Trends In Public Opinion Research Articles

Utilizing Baidu Index Data to Investigate the Spatiotemporal Characteristics of Public Concern towards Lifelong Education

Background and purpose: With the advancement of Industry 4.0 and the vigorous development of the Internet, search engines have become one of the important platforms for the public to obtain information. As the world's largest Chinese search engine, Baidu is widely popular with the Chinese public. Similar to Google Trends, the Baidu Index is one of the most important databases on the Internet and even in the entire data age. This study adopted the Baidu Index to investigate the public's attention to "Lifelong Education" and analyze its changing trend and geographical distribution, to grasp the public's attention to it in a more comprehensive way. Materials and methods: The Baidu Index was used to obtain the attention of the keyword “Lifelong Education" from July to November 2023. The characteristics of attention are analyzed from three aspects: trend research, demand graph, and user portrait. Finally, it focuses on the comparison between the keyword "Lifelong Education" and the related word "Lifelong Education Platform". Results: The term "Lifelong Education" showed two fluctuations during the period, both of which showed an initial rise and then a decline. For the demand graph, the public pays the most attention to "Lifelong Education Platform", "Lifelong Learning" and "Compulsory Education". The group that pays more attention to "Lifelong Education" is mainly distributed in East China, mostly female, 20~29 years old. Conclusion: Similar to Google Trends, the Baidu Index is a useful tool to study the trend of keyword searches, insight into the change in netizens' demand and monitoring the trend of media public opinion. The results show that the main reasons for the two obvious changes in search volume are that the National Open University of China opens freely lifelong education platform and provides free and comprehensive learning resources, and "Lifelong Education" appears as a translation in the National College English Test Band 4 (CET-4) in June 2023.

People's Voice: Exploring Discussion Themes on Generative AI and Libraries on X

ABSTRACTThe launch of ChatGPT and other Generative Artificial Intelligence (GenAI) models has significantly influenced people's perceptions of AI, making its impact a widely discussed topic in various areas. In Library and Information Science (LIS), researchers experience the opportunities and challenges presented by GenAI, such as using the rich text from books and digital collections as training data for GenAI and enabling quick summarization and reference consultation for patrons. Meanwhile, concerns have arisen about the generation of fake information and nonexistent text by GenAI and their potential effects on users. This project explores the themes in public discussions regarding the impact of GenAI on libraries. To explore public opinions about the impact of GenAI in libraries, we collected over 50,000 posts from X (formerly Twitter) between November 1, 2022, and May 6, 2024. We manually sampled 1,000 posts focusing on GenAI in the context of libraries. We conducted content analysis to identify the key themes and relationships among these themes. This poster presents our initial findings on different themes and highlights the trends in public opinion. Our analysis provides a curated dataset for training APIs for future investigation.

From Impoliteness to Aggression: Examining Cyberbullying Patterns on Sina Weibo Using the “Discourse-Cognition-Society” Model

This study applies Culpeper’s Impoliteness Strategy and van Dijk’s “Discourse-Cognition-Society” Model to analyze 664 comments from nine cyberbullying incidents on Sina Weibo, examining these within their social contexts. Ten sub-strategies were identified, belonging to two main categories: bald-on record and off-record impoliteness strategies, finding a dominant use of bald-on record strategies. Our analysis underscores that with the use of impoliteness strategies, cyberbullying language mirrors the cognition and ideologies of speakers, such as traditional attitudes towards “good students” and the prejudice against non-mainstream appearances, reflecting the trends in public opinion, influencing societal cognition and dynamics. Extending van Dijk’s framework, our study offers new insights into the complex interplay between online communication, societal realities, and cognitive processes. Furthermore, this study proposes recommendations for managing online communication, conducive to fostering a sound online communication environment.

Sentiment Analysis of Arab Tweets: Unveiling Public Opinion Trends Using Machine Learning

Abstract: The rapid spread of social media platforms, especially Twitter, has provided a rich and valuable source of data for understanding public opinion. This thesis presents a comprehensive study on analyzing the sentiments of Arab tweeters, intending to reveal public opinion trends using advanced machine learning techniques. The study begins by collecting a large data set of tweets in Arabic. Preprocessing steps are carefully implemented to deal with the unique linguistic characteristics of Arabic, including encoding, normalization, and deletion. The BERT system was used for the application and the model was created using AJGT.csv Dataset. The evaluation was performed using different metrics. The test results were as follows: 0.9822 % accuracy, precision 0.9823, recall 0.9822, F1 0.982265 and FPR 0.0177. The findings of this research provide significant insights into the prevailing public opinion trends in the Arab world, revealing the potential of sentiment analysis as a powerful tool for policymakers, businesses, and researchers to gauge public sentiment and make informed decisions. The thesis concludes with a discussion of the limitations of the study and suggestions for future research directions, emphasizing the need for more sophisticated models and larger datasets to further enhance the accuracy and reliability of sentiment analysis in the Arabic language.

Empowering Equality Law With Nlp: A Social Network Analysis Approach to Drive Legal SDGS

Objectives: The purpose of this study is to use social media analysis to understand people's opinions about legal equality. Theoretical Framework: The study builds on social studies theory and existing literature to explore the complexity of society's reaction to legal equality. It focuses on how public opinion evolves over time and the factors influencing these changes, including language, age, and gender. Method: Data was gathered from various social media platforms on September 8, 14, and 15, 2023, using the Talkwalker Social Intelligence Platform. The analysis involved monitoring public opinion trends and performing a theme analysis to identify recurring themes in the discourse. Results and Discussion: The findings revealed essential trends in societal reactions to legal equality. Notable changes in perceptions of specific issues were observed, and prominent aspects of legal equality, such as language, age, and gender, were identified. These results provide a comprehensive understanding of how society perceives legal equality and the dynamics influencing these perceptions. Research Implications: The study enhances existing literature and advances social studies theory by offering insights into the complexity of societal reactions to legal equality. It also demonstrates the potential of social media analysis using Social Network Analysis (SNA) and Talkwalker-type tools to understand how social interactions shape opinions and to inform policy planning. Originality/Value: This research illuminates the social dynamics of legal equality and showcases the effectiveness of combining SNA with social media analysis tools. The findings offer valuable contributions to theoretical and practical discussions on legal equality and policy-making, particularly in the context of promoting the SDGs.

Societal guardrails for AI? Perspectives on what we know about public opinion on artificial intelligence

Abstract Artificial intelligence (AI) not only holds immense potential for improving quality of life but also creates complex ethical, legal, and societal challenges. AI has gained significant attention recently, particularly by introducing ChatGPT and other emerging applications. This paper offers a comprehensive overview of public opinion trends on AI, drawing from fifteen surveys conducted in the USA over the past 4 years. The findings reveal a divided American public regarding AI’s applications. Americans generally call for more regulation and government oversight, expressing a need for increased measures before fully embracing AI. While specific AI applications, such as skin cancer screening, have been welcomed, most Americans emphasize the importance of addressing societal concerns such as racial bias and inequities before the widespread implementation of AI. The evolving landscape of AI necessitates ongoing monitoring of public sentiment and the consideration of societal implications.

Research on the evolution of cross-platform online public opinion for public health emergencies considering stakeholders.

To explore the different processes of the themes and emotional evolution of various stakeholders in the network public opinion of sudden public health emergencies at different stages of the public opinion evolution lifecycle. This paper proposes a cross-platform analysis method for online public opinion during the public health emergencies based on stakeholders. Firstly, data from multiple platforms are collected and integrated. Secondly, stakeholders are categorized and the stages of public opinion evolution are determined based on stakeholder theory and lifecycle theory. Finally, the Latent Dirichlet Allocation (LDA)+Word2vec model and Convolutional Neural Network (CNN) model are used to analyze the themes and emotional evolution of stakeholders during different stages of public opinion evolution. There are differences in the evolution patterns of different types of stakeholders. The evolution process of stakeholders' focus points exhibits a two-stage transition from concentration to divergence. The focus points of stakeholders are closely associated with their respective social domains. The emotions of the public undergo a three-stage process of positive-negative-positive change. This study can provide a reference for the government to have a more comprehensive understanding of the development trend of public opinion and reduce the negative impact of public opinion.

Enhanced earthquake impact analysis based on social media texts via large language model

Social media aids disaster response but suffers from noise, hindering accurate impact assessment and decision making for resilient cities, which few studies considered. To address the problem, this study proposes the first domain-specific LLM model and an integrated method for rapid earthquake impact assessment. First, a few categories are introduced to classify and filter microblogs considering their relationship to the physical and social impacts of earthquakes, and a dataset comprising 7282 earthquake-related microblogs from twenty earthquakes in different locations is developed as well. Then, with a systematic analysis of various influential factors, QuakeBERT, a domain-specific large language model (LLM), is developed and fine-tuned for accurate classification and filtering of microblogs. Meanwhile, an integrated method integrating public opinion trend analysis, sentiment analysis, and keyword-based physical impact quantification is introduced to assess both the physical and social impacts of earthquakes based on social media texts. Experiments show that data diversity and data volume dominate the performance of QuakeBERT and increase the macro average F1 score by 27 %, while the best classification model QuakeBERT outperforms the CNN- or RNN-based models by improving the macro average F1 score from 60.87 % to 84.33 %. Finally, the proposed approach is applied to assess two earthquakes with the same magnitude and focal depth. Results show that the proposed approach can effectively enhance the impact assessment process by accurate detection of noisy microblogs, which enables effective post-disaster emergency responses to create more resilient cities.

Study of the Problems of Determining Public Opinion of the Israeli-Palestinian War in Social Networks

This paper explores the use of neural networks for determining public opinion on the Israeli-Palestinian conflict. In today's world, where numerous user opinions on various topics are posted daily on social media platforms, it is important to be able to analyze this public sentiment. The method of sentiment analysis and weighting of votes in comments is proposed, allowing for the identification of overall audience moods and assessment of their reactions to different events or content. The research results indicate the potential usefulness of these methods for marketing strategies, reputation management, and decision-making in various fields. However, there are challenges associated with data verification and consideration of manipulative interventions in public opinion. This work aims to review the current state of research in this area and identify prospects for further development. In today's digital world, analyzing public opinion on social media is becoming increasingly important. Understanding how users react to various topics and events is crucial for various fields, from marketing to politics. This paper discusses methods of sentiment analysis and vote weighting in comments using the example of analyzing public opinion on the Israeli-Palestinian conflict on the Reddit social network. The use of natural language processing tools, machine learning, and data visualization allows for valuable insights into users' emotional attitudes toward discussed topics and trends in public opinion. However, to achieve the best results, it is necessary to consider not only textual content but also social interaction and user status. The development of effective social media analysis tools opens the way to a deeper understanding of public opinion and making informed decisions in various areas of social life.

Research on the Sentiment Analysis and Evolutionary Mechanism of Sudden Network Public Opinion Based on SNA-ARIMA Model with Text Mining

This article is based on the massive text data in the digital era, using text mining methods to explore the evolution mechanism of online public opinion, in order to achieve the control of public opinion trends in sudden network events and regulate the stability of online and offline public opinion. We introduced time series for decision-making assistance, combined with time series autoregressive integrated moving average model (ARIMA) for prediction, and comprehensively evaluates the evolution of public opinion from two dimensions: sentimental orientation and social network analysis (SNA). Based on this, a new exploration model for public opinion evolution, SNA-ARIMA, is constructed. The research results indicate that the process of public opinion dissemination exhibits distinct phased characteristics, and each stage is comprehensively influenced by different key nodes or factors. This study provides decision support for managing public opinion crises, making the identification of key nodes in online public opinion events, the evolution mechanism of public opinion, and the guidance of public opinion crises more systematic, forward-looking, and scientific.

An Enhanced IHHO-LSTM Model for Predicting Online Public Opinion Trends in Public Health Emergencies

Social networks accelerate information communication in public health emergencies. Some negative information may cause an outbreak of public opinion crisis. Accurately predicting online public opinion trends can help the relevant departments take timely and effective measures to cope with risks. Therefore, this research proposes a prediction model incorporating the swarm intelligence optimization algorithm and the deep learning method. In this model, we improve the Harris Hawks Optimization (HHO) algorithm by introducing the Cauchy distribution function, the stochastic contraction exponential function, and the adaptive inertia weight. Then we utilize the improved HHO (IHHO) algorithm to optimize the hyperparameters of the deep learning method LSTM, including the learning rate and the number of neurons in the hidden layer. Finally, we construct the IHHO-LSTM model to make predictions in three public health emergencies. The experiments verify that the proposed model outperforms other single and hybrid models. The MAPE values reduce by 78.34%, 54.46%, and 46.42% relative to the average values of the three single models. Compared with the mean values of the two hybrid models, the MAPE values decrease by 47.69%, 18.45%, and 5.78%. The IHHO-LSTM model can be applied to public opinion early warning and reversal identification, providing a reference in public opinion management.

A Multi-Information Dissemination Model Based on Cellular Automata

Significant public opinion events often trigger pronounced fluctuations in online discourse. While existing models have been extensively employed to analyze the propagation of public opinion, they frequently overlook the intricacies of information dissemination among heterogeneous users. To comprehensively address the implications of public opinion outbreaks, it is crucial to accurately predict the evolutionary trajectories of such events, considering the dynamic interplay of multiple information streams. In this study, we propose a SEInR model based on cellular automata to simulate the propagation dynamics of multi-information. By delineating information dissemination rules that govern the diverse modes of information propagation within the network, we achieve precise forecasts of public opinion trends. Through the concurrent simulation and prediction of multi-information game and evolution processes, employing Weibo users as nodes to construct a public opinion cellular automaton, our experimental analysis reveals a significant similarity exceeding 98% between the proposed model and the actual user participation curve observed on the Weibo platform.

The Challenge of Foreign Investment in Mexico: Between Theory and Public Opinion. An Analysis of Perception and Challenges Regarding Fdi As A Source of Innovation

Purpose: This research explores the public opinion of online news consumers, expressed through Facebook comments, regarding Foreign Direct Investment (FDI) as a source of innovation in Mexico. The study seeks to comprehend the challenges faced by FDI in the country and their potential impact on the public perception of FDI as a driver of innovation in the Mexican economy. Theoretical Framework: This study examines previous research that explores FDI as a source of innovation in host economies, aiming to provide the context necessary for understanding how the public perceives the role of foreign resources in fostering innovation in Mexico. Design/Methodology/Approach: The methodology employed in this research was the phenomenological design of a qualitative approach to analyze interactions and comments on Facebook from users who consume online news related to FDI in Mexico. The aim was to identify patterns and trends in public opinion regarding the perception of FDI as a source of innovation. Findings: The theoretical perspective suggests that FDI can foster innovation in host countries; however, an analysis of public perception revealed challenges and controversies associated with FDI in the Mexican economy. Research, Practical & Social Implications: The comprehension of public opinion regarding FDI as a source of innovation allows the establishment of suitable strategies for the integration of Mexican companies into the international market. Originality/Value: This paper offers a perspective to understand how public opinion of FDI can influence its role as a catalyst for innovation in the Mexican economy.

A Multi-Agent System for Fine-Grained Opinion Dynamics Analysis in Online Social Networks

The influence and dissemination of users’ opinions are the essence of the opinion dynamics in online social networks (OSNs). Understanding the process of users’ opinion formation and propagation can provide better service for public opinion monitoring and product advertising. However, most opinion dynamics research typically do not distinguish between user opinion formation and propagation, instead focusing on the process of mass opinion polarization. This article presents a multiagent system (MAS) to analyze fine-grained opinion dynamics (FOD) by agent-based modeling (ABM). At the macro level, the MAS-FOD system we designed can produce a statistical analysis of public opinion evolution based on different parameter constellations and analyze the changes in personalized agent opinions. At the micro level, the agent-based model is presented to describe the single user in OSNs as an individualized agent in MAS-FOD. Specifically, we propose two mechanisms, agent-based opinion formation (AOF) mechanism and agent-based opinion propagation (AOP) mechanism, for agents to form and disseminate opinions, respectively. Meanwhile, multidimensional social influence features mining from self, local neighbors, and global topic community are defined and used in two mechanisms to train personalized agents to self-adapt to the MAS-FOD system. We demonstrate the rationality and effectiveness of the MAS-FOD system from two types of experiments: empirical analysis and simulation analysis. The former is driven by empirical social network data to analyze the predictive performance of AOF; the latter simulates the public opinion propagation through the AOP mechanism in the MAS-FOD system. The results demonstrate that: 1) the AOF outperforms SOTA methods in the accuracy of agent opinion prediction; 2) the dissemination effect based on the AOP is more closer to the actual evolution trend of public opinion than the baseline; and 3) the MAS-FOD system can perform fine-grained analysis of opinion dynamics by adjusting different parameters.

Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media.

The COVID-19 pandemic raised wide concern from all walks of life globally. Social media platforms became an important channel for information dissemination and an effective medium for public sentiment transmission during the COVID-19 pandemic. Mining and analyzing social media text information can not only reflect the changes in public sentiment characteristics during the COVID-19 pandemic but also help the government understand the trends in public opinion and reasonably control public opinion. First, this study collected microblog comments related to the COVID-19 pandemic as a data set. Second, sentiment analysis was carried out based on the topic modeling method combining latent Dirichlet allocation (LDA) and Bidirectional Encoder Representations from Transformers (BERT). Finally, a machine learning linear regression (ML-LR) model combined with a sparse matrix was proposed to explore the evolutionary trend in public opinion on social media and verify the high accuracy of the model. The experimental results show that, in different stages, the characteristics of public emotion are different, and the overall trend is from negative to positive. The proposed method can effectively reflect the characteristics of the different times and space of public opinion. The results provide theoretical support and practical reference in response to public health and safety events.

Employing the BERT model for sentiment analysis of online commentary

The objective of this research is to carry out a tone and semantic sentiment analysis of network comments on new media platforms by leveraging the BERT model. With the burgeoning popularity of social media, network comments, rich in emotional and tonal features, have emerged as a significant part of the online culture. Accurate interpretation and analysis of these comments' sentiment and semantic meanings are paramount to grasping online public opinion and user psychology. In this study, the BERT model, lauded for its bidirectional encoding and contextual understanding capabilities, is selected to scrutinize the sentiment and tone of network comments on new media platforms. Through a process of pre-training and fine-tuning, the sentiment attitudes and polarity of comments are accurately identified along with their conveyed tonal features, such as joy, anger, and sarcasm. Conducting an accurate tone and semantic sentiment analysis of network comments on new media platforms facilitates a profound understanding of user preferences and trends in public opinion. This can assist in optimizing content recommendations, enhancing user experiences, and increasing the operational effectiveness of new media platforms. The outcomes of this research will bear significant implications for studies and applications in online culture, offering invaluable references and guidance in related domains.

The challenge of Foreign Investment in Mexico: Between Theory and Public Opinion. An Analysis of Perception and Challenges Regarding FDI as a Source of Innovation

Purpose: This research explores the public opinion of online news consumers, expressed through Facebook comments, regarding Foreign Direct Investment (FDI) as a source of innovation in Mexico. The study seeks to comprehend the challenges faced by FDI in the country and their potential impact on the public perception of FDI as a driver of innovation in the Mexican economy. Theoretical Framework: This study examines previous research that explores FDI as a source of innovation in host economies, aiming to provide the context necessary for understanding how the public perceives the role of foreign resources in fostering innovation in Mexico. Design/Methodology/Approach: The methodology employed in this research was the phenomenological design of a qualitative approach to analyze interactions and comments on Facebook from users who consume online news related to FDI in Mexico. The aim was to identify patterns and trends in public opinion regarding the perception of FDI as a source of innovation. Findings: The theoretical perspective suggests that FDI can foster innovation in host countries; however, an analysis of public perception revealed challenges and controversies associated with FDI in the Mexican economy. Research, Practical & Social Implications: The comprehension of public opinion regarding FDI as a source of innovation allows the establishment of suitable strategies for the integration of Mexican companies into the international market. Originality/Value: This paper offers a perspective to understand how public opinion of FDI can influence its role as a catalyst for innovation in the Mexican economy.

Search Trends for Micronutrients on Google Trends in Iran for Historical Effect of the COVID-19 Pandemic: A Ecological Study

Background: The use of micronutrients to maintain health, prevent disease, and reduce disease severity is increasing. Objectives: This study aimed to evaluate the impact of the COVID-19 pandemic on the trend of public opinion toward micronutrients in Iran. Methods: An ecological study was conducted in the Google Trend (GT) database. First, users' specified search terms related to “COVID-19,” “corona (Iranian COVID-19),” “vitamin-D,” “vitamin-C,” and “Neurobion” were searched among all searches performed using GTs from January 2017 to 2023. Then, Spearman's correlation coefficient was used to check the correlation between the keywords of Micronutrients with the words COVID-19 during different years. Results: The trend for all three micronutrients (vitamin D and C and Neurobion) increased significantly since 2020, consistent with the search trend for COVID-19, corona, and the waves of COVID-19 in Iran. Vitamin D peaks were observed in 2020 (first wave of COVID-19), vitamin C in 2021 (fifth wave), and Neurobion in 2022 (seventh and eighth waves). There was a high and objective correlation coefficient between COVID-19, vitamin C (R2 = 0.73, P = 0.001), and Neurobion (R2 = 0.63, P = 0.001). However, no correlation was observed with vitamin D (R2 = 0.18, P = 0.12). Conclusions: Based on the results, the search for micronutrients in Iran increased significantly during the COVID-19 pandemic. Public interest might reflect behavior and consumption. GT is a helpful tool for epidemiologists to understand the interests and trends of the general population.

Analysis of Public Opinion Crisis Control by Mainstream Media During COVID-19

The sudden outbreak of new pneumonia has had a profound impact on the global world and all walks of life. At the same time, it also creates a torrent of public opinion under the mud of information and accelerates the fermentation and loss of control of the Internet public opinion field. Tuchman, a famous scholar, claimed in Doing the News that news is the constructed reality, and the mainstream media controls the trend of public opinion when disseminating news. In this paper, public opinion at each stage of the epidemic was analyzed based on the crisis phase analysis theory proposed by Steven Fink. In the face of the epidemic crisis, the mapping of the media plays an important role. Mainstream media should control the public opinion crisis through different discourse strategies and social effects caused by communication methods.

IPSO-LSTM hybrid model for predicting online public opinion trends in emergencies.

When emergencies are widely discussed and shared, it may lead to conflicting opinions and negative emotions among internet users. Accurately predicting sudden network public opinion events is of great importance. Therefore, this paper constructs a hybrid forecasting model to solve this problem. First, this model introduces an improved inertia weight and an adaptive variation operation to enhance the Particle Swarm Optimization (PSO) algorithm. Then, the improved PSO (IPSO) algorithm optimizes the parameters of the Long Short-Term Memory (LSTM) neural network. Finally, the IPSO-LSTM hybrid prediction model is constructed to forecast and analyze emergency public opinion dissemination trends. The experimental outcomes indicate that the IPSO-LSTM model surpasses others and has high prediction accuracy. In the four emergency predictions we select, the MAPE value of IPSO-LSTM is 74.27% better than that of BP, 33.96% better than that of LSTM, and 13.59% better than that of PSO-LSTM on average. This study aims to assist authorities in quickly identifying potential public opinion crises, developing effective strategies, and promoting sustainable and positive growth in the network environment.