative theory”. It is the first framework to integrate elements of time, space and emotion to analyze the emotional evolution trajectories of characters in novels. The execuability and operability of the framework is also verified with a case novel to suggest a new path for quantitative analysis of other novels.

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.

Purpose – Uses Kenneth Boulding's concept of “serial reciprocity” in conjunction with information about the evolution of emotions and social exchange processes to identify possible mechanisms that can help explain the rise of early Christianity.

Design/methodology/approach – Using the concept of serial reciprocity as a central organizing principle, a theoretical account is developed that integrates ideas from evolutionary sociology, the sociology of emotions, and exchange theory in order to extend Rodney Stark's analysis of social forces responsible for the success of early Christianity as a social movement.

Findings – Patterns of serial reciprocity may develop when evolved emotions such as gratitude, sympathy, and empathy are activated by recipients of altruism who, in turn, become motivated to repay their benefactor by transmitting a benefit to a third-party recipient. Historical evidence reviewed by Stark is consistent with the claim that serial reciprocity may have conferred benefits to victims suffering from plagues that swept the Roman Empire during the early history of Christianity. Similar processes may be operating today in regions of the world in which aid workers provide assistance to victims of natural and man-made disasters.

Originality/value – This analysis demonstrates the value of integrating conventional sociological analysis and evolutionary theory to gain new explanatory insights about social processes such as serial reciprocity that have received relatively little prior attention by sociological researchers.

The purpose of this paper is to evaluate four visualizations that represent affective states of students.

An empirical-experimental study approach was used to assess the usability of affective state visualizations in a learning context. The first study was conducted with students who had knowledge of visualization techniques (n=10). The insights from this pilot study were used to improve the interpretability and ease of use of the visualizations. The second study was conducted with the improved visualizations with students who had no or limited knowledge of visualization techniques (n=105).

The results indicate that usability, measured by perceived usefulness and insight, is overall acceptable. However, the findings also suggest that interpretability of some visualizations, in terms of the capability to support emotional awareness, still needs to be improved. The level of students’ awareness of their emotions during learning activities based on the visualization interpretation varied depending on previous knowledge of information visualization techniques. Awareness was found to be high for the most frequently experienced emotions and activities that were the most frustrating, but lower for more complex insights such as interpreting differences with peers. Furthermore, simpler visualizations resulted in better outcomes than more complex techniques.

Detection of affective states of students and visualizations of these states in computer-based learning environments have been proposed to support student awareness and improve learning. However, the evaluation of visualizations of these affective states with students to support awareness in real life settings is an open issue.

Effects of stockouts on purchase decisions have been examined from a variety of perspectives; little is yet known about how consumers react to stockouts in online shopping contexts. The present study investigates how stockout reactions depend on a consumer's mindset and familiarity with a website and investigates the role of negative affect in determining a consumer's stockout reaction.

Shopping mindsets (deliberative vs. implemental) and website familiarity (high vs. low) were manipulated in an online experiment consisting of a simulated shopping task at an existing website which next was presented as having a stockout. The study observed the participants' switching responses and measured their negative affect.

Findings indicate that when encountering an online stockout, consumers in an implemental mindset are more likely to switch away from the website than those in a deliberative mindset and are more likely to search for additional items at a competing site. Consumers who are more familiar with the website where they encounter the stockout display a higher likelihood of defecting to a competing site; however, when they are in an implemental mindset, their inclination to defect decreases. The study also shows that the strength of negative emotions affects OOS responses in that buyers that experience more negative emotions are more likely to defect from the site.

The study's findings provide suggestions as to how retailers can manage and minimize defection behaviours associated with online stockouts. In designing operational and marketing strategies retailers need to pay close attention to how consumers' individual mindsets may vary by trait or circumstance and how they hence may respond differently to stockouts.

The authors introduce a novel perspective to the literature on stockout induced reactions and contribute by furthering investigation into previously unexplored specific consumer characteristics and intricacies of stockouts that drive particular stockout reactions.

The purpose of this paper is to study dynamic evolution of passenger emotional contagion among different flights emerging in mass flight delays, so as to quantitatively analyze emotional variation tendencies and influences of concerned factors and intervention measures.

An intervening variable of group emotion was introduced into emotional contagion model to simulate passenger emotional evolution among multi-flight groups. Besides, personalities, characters and social relationships were considered to represent individual differences in emotional changes. Based on personal contact relationships, emotional contagion model was proposed to evaluate cross-emotion transition processes among different groups under scenarios of information shortage. Eventually, evolutionary processes of passenger emotions were fused in an agent-based simulation based on social force correction model.

Simulation experiment results revealed that passenger emotions suffer from combined impacts of individual emotional changes and emotional interactions among adjacent flights through a comparison with actual survey. Besides, emotional interactions accelerate processes of emotion transitions, and have significant impacts on adjacent flights when different measures are taken. Moreover, taking intervention measures simultaneously seems more effective than implementing intervention successively.

The proposed method makes up for deficiency of ignoring effects of emotional interactions among adjacent flights. It contributes to providing control methods and strategies for relevant departments and improving the efficiency and ability of handling passenger collective events in mass flight delays.

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.