Public Emotions Research Articles

Exploring public attention about green consumption on Sina Weibo: Using text mining and deep learning

Achieving the goal of carbon neutrality and carbon peak as scheduled puts forward new demands for the green transition of low-carbon lifestyle in Chinese society. In-depth practice of green consumption (GC) behavior can effectively promote the supply-side and consumption-side emission reduction work, but the phenomenon of “high awareness, low practice” is widespread in GC. The causes of consumers' low practice of GC need to be analyzed from the perspective of time and space from the actual media data. Furthermore, this process assists policymakers and stakeholders to understand the general attitude of the public towards GC, clarifying the propagation path of public emotions and the source of negative emotions. Based on the data from Sina Weibo, this paper applied text mining, a hybrid model of convolutional neural network and long and short-term memory neural network to analyze the public's attention, sentiment tendency and hot topics on GC. The results show that the vast majority of the Chinese public has a positive attitude toward GC; women and economically developed regions are more concerned about GC; the drivers of positive public sentiment toward GC include environmental awareness education, air pollution prevention and control, and online shopping; high green product prices, excessive time costs, chaotic sharing economy and one-size-fits-all solutions lead to negative public sentiment toward GC. By providing public sentiment analysis of GC, this research would assist decision-makers to understand the dissemination mechanism of public will in social media and clarify targeted solutions, which is of great significance for policy formulation and improvement.

Microstate Detection in Naturalistic Electroencephalography Data: A Systematic Comparison of Topographical Clustering Strategies on an Emotional Database.

Electroencephalography (EEG) microstate analysis is a powerful tool to study the spatial and temporal dynamics of human brain activity, through analyzing the quasi-stable states in EEG signals. However, current studies mainly focus on rest-state EEG recordings, microstate analysis for the recording of EEG signals during naturalistic tasks is limited. It remains an open question whether current topographical clustering strategies for rest-state microstate analysis could be directly applied to task-state EEG data under the natural and dynamic conditions and whether stable and reliable results could still be achieved. It is necessary to answer the question and explore whether the topographical clustering strategies would affect the performance of microstate detection in task-state EEG microstate analysis. If it exists differences in microstate detection performance when different topographical clustering strategies are adopted, then we want to know how the alternations of the topographical clustering strategies are associated with the naturalistic task. To answer these questions, we work on a public emotion database using naturalistic and dynamic music videos as the stimulation to evaluate the effects of different topographical clustering strategies for task-state EEG microstate analysis. The performance results are systematically examined and compared in terms of microstate quality, task efficacy, and computational efficiency, and the impact of topographical clustering strategies on microstate analysis for naturalistic task data is discussed. The results reveal that a single-trial-based bottom-up topographical clustering strategy (bottom-up) achieves comparable results with the task-driven-based top-down topographical clustering (top-down). It suggests that, when task information is unknown, the single-trial-based topographical clustering could be a good choice for microstate analysis and neural activity study on naturalistic EEG data.

Public Emotional Diffusion over COVID-19 Related Tweets Posted by Major Public Health Agencies in the United States

Abstract Since the end of 2019, the COVID-19 outbreak worldwide has not only presented challenges for government agencies in addressing public health emergency, but also tested their capacity in dealing with public opinion on social media and responding to social emergencies. To understand the impact of COVID-19 related tweets posted by the major public health agencies in the United States on public emotion, this paper studied public emotional diffusion in the tweets network, including its process and characteristics, by taking Twitter users of four official public health systems in the United States as an example. We extracted the interactions between tweets in the COVID-19-TweetIds data set and drew the tweets diffusion network. We proposed a method to measure the characteristics of the emotional diffusion network, with which we analyzed the changes of the public emotional intensity and the proportion of emotional polarity, investigated the emotional influence of key nodes and users, and the emotional diffusion of tweets at different tweeting time, tweet topics and the tweet posting agencies. The results show that the emotional polarity of tweets has changed from negative to positive with the improvement of pandemic management measures. The public's emotional polarity on pandemic related topics tends to be negative, and the emotional intensity of management measures such as pandemic medical services turn from positive to negative to the greatest extent, while the emotional intensity of pandemic related knowledge changes the most. The tweets posted by the Centers for Disease Control and Prevention and the Food and Drug Administration of the United States have a broad impact on public emotions, and the emotional spread of tweets' polarity eventually forms a very close proportion of opposite emotions.

Comprehensive Evolution Analysis of Public Perceptions Related to Pediatric Care: A Sina Weibo Case Study (2013–2020)

The frequent occurrence of doctor-patient disputes in pediatrics departments in China has led to more doctor-patient online public discussions. The effects of comprehensive evolution have become the focus of public opinion governance. This paper obtains social media data regarding pediatric care from Sina Weibo, and analyzes changes in emotional tendencies and the evolutionary characteristics of public emotional events. Simulation experiments are conducted by constructing a comprehensive evolution model of public emotion to reveal the derivation rules of the information community. The results of the study show that: (1) the Chinese public’s attention in the field of pediatrics in recent years is focused on children medicine; (2) emotional tendencies show a small fluctuation, and the positive emotions are mainly stable; (3) when the public opinion evolves, the evolution effect of the community has a great effect on the topic escalating and social emotions strengthening; (4) other relevant events could promote the exacerbation of current events, making the expansion of the topic larger and longer. Research on pediatric doctor-patient online public emotional events is beneficial to the public’s key needs for pediatric medical care, and provides effective information for stakeholders to effectively monitor and guide public emotional events.

Research on the Emotional Evolution Mechanism of Network Public Opinion Based on an Information Ecosystem

As an important carrier of public emotion expression, public opinion spreads on a large scale with the continuous upgrading of social networks, and effectively controlling the spreading process of public opinion is an essential topic of contemporary social research. In view of the competition between positive and negative information in the process of public opinion dissemination, this paper introduces the theory of emotional infection and proposes a network public opinion communication model based on emotional contagion, considering the reinforcement effect of different individual mentalities and the influence of government intervention. Based on the data from the COVID‐19 epidemic situation, MATLAB simulation technology is used to verify the validity of the model, and the effect of strengthening the validity and government intervention on public opinion control is discussed. According to the experiment, three conclusions have been come up with. First, a positive reinforcement effect can enhance the ignorant participants’ ability to maintain the same emotion as the infected information. When positive information repeatedly stimulates the ignorant, it will positively strengthen the ability of people with a positive mentality to maintain positive emotions, which is significantly beneficial to public opinion control. Its essence is to increase the effect of positive information’s belief factor on the dynamic infection rate. When negative information repeatedly stimulates an ignorant person, it will positively strengthen the ability of the person with a negative attitude to maintain negative emotion, which is not conducive to public opinion control. Second, a negative reinforcement effect will strengthen the ignorant ability to change the same emotion as the infected information. When negative information repeatedly stimulates the ignorant, the negative reinforcement effect will strengthen the positive people’s ability to change negative information into positive emotion, which is significantly beneficial to public opinion control. Its essence is to increase the effect of suspicion factor on the dynamic immunization rate. It will strengthen the positive mentality and negative mentality into the path of immunization, which is beneficial to epidemic control. When positive information repeatedly stimulates the ignorant, it will negatively strengthen the ability of the negative mentality to change the positive information into negative emotions, which is not beneficial to the control of public opinion. It will be harmful to strengthen the path of positive and negative mentality into immunization, which is beneficial to epidemic control. Third, the earlier the government intervenes in public opinion, the better it will be. The essence of intervention is to decrease the dynamic incitement rate.

Predicting the popularity of tweets by analyzing public opinion and emotions in different stages of Covid-19 pandemic

In this study, public opinion and emotions regarding different stages of the Covid-19 pandemic from the outbreak of the disease to the distribution of vaccines were analyzed to predict the popularity of tweets. More than 1.25 million English tweets were collected, posted from January 20, 2020, to May 29, 2021. Five sets of content features, including topic analysis, topics plus TF-IDF vectorizer, bag of words (BOW) by TF-IDF vectorizer, document embedding, and document embedding plus TF-IDF vectorizer, were extracted and applied to supervised machine learning algorithms to generate a predictive model for the retweetability of posted tweets. The analysis showed that tweets with higher emotional intensity are more popular than tweets containing information on Covid-19 pandemic. This study can help to detect the public emotions during the pandemic and after vaccination and predict the retweetability of posted tweets in different stages of Covid-19 pandemic.

Spread of Misinformation in Social Networks: Analysis Based on Weibo Tweets

Social networks are filled with a large amount of misinformation, which often misleads the public to make wrong decisions, stimulates negative public emotions, and poses serious threats to public safety and social order. The spread of misinformation in social networks has also become a widespread concern among scholars. In the study, we took the misinformation spread on social media as the research object and compared it with true information to better understand the characteristics of the spread of misinformation in social networks. This study adopts a deep learning method to perform content analysis and emotion analysis on misinformation dataset and true information dataset and adopts an analytic network process to analyze the differences between misinformation and true information in terms of network diffusion characteristics. The research findings reveal that the spread of misinformation on social media is influenced by content features and different emotions and consequently produces different changes. The related research findings enrich the existing research and make a certain contribution to the governance of misinformation and the maintenance of network order.

From red spirit to underperforming pyramids and coercive institutions: Michael Polanyi against economic planning

ABSTRACT This paper examines the evolution of Michael Polanyi’s critique of economic planning. It portrays how the focal point of his critique shifted from addressing the ‘spirit,’ ‘social consciousness,’ and ‘public emotion’ of the people supporting planned economies to addressing the administrative ‘unmanageability’ and the logical impossibility of economic planning. Polanyi developed thought experiments of imaginary economies, contrasted the ‘pyramid of authority’ with the polygons of liberty, and explained organic (spontaneous order) and inorganic (corporate order) ways of adjusting economic relations. He attempted to relax the Leviathan of Soviet economics, and drew the conclusion that mathematics is not sufficient in itself to properly address the economy. Eventually, Polanyi developed an institutionalist approach in order to be able to address both the variability of market economies and the failures of socialist ‘super-planners’ who claimed to eliminate the drift of individual economic adjustments.

Data Augmentation for EEG-Based Emotion Recognition Using Generative Adversarial Networks.

One of the greatest limitations in the field of EEG-based emotion recognition is the lack of training samples, which makes it difficult to establish effective models for emotion recognition. Inspired by the excellent achievements of generative models in image processing, we propose a data augmentation model named VAE-D2GAN for EEG-based emotion recognition using a generative adversarial network. EEG features representing different emotions are extracted as topological maps of differential entropy (DE) under five classical frequency bands. The proposed model is designed to learn the distributions of these features for real EEG signals and generate artificial samples for training. The variational auto-encoder (VAE) architecture can learn the spatial distribution of the actual data through a latent vector, and is introduced into the dual discriminator GAN to improve the diversity of the generated artificial samples. To evaluate the performance of this model, we conduct a systematic test on two public emotion EEG datasets, the SEED and the SEED-IV. The obtained recognition accuracy of the method using data augmentation shows as 92.5 and 82.3%, respectively, on the SEED and SEED-IV datasets, which is 1.5 and 3.5% higher than that of methods without using data augmentation. The experimental results show that the artificial samples generated by our model can effectively enhance the performance of the EEG-based emotion recognition.

A multi-view network for real-time emotion recognition in conversations

Real-time emotion recognition in conversations (RTERC), the task of using the historical context to identify the emotion of a query utterance in a conversation, is important for opinion mining and building empathetic machines. Existing works mainly focus on obtaining each utterance representation separately and then utilizing utterance-level features to model the emotion representation of the query. These approaches treat each utterance as a unit and capture the utterance-level dependencies in the context, but ignore the word-level dependencies among different utterances. In this paper, we propose a multi-view network (MVN) to explore the emotion representation of a query from two different views, i.e., word- and utterance-level views. For the word-level view, MVN takes the context and query as word sequences and then models the word-level dependencies among utterances. For the utterance-level view, MVN extracts each utterance representation separately and then models the utterance-level dependencies in the context. Experimental results on two public emotion conversation datasets show that the proposed model outperforms the state-of-the-art baselines.

Urban collage: how can affective dynamics shape waterfront landscapes?

ABSTRACT This article unravels the factors of affective dynamics that shape and sustain waterfront regeneration while critically examining the essence and influence of megaprojects. It examines why and how urban atmospheres can be provoked and manipulated to dominate the rationale, political orientations, intrinsic motivations, and power struggles behind waterfront landscapes. It provides explicit insight into how waterfront regeneration operates affectively and atmospherically through the convergence of emotion-focused planning, value-creation approaches, and affective adaptability to transform the physical and political landscape. Focusing on the ‘Asia New Bay Area’ (ANBA) project in Kaohsiung, this article explains why and how megaprojects can be accepted and supported by local people, despite a lack of professional evaluation, public participation, and revenue. The key claim of this article is that addressing the city’s affective dynamics and atmospheric resonances is critical for understanding how megaprojects can be employed to direct public emotions and concerns.

The “War on Terror” and Public Diplomacy during the Cold War: Israeli–Turkish Relations and the 1980 Military Coup

What influence does the digital diplomacy of the post 9/11 world have on our understanding of counter-terrorism (CT) diplomacy during the Cold War? This article explores this question and the intersection between intelligence, counterterrorism diplomacy and the digital transformation long overlooked by scholars of Israeli–Turkish relations, Cold War history and terrorism studies. Diplomacy in crisis situations usually operates in an uncertain reality triggered by conflict. Turkey’s domestic crises, specifically its energy crisis between 1978 and 1980, served to shift the country’s foreign policy toward the more anti-Israeli stance of the Arab nations and their demands that Turkey boycott Israel in return for supplying Turkey’s energy needs. This came in the context of a regional wave of contentious politics in the Middle East after the 1979 Iranian revolution, when Israel had just lost three decades of massive investment in Iran under the Shah. I argue that knowing the Turkish military junta’s primary goals centered on the fight against the far left and right political violence at home and on Armenian terror attacks against Turkish diplomats abroad, Israeli diplomats employed a very selective CT policy focusing on the cooperation between Armenian Secret Army for the Liberation of Armenia and the Palestine Liberation Organization. Public diplomacy was key to conveying this message, and attempts to plant information about the Armenian-Palestinian cooperation in Turkish news outlets and national television were made. Israel’s goal was to influence Turkey’s public emotions and convey to Turkey’s military junta that Jerusalem was a true ally, working to confront the mutual regional threat of Armenian-Palestinian terrorists arising from Lebanese training camps. This case study highlights the paradox that Ministries of Foreign Affairs and diplomats enjoyed an unparalleled monopoly of power during the Cold War over the use of means of public diplomacy, while at the time having very limited capacity, and a frequent need for third party mediators to engage with foreign public audiences. It is thus unlikely that the conveyed messages would have been as visible and disseminated as easily as they could be in the post 9/11 via means of digital diplomacy.

Analysis of social media data for public emotion on the Wuhan lockdown event during the COVID-19 pandemic

BackgroundWith outbreaks of COVID-19 around the world, lockdown restrictions are routinely imposed to limit the spread of the virus. During periods of lockdown, social media has become the main channel for citizens to exchange information with others. Public emotions are being generated and shared rapidly online with citizens using internet platforms to reduce anxiety and stress, and stay connected while isolated. ObjectivesThis study aims to explore the regularity of emotional evolution by examining public emotions expressed in online discussions about the Wuhan lockdown event in January 2020. MethodsData related to the Wuhan lockdown was collected from Sina Weibo by web crawler. In this study, the Ortony, Clore, and Collins (OCC) model, Word2Vec, and Bi-directional Long Short-Term Memory model were employed to determine emotional types, train vectorization of words, and identify each text emotion for the training set. Latent Dirichlet Allocation models were also employed to mine the various topic categories, while topic emotional evolution was visualized. ResultsSeven types of emotions and four phases were categorized to describe emotional evolution on the Wuhan lockdown event. The study found that negative emotions such as blame and fear dominated in the early days, and public attitudes towards the lockdown gradually alleviated and reached a balance as the situation improved. Emotional expression about Wuhan lockdown event were significantly related to users’ gender, location, and whether or not their account was verified. There were statistically significant correlations between different emotions within the subtle emotional categories. In addition, the evolution of emotions presented a different path due to different topics. ConclusionsMultiple emotional categories were determined in our study, providing a detailed and explainable emotion analysis to explored emotional appeal of citizen. The public emotions were gradually easing related to the Wuhan lockdown event, there yet exists regional discrimination and post-traumatic stress disorder in this process, which would lead us to pay continuous attention to citizens lives and psychological status post-pandemic. In addition, this study provided an appropriate method and reference case for the government's public opinion control and emotional appeasement.

Dynamic impact of negative public sentiment on agricultural product prices during COVID-19

The COVID-19 pandemic has had a significantly negative impact on public sentiment, which has resulted in panic and some irrational buying behavior, which in turn has had a complex impact on agricultural product prices. This study quantified online negative sentiment using micro-blog text mining and a time-varying parameter vector autoregressive model (TVP-VAR) to empirically analyze the dynamic impact of negative public emotions on agricultural product prices during the COVID-19 pandemic in China. It was found that the online negative sentiment impacted agricultural products prices during COVID-19 and had significant time-varying, lag, and life cycle characteristics, with the responses being most significant in the spread and recession periods. Differences were found in the price responses for different agricultural products and in different risk areas. The online negative sentiment was found to have the greatest impact on vegetable prices, with livestock products and vegetable prices being mainly positively impacted, fruit prices being mainly negatively impacted, and aquatic product prices being negatively impacted in the early stage and positively impacted in the middle and late stages. The online negative sentiment had the greatest impact on medium-risk area agricultural product prices, followed by low-risk areas, with the lowest impact found on the high-risk area agricultural product prices. Three policy suggestions for epidemic monitoring, public opinion guidance and control, and the timely release of agricultural product information are given based on the results.

‘Remember Their Names’: Gay Men’s HIV and AIDS Death Notices, 1984–96

When the HIV and AIDS epidemic began to decimate gay male populations in the 1980s and 1990s, the expression of grief by friends of the deceased was inhibited by the social stigma attached to both the virus and their sexualities. This article tells how the grieving gay community used death notices and obituaries published in the fortnightly Sydney Star Observer to make the community’s grieving a shared public emotion and an assertion of the value of the lives lost to HIV and AIDS. Based on interviews with men who read and/or placed the notices, this article not only conveys the emotional and political impact of the epidemic on the survivors; it argues that the death notices and obituaries were part of a wider cultural struggle over Australians’ norms of grieving and of sexuality.

Exploring the Impact of Urban Built Environment on Public Emotions Based on Social Media Data: A Case Study of Wuhan

In the era of public participation in government, public emotions and expectations are important considerations influencing urban construction, planning, and management. A desirable urban environment can make people feel at ease and comfortable and contribute to promoting positive public emotions. However, in the process of rapid urban development, the high-density and overloaded urban built environment has triggered people’s mental tension and anxiety and has contributed to negative emotions. Thus, this study aimed to explore the spatial distribution of public emotions and urban built environments in cities and to thoroughly investigate the correlation between urban built environments and public emotions. Considering the lack of dynamic elements analysis and emotions spatial analysis in previous studies, this study takes Wuhan City as an example, uses social media big data as the basis for text emotion analysis, introduces dynamic traffic elements, and establishes a multidimensional urban built environment measurement index system from five aspects: land use, spatial form, road and traffic, green space and open space, and daily life service facilities. Subsequently, the spatial distribution characteristics of public sentiment and urban built environment elements in Wuhan were analyzed. Finally, a geographically weighted regression method was used to analyze the degree of influence of different urban built environment elements on public emotions. The results showed that public emotions in Wuhan are not homogeneously distributed in terms of score and space and that there are significant differences. The urban built environment has a significant influence on public emotions. Higher land use mix, higher road network density, higher number of public transportation facilities, higher number of public open spaces, lower traffic congestion, and impact of freight transportation play important roles in promoting positive emotions. Therefore, in the process of urban construction, planners and decision makers should purposefully improve the quality of the built environment. Measures can include improving the mix of land functions, alleviating traffic congestion, avoiding the negative effects of freight traffic, rationally constructing green and open spaces, and improving various living facilities. This can help contribute toward improving urban functions and urban environments, and promote the construction of a people-oriented healthy city.

Mining of Opinions on COVID-19 Large-Scale Social Restrictions in Indonesia: Public Sentiment and Emotion Analysis on Online Media.

BackgroundOne of the successful measures to curb COVID-19 spread in large populations is the implementation of a movement restriction order. Globally, it was observed that countries implementing strict movement control were more successful in controlling the spread of the virus as compared with those with less stringent measures. Society’s adherence to the movement control order has helped expedite the process to flatten the pandemic curve as seen in countries such as China and Malaysia. At the same time, there are countries facing challenges with society’s nonconformity toward movement restriction orders due to various claims such as human rights violations as well as sociocultural and economic issues. In Indonesia, society’s adherence to its large-scale social restrictions (LSSRs) order is also a challenge to achieve. Indonesia is regarded as among the worst in Southeast Asian countries in terms of managing the spread of COVID-19. It is proven by the increased number of daily confirmed cases and the total number of deaths, which was more than 6.21% (1351/21,745) of total active cases as of May 2020.ObjectiveThe aim of this study was to explore public sentiments and emotions toward the LSSR and identify issues, fear, and reluctance to observe this restriction among the Indonesian public.MethodsThis study adopts a sentiment analysis method with a supervised machine learning approach on COVID-19-related posts on selected media platforms (Twitter, Facebook, Instagram, and YouTube). The analysis was also performed on COVID-19-related news contained in more than 500 online news platforms recognized by the Indonesian Press Council. Social media posts and news originating from Indonesian online media between March 31 and May 31, 2020, were analyzed. Emotion analysis on Twitter platform was also performed to identify collective public emotions toward the LSSR.ResultsThe study found that positive sentiment surpasses other sentiment categories by 51.84% (n=1,002,947) of the total data (N=1,934,596) collected via the search engine. Negative sentiment was recorded at 35.51% (686,892/1,934,596) and neutral sentiment at 12.65% (244,757/1,934,596). The analysis of Twitter posts also showed that the majority of public have the emotion of “trust” toward the LSSR.ConclusionsPublic sentiment toward the LSSR appeared to be positive despite doubts on government consistency in executing the LSSR. The emotion analysis also concluded that the majority of people believe in LSSR as the best method to break the chain of COVID-19 transmission. Overall, Indonesians showed trust and expressed hope toward the government’s ability to manage this current global health crisis and win against COVID-19.

Systemic racism in global health: a personal reflection

Systemic racism in global health: a personal reflection

Text Mining Approaches to Analyze Public Sentiment Changes Regarding COVID-19 Vaccines on Social Media in Korea.

The COVID-19 pandemic has affected the entire world, resulting in a tremendous change to people’s lifestyles. We investigated the Korean public response to COVID-19 vaccines on social media from 23 February 2021 to 22 March 2021. We collected tweets related to COVID-19 vaccines using the Korean words for “coronavirus” and “vaccines” as keywords. A topic analysis was performed to interpret and classify the tweets, and a sentiment analysis was conducted to analyze public emotions displayed within the retrieved tweets. Out of a total of 13,414 tweets, 3509 were analyzed after preprocessing. Eight topics were extracted using the Latent Dirichlet Allocation model, and the most frequently tweeted topic was vaccine hesitation, consisting of fear, flu, safety of vaccination, time course, and degree of symptoms. The sentiment analysis revealed a similar ratio of positive and negative tweets immediately before and after the commencement of vaccinations, but negative tweets were prominent after the increase in the number of confirmed COVID-19 cases. The public’s anticipation, disappointment, and fear regarding vaccinations are considered to be reflected in the tweets. However, long-term trend analysis will be needed in the future.

Cross-Subject EEG Emotion Recognition With Self-Organized Graph Neural Network.

As a physiological process and high-level cognitive behavior, emotion is an important subarea in neuroscience research. Emotion recognition across subjects based on brain signals has attracted much attention. Due to individual differences across subjects and the low signal-to-noise ratio of EEG signals, the performance of conventional emotion recognition methods is relatively poor. In this paper, we propose a self-organized graph neural network (SOGNN) for cross-subject EEG emotion recognition. Unlike the previous studies based on pre-constructed and fixed graph structure, the graph structure of SOGNN are dynamically constructed by self-organized module for each signal. To evaluate the cross-subject EEG emotion recognition performance of our model, leave-one-subject-out experiments are conducted on two public emotion recognition datasets, SEED and SEED-IV. The SOGNN is able to achieve state-of-the-art emotion recognition performance. Moreover, we investigated the performance variances of the models with different graph construction techniques or features in different frequency bands. Furthermore, we visualized the graph structure learned by the proposed model and found that part of the structure coincided with previous neuroscience research. The experiments demonstrated the effectiveness of the proposed model for cross-subject EEG emotion recognition.