Understanding Public Opinion Research Articles

Exploring #MentholBan on TikTok: A Thematic and Semantic Network Analysis.

In April 2021, the U.S. Food and Drug Administration (FDA) announced its intention to ban the sale of menthol cigarettes and cigars. Decades of research support the premise that a menthol ban will reduce initiation and disparities in tobacco-related disease among menthol smokers. The tobacco industry opposed such a policy and worked for decades to shape public opposition. Social media discourse can inform our understanding of public opinion about the proposed ban and guide communication strategies and policy implementation. This research employed a mixed-methods design to explore TikTok posts discussing the announced menthol ban. Using a TikTok web scraper to extract all content in the #mentholban hashtag (n = 171), we coded for 11 themes, characterized content with descriptive statistics, and created a semantic network of co-occurring hashtags. We found primarily negative attitudes towards the U.S. ban announcement and a large volume of menthol "hacks" to circumvent the bans. Our semantic network analysis revealed strong co-occurrences between #mentholban and popularity-seeking hashtags. The metadata associated with each TikTok demonstrated that most posters in #mentholban are not "influencers" in the sense of having many followers, aside from a few niche organizations with multiple posts. We found that perceived political and racial motivations shaped posters' assessments of the menthol ban. Furthermore, we uncovered how individuals and organizational actors shaped menthol ban content on TikTok. Our study indicates targeted marketing from alternative menthol product companies and advocacy organizations. The latter of these organizations is more likely to saturate the TikTok landscape with multiple posts and strategic hashtags. This study pursued an exploration of tobacco policy discussion on TikTok, specifically related to the FDA-proposed menthol ban. TikTok is a newer platform and our study provides early evidence of policy discussion emerging there, including the types of accounts creating the content and their valence toward the policy.

Unveiling Sentiments: A Comprehensive Analysis of Arabic Hajj-Related Tweets from 2017–2022 Utilizing Advanced AI Models

Sentiment analysis plays a crucial role in understanding public opinion and social media trends. It involves analyzing the emotional tone and polarity of a given text. When applied to Arabic text, this task becomes particularly challenging due to the language’s complex morphology, right-to-left script, and intricate nuances in expressing emotions. Social media has emerged as a powerful platform for individuals to express their sentiments, especially regarding religious and cultural events. Consequently, studying sentiment analysis in the context of Hajj has become a captivating subject. This research paper presents a comprehensive sentiment analysis of tweets discussing the annual Hajj pilgrimage over a six-year period. By employing a combination of machine learning and deep learning models, this study successfully conducted sentiment analysis on a sizable dataset consisting of Arabic tweets. The process involves pre-processing, feature extraction, and sentiment classification. The objective was to uncover the prevailing sentiments associated with Hajj over different years, before, during, and after each Hajj event. Importantly, the results presented in this study highlight that BERT, an advanced transformer-based model, outperformed other models in accurately classifying sentiment. This underscores its effectiveness in capturing the complexities inherent in Arabic text.

Sentiment Analysis of Twitter Data: Understanding Public Opinion before the 15th General Elections in Malaysia

Amid the ever-growing social media content, sentiment analysis is a technique that is necessary for analyzing public opinion. This paper aims to discover what sentiment prevailed and what reaction took place preceding the election. This paper also presents the results of a study that applied sentiment analysis to Twitter data relating to the 15th General Elections of Malaysia. This research reviews several sentiment analysis techniques based on Twitter data. Using the method of analyzing 1566 tweets, including re-tweets and replies, gathered between November 12th and November 18th, 2022, the findings give us an understanding of the level of emotions that were tweeted by the users of Twitter towards the 15th general election in Malaysia. The results indicate that the sentiments expressed in the analysed tweets are distributed as follows: slightly positive (41%), positive (31%), neutral (24%), and slightly negative (4%).

Analysis of X (Formerly Twitter) Users’ Perceptions towards the Election of Jolidee Matongo as Mayor of Johannesburg

This paper presents a study on the analysis of X (formerly Twitter) users’ perceptions towards the election of Jolidee Matongo as mayor of Johannesburg, South Africa in 2021. This election was significant because it was the first time that a person of Zimbabwean descent had been elected mayor of Johannesburg. Given the vast amount of user-generated content on Twitter, analysing tweets can serve as a valuable proxy for capturing public opinion. However, to date, there appears to be a notable research gap concerning the wide adoption of tweet analysis for understanding public opinion. The study used 4324 tweets that were collected from X (Twitter) via the Twitter application programming interface (API) over a period of one week after the election. Sentiment analysis and topic modelling were used to analyse the results. The results suggested that social media platforms can be used to gain valuable insights into public opinion and the different perspectives. Social media platforms can also be used to spread xenophobia. Cognitive biases, specifically the confirmation bias has been found to influence Twitter users’ perception towards the election of Matongo. A significant number of negative tweets expressed xenophobic views due to Matongo’s Zimbabwean roots. However, the average sentiment score of 0.10 suggested that the overall sentiment of the tweets was slightly positive.

Correcting Myopia: Effect of Information Provision on Support for Preparedness Policy

Some scholars argue that the public is generally myopic in their attitudes about disaster preparedness spending, because they prefer to spend money on disaster response rather than preparedness, despite the greater cost effectiveness of the later. Given voters’ general lack of policy information, it is possible that limited support for preparedness comes from lack of information about its efficacy. In this paper, we build on these studies by examining how people respond to new information about the effectiveness of policy initiatives in the context of public health and the COVID-19 pandemic. Through two online survey experiments with over 3400 respondents, we demonstrate that information can lead people to update attitudes about preparedness, illustrating the potential for information campaigns to increase support for preparedness policies. Our results suggest that information about the efficacy of preparedness can increase support for these policies, and the information effect exists even for individuals whose prior beliefs were that public health programs were ineffective. These results suggest that information can make people more supportive of preparedness spending, which could provide electoral incentives for its provision. We conclude by providing some directions for future research to enhance our understanding of public opinion and preparedness spending.

Autocratic Legalism, Partisanship, and Popular Legitimation in Authoritarian Cameroon.

Authoritarian regimes regularly turn to the law to justify repression. This article examines whether invoking legal institutions has a persuasive effect on public perceptions of repression, and whether that effect is shaped by partisanship. The article uses the case of Cameroon's Special Criminal Tribunal, created in 2011 to prosecute high-profile corruption cases. A survey experiment was designed that describes the arrest and trial of a suspected corrupt oppositional minister and reminds a treatment group about the Special Criminal Tribunal. The results show that neither regime nor opposition partisans are swayed by legal justifications for repression. By contrast, nonpartisans respond negatively to autocratic legalism, particularly those with low levels of regime trust. The article clarifies when autocratic legalism might be used for public legitimation, suggests that partisanship is a useful lens for understanding public opinion in an autocracy, and elaborates upon the meaning of nonpartisanship in electoral authoritarian regimes.

COVID-19 Sentiment Analysis using K-Means and DBSCAN

The analysis of sentiment towards COVID-19 plays a crucial role in understanding public opinion. This research paper proposes sentiment analysis using K-means and DBSCAN clustering algorithms on the dataset of tweets related to COVID-19. Pre-processing and extraction of features is carried out using Term Frequency-Inverse Document Frequency (Tf-idf) to capture the weight of words in the dataset. K-means clustering is explored to group similar sentiments together, enabling the identification of sentiment clusters related to COVID-19. The DBSCAN algorithm is then employed to identify outliers and noise in the sentiment clusters. The evaluation metrics considered were accuracy, recall, F1-score, and precision. It was observed that DBSCAN was more effective in identifying underlying patterns in the data more accurately.

Analyzing Sentiment and Emotion in Omicron-Related Social Media Content: An NLP Perspective

This paper presents an approach to sentiment analysis using various text processing techniques on a dataset of textual data related to the Omicron variant. The study applies Natural Language Processing (NLP) methods, including data cleaning, stemming, and stopword removal, to preprocess the text. Subsequently, it employs Word Cloud visualizations to explore the frequency of words and hashtags in the dataset. Sentiment analysis is performed using the VADER sentiment analysis tool to categorize the sentiments expressed in the text into positive, negative, and neutral categories. The aggregated sentiment scores are analyzed to determine the overall sentiment trend within the dataset. The results indicate the predominant sentiment as positive, with detailed insights into the distribution of sentiments. The paper highlights the effectiveness of combining NLP techniques with sentiment analysis for understanding public opinion and trends in textual data.

‘I Tweet about Our #GreenEnergy’—Automated Classification of Social Identity and Opinion Mining of the Dutch Twitter Discourse on Green-Energy Technologies

Understanding the complexities of public opinion is crucial for a green-energy transition. This present study examines the sentiment of public opinion towards various energy technologies on Twitter during the Dutch 2021 general elections. A dataset comprising 186,822 tweets and profile descriptions was analyzed using two automated text classifiers to explore how individuals with different self-proclaimed identities perceive green-energy technologies. The analysis involved the application of the sentiment and social identity classifier models, followed by a frequency and co-occurrence analysis. The findings revealed a negative overall sentiment towards green-energy technologies in the Twitter discourse. It further showed that perceptions may differ depending on a technology’s development stage, with emerging technologies generally receiving more favorable views compared to established ones. Furthermore, it was found that, although there is a general trend of negative sentiment based on political identity, and positive sentiment based on occupational identity, this trend did not consistently apply to specific energy technologies. This discrepancy can likely be attributed to varying implementation effects and contextual situations associated with the technologies. The findings suggest that personalized communication strategies for specific social groups may be beneficial for understanding and addressing public opinions, needs, and concerns within the energy transition. The complexity of understanding public opinion in the context of green-energy highlights the need for a nuanced approach in future research.

Performance of Machine Learning Models in Predicting Sentiments of Post-Covid Patients

With the widespread use of social media platforms, sentiment analysis of user-generated content has become a crucial task in understanding public opinion and trends. In this paper, we compare the performance of three popular machine learning models, namely Random Forest, Support Vector Machine (SVM), and Logistic Regression, in predicting sentiments of post-COVID patients on social media tweets. The study utilizes a dataset of labeled tweets representing positive, negative, and neutral sentiments. The preprocessing of textual data involves tokenization, stop-word removal, and conversion to lowercase to create a suitable input for the models. We utilize Term Frequency-Inverse Document Frequency (TF-IDF) vectorization to transform the text data into numerical features. The sentiment labels are converted to numeric representations for model training and evaluation. The three machine learning models are trained and evaluated on the dataset using metrics such as accuracy, precision, recall and F1-score. The evaluation results are presented and analyzed for each model, providing insights into their strengths and weaknesses in predicting sentiments. The experimental results demonstrate that Random Forest achieves the highest accuracy and F1-score, closely followed by SVM, while Logistic Regression performs slightly lower in comparison. However, all three models exhibit strong predictive capabilities, and their performances vary depending on the specific sentiment class. The findings provide valuable information for researchers and practitioners seeking to employ sentiment analysis in social media monitoring and other related applications. Overall, this study contributes to the understanding of the capabilities of Random Forest, SVM, and Logistic Regression models in sentiment analysis of social media tweets, and offers valuable insights for selecting the most suitable model for specific sentiment prediction tasks.

Spatial-Temporal Aware Disaster Topic Recognition in Social Media Text Using SageMaker

With the popularization and widespread use of social media, a large amount of information is shared and disseminated by users on the platforms, including disaster-related text messages. The disaster information contained in these social media text messages is of great value for emergency response, disaster research, and the understanding of public opinion. In the task of text classification of social media disaster topics, previous approaches generally only semantically analyze the text itself to determine its relevance to the disaster, and this paper considers a new perspective that combines the spatio-temporal attributes of social media texts and textual information to complete the text classification task. Based on the above idea, this paper constructs a heterogeneous graph on the dataset using the temporal and spatial attributes of blog posts, initializes the blog nodes using BERT features, and learns the blog features through relational graph convolution operation. Experiments on the SageMaker platform use BERT as a baseline model, while blog post features are initialized using this model. The experimental results show that the spatio-temporal model achieves better performance compared to the baseline model.

How politicians learn about public opinion

Politicians learning about public opinion and responding to their resulting perceptions is one key way via which responsive policy-making comes about. Despite the strong normative importance of politicians’ understanding of public opinion, empirical evidence on how politicians learn about these opinions in the first place is scant. Drawing on survey data collected from almost 900 incumbent politicians in five countries, this study presents unique descriptive evidence on which public opinion sources politicians deem most useful. The findings show that politicians deem direct citizen contact and information from traditional news media as the most useful sources of public opinion information, while social media cues and polls are considered much less useful. These findings matter for substantive representation, and for citizens’ feeling of being represented.

Twitter Data Sentiment Analysis

Abstract: Twitter sentiment analysis has gained significant attention in recent years due to the vast amount of data generated on social media platforms and the potential applications of sentiment analysis in various domains. This paper presents an overview of Twitter sentiment analysis, including its applications, challenges, and state-of-the-art techniques. We discuss the various methods and algorithms used for sentiment analysis and highlight their advantages and limitations. We also examine the ethical considerations associated with sentiment analysis and the need for responsible and transparent use of this technique. Through a comprehensive analysis of the existing literature, we explore the potential of Twitter sentiment analysis for understanding public opinion and its implications for various domains, including marketing, politics, and social sciences. Finally, we present a case study to demonstrate the application of sentiment analysis in the context of real-world data. Our analysis shows that Twitter sentiment analysis has significant potential for understanding public opinion, tracking trends, and providing valuable insights into various domains. However, it also highlights the need for careful consideration of ethical concerns and the importance of responsible and transparent use of this technique.

Aspect-based Sentiment and Correlation-based Emotion Detection on Tweets for Understanding Public Opinion of Covid-19

Background: During the Covid-19 period, the government made policies dealing with it. Policies issued by the government invited public opinion as a form of public reaction to these policies. The easiest way to find out the public’s response is through Twitter’s social media. However, Twitter data have limitations. There is a mix between facts and personal opinions. It is necessary to distinguish between these. Opinions expressed by the public can be both positive and negative, so correlation is needed to link opinions and their emotions. Objective: This study discusses sentiment and emotion detection to understand public opinion accurately. Sentiment and emotion are analyzed using Pearson correlation to determine the correlation. Methods: The datasets were about public opinion of Covid-19 retrieved from Twitter. The data were annotated into sentiment and emotion using Pearson correlation. After the annotation process, the data were preprocessed. Afterward, single model classification was carried out using machine learning methods (Support Vector Machine, Random Forest, Naïve Bayes) and deep learning method (Bidirectional Encoder Representation from Transformers). The classification process was focused on accuracy and F1-score evaluation. Results: There were three scenarios for determining sentiment and emotion, namely the factor of aspect-based and correlation-based, without those factors, and aspect-based sentiment only. The scenario using the two aforementioned factors obtained an accuracy value of 97%, while an accuracy of 96% was acquired without them. Conclusion: The use of aspect and correlation with Pearson correlation has helped better understand public opinion regarding sentiment and emotion more accurately. Keywords: Aspect-based sentiment, Deep learning, Emotion detection, Machine learning, Pearson correlation, Public opinion.

Analysis of Recognition of Twitter Posts using a Hybrid Machine Learning Approach

Sentiment analysis of tweets has become a crucial task in understanding public opinion, sentiment trends, and brand perception. With the exponential growth of social media data, efficient and accurate sentiment analysis methods are essential. This research paper presents a comprehensive study on the design, simulation, and assessment of sentiment analysis of tweets using an improved machine learning methodology. The proposed methodology combines the strengths of natural language processing techniques and advanced machine learning algorithms to achieve superior performance in sentiment classificationIn the introduction, the paper outlines the significance of sentiment analysis and its applications in various domains. It discusses the challenges faced by traditional sentiment analysis approaches and highlights the need for more robust and accurate methodologies. The paper then delves into related works, exploring the existing state-of-the-art techniques and identifying gaps that the proposed methodology aims to address.The proposed methodology section details the steps involved in the sentiment analysis pipeline. It begins with data preprocessing, including tokenization, stop-word removal, and stemming. Feature extraction methods, such as TF-IDF and word embeddings, are explored and compared. Next, the paper presents an improved machine learning algorithm that combines ensemble learning and deep learning techniques. The model's architecture and training process are elaborated, along with the parameter tuning strategies to optimize performance.The results indicate that the proposed methodology outperforms traditional sentiment analysis approaches, achieving higher accuracy and robustness in sentiment classification. The paper highlights the model's strengths in handling sarcasm, irony, and context-specific language, which are common challenges in sentiment analysis. Furthermore, the efficiency of the proposed methodology is demonstrated through its ability to handle large-scale datasets in real-time. In conclusion, this research paper emphasizes the importance of sentiment analysis in understanding public opinion and its role in decision-making processes for businesses and governments. The proposed methodology exhibits promising results, making it a viable solution for sentiment analysis of tweets and other social media data. The study concludes by suggesting future research directions to further enhance sentiment analysis techniques and address emerging challenges in the field.

Optimizing sentiment analysis of Nigerian 2023 presidential election using two-stage residual long short term memory

Sentiment analysis is the process of recognizing positive or negative attitudes in text. This technique makes use of computational linguistics, text analysis, and natural language processing. The 2023 presidential election in Nigeria is a significant event for the country, as it will determine the leader of the nation for the next four years. As such, it is important to understand the sentiment of the public towards the different candidates. In this research, we aimed to understand the sentiment of the public towards the three main candidates in the 2023 presidential election in Nigeria, Atiku, Tinubu, and Obi, by conducting a sentiment analysis on tweets related to the candidates. We used the long short-term memory (LSTM), peephole long short term memory (PLSTM), and two-stage residual long short-term memory (TSRLSTM) models to classify tweets as positive, neutral, or negative. Our dataset consisted of a large number of tweets that were preprocessed to remove noise and irrelevant information. Results showed that TSRLSTM performed excellently well in classifying the tweets and in identifying the sentiment towards each candidate individually. Our findings provide valuable insights into the public's opinion on the candidates and their campaign strategies, which can be useful for researchers, political analysts, and decision-makers. Our study highlights the importance of sentiment analysis in understanding public opinion and its potential applications in the field of political science.

Understanding Public Opinion Toward the #StopAsianHate Movement and the Relation With Racially Motivated Hate Crimes in the US

#StopAsianHate and #StopAAPIHate are two of the most commonly used hashtags that represent the current movement to end hate crimes against the Asian American and Pacific Islander communities. We conduct a social media study of public opinion toward the #StopAsianHate and #StopAAPIHate movement based on 46 058 Twitter users across 30 states in USA ranging from March 18, 2021 to April 11, 2021. To facilitate fine-grained analyses, the demographic information of the Twitter users, including age, gender, race/ethnicity, social capital, political affiliation, geolocation, family, income, and religious status, is either retrieved from the user profiles or inferred using classifiers. We find that the movement attracts more participation from women, younger adults, Asian, and Black communities. It is noteworthy that most of them are also active in other online movements related to racial or social issues, such as #BlackLivesMatter or #SayHerName; 51.56% of the Twitter users show direct support, 18.38% are news about anti-Asian hate crimes, and 5.43% show a negative attitude toward the movement. By conducting logistic regression, we find that the public opinion varies across user characteristics. Furthermore, among the states with most racial bias-motivated hate crimes, the negative attitude toward the #StopAsianHate and #StopAAPIHate movement is the weakest. The majority of the top influencers are Asian–American reporters, journalists, or politicians. To the best of our knowledge, this is the first large-scale social media-based study to understand public opinion toward the #StopAsianHate and #StopAAPIHate movement. We hope that our study can provide insights and promote research on anti-Asian hate crimes and ultimately help address such a serious societal issue for the common benefits of all communities.

Feasible Sentiment Analysis of Real Time Twitter Data

Sentiment analysis plays a significant role in understanding public opinion, trends, and sentiments expressed on social media platforms. In this paper, we focus on performing sentiment analysis on real-time Twitter data to gain insights into the sentiments related to specific topics or events, we collect a stream of tweets based on predefined keywords or hashtags. The collected tweets undergo pre-processing steps to clean and standardize the text for sentiment analysis. We employ machine learning classify the sentiments expressed in tweets, utilizing sentiment lexicons and training data as references. Real-time sentiment analysis is performed as new tweets are collected, enabling continuous monitoring and analysis of public sentiment. The sentiment analysis results are visualized through informative visualizations such as sentiment distribution charts and sentiment trends over time. Additionally, we focus on topic-specific analysis by filtering tweets based on relevant keywords or hashtags, providing deeper insights into sentiments related to specific subjects. The paper faces challenges such as noisy and informal text, ambiguity in sentiment expression, and handling large volumes of real-time data. Addressing these challenges, we aim to develop an effective sentiment analysis system that provides valuable insights into public sentiment and supports decision-making processes in various domains.

Социальное доверие и недоверие в сфере российского здравоохранения в период пандемии COVID-19: теоретико-методологические подходы и источники негативизации

Introduction. The article discusses theoretical approaches and methodological limitations of sociological research of trust and distrust to the healthcare services in the framework of the COVID-19 pandemic. The main difficulties are caused by the stable cognitive fashion for the quantitative measurement of trust/distrust indices. It leads to the illusion of understanding public opinion and social moods to this issue. The cognitive framework is also associated with the fundamental evaluative opposition of society's trust to medical care as a blessing and distrust as a socio-cultural barrier to the development of the healthcare system. Methods. The study is based on the methodology of sociological constructivism. Such approach makes it possible to study both mental and institutional factors that shape the crisis of public confidence to the health care services. Results and its discussion. The authors argue that social trust is constructed as a result of activity and requires directed efforts, an active position of key agents. Taking into account the neoliberal transformation of the healthcare institution in modern Russia, behavioral indicators of trust should be supplemented with information about objective indicators of the accessibility and quality of medical care. Based on empirical data, the dynamics of public opinion on the effectiveness of the Russian healthcare system and its main problems in a complex epidemiological situation are analyzed. The leading sources of negative social attitudes to the healthcare system during the COVID-19 pandemic are the following: aggressive policy of media; political decisions which are not always consistent and justified; claims for social control, external intervention in private space and strategies of medical activity in the society. The authors came to the conclusion that the COVID-19 pandemic only changed the structure of social attitude to the health care services, escalated chronic difficulties, but did not affect significantly the downward vector of consumer confidence in the health care system. The conclusion is made about the need for both systemic regulation of institutional mechanisms of trust, and increasing the reserve of personal responsibility and adaptive potential of consumers of medical care. In this regard, the relevance of sociological research in public health and healthcare will only increase in the post-coronavirus period. The synthesis of quantitative, qualitative assessments of trust and objective indicators of the availability and quality of medical care will make it possible to explain the dynamics of ideas, values and attitudes that shape the background of the social mood; to predict alternative scenarios and vectors for the transformation of the healthcare institution, depending on the semantic content of social trust / distrust to medical organizations.

Eyes on the Streets: Media Use and Public Opinion About Facial Recognition Technology

This study examines how different forms of media use predict attitudes toward the development of facial recognition technology (FRT) and applications of it by law enforcement to identify criminal suspects, identify potential terrorists, and monitor public protests. The theoretical framework builds on theories of cultivation and genre-specific viewing to develop hypotheses and research questions. The analyses draw on original data from two nationally representative surveys of the U.S. public conducted in 2020, amid a series of controversies and protests about policing and racial justice. The results demonstrate that overall television viewing and crime media viewing predicted support for multiple uses of FRT, while Fox News viewing predicted support for using FRT to monitor protests. The findings advance our understanding of public opinion toward the technology and its implications for policing, protests, and social justice.