Temporal dynamics of public engagement and sentiment in AI-Enabled mathematics education from YouTube, Twitter and TikTok comments

This study examines public sentiment toward Indonesia’s Constitutional Court (Mahkamah Konstitusi, MK) following its 2024 regional election ruling. Using sentiment analysis and Martin and White’s (2005) Appraisal Theory, the research investigates emotional, evaluative, and dialogic patterns in public discourse through YouTube comments. A mixed-method approach was adopted by combining qualitative appraisal interpretation with quantitative categorization and machine learning-based sentiment classification, all conducted using the Orange data mining application. Orange was chosen for its visual programming interface, ease of integration between linguistic theory and machine learning workflows, and accessibility for researchers working across disciplines. From 4,010 YouTube comments, 223 relevant entries were filtered and analysed according to the three domains of Appraisal Theory: Attitude (affect, judgment, appreciation), Engagement (monogloss and heterogloss), and Graduation (force and focus), enabling a structured evaluation of public responses. Three machine learning models were employed for sentiment classification within Orange: Naive Bayes, for its speed and efficiency in text classification; Logistic Regression, for its interpretability and robust baseline performance; and Neural Network, for its ability to capture nuanced emotional expressions. Among these, the Neural Network achieved the highest performance (AUC: 0.958; F1 score: 0.853), followed by Logistic Regression (AUC: 0.931; F1: 0.807), and Naive Bayes (AUC: 0.925; F1: 0.802). Each model offered distinct strengths: Neural Network revealed deeper emotional intensity, Logistic Regression emphasized positive affect, and Naive Bayes captured dominant monoglossic tendencies in discourse. The findings reveal a predominance of neutral and moderately positive sentiments, with joy, fear, surprise, and dissatisfaction emerging as key affective responses. The integration of Appraisal Theory and sentiment modeling through Orange demonstrates a systematic and scalable method for interpreting public discourse in digital environments. This research contributes methodologically by bridging qualitative linguistic analysis with accessible data mining tools, and substantively by offering insight into how digital publics engage with constitutional authority. It advances the literature on institutional trust by illustrating how social media serves as a platform for democratic evaluations of judicial decisions.

Purpose The purpose of this research was to use develop a sentiment model using machine learning algorithms for discerning public response about the misinformation correction practices of Muslim clerics on YouTube. Method This study employed three machine learning algorithms, Naïve Bayes, SVM, and a Balanced Random Forest to build a sentiment model that can detect Muslim sentiment about Muslim clerics’ anti-misinformation campaign on YouTube. Overall, 9701 comments were collected. An LDA-based topic model was also employed to understand the most expressed topics in the YouTube comments. Results The confusion matrix and accuracy score assessment revealed that the balanced random forest-based model demonstrated the best performance. Overall, the sentiment analysis discovered that 74 percent of the comments were negative, and 26 percent were positive. An LDA-based topic model also revealed the eight most discussed topics associated with ten keywords in those YouTube comments. Practical implications The sentiment and topic model from this study will particularly help public health professionals and researchers to better understand the nature of vaccine misinformation and hesitancy in the Muslim communities. Social implications This study offers the joint task force of Muslim clerics and medical professionals, and the future misinformation campaigns a sentiment detection model to understand public attitude to such practices on social media. Originality While the impact of misinformation on public sentiment and opinion on social media has been researched extensively, Muslim perspectives on combating misinformation have received less attention. This research is the first to evaluate responses towards Muslim clerics correcting religious vaccine misinformation using machine learning models.

Purpose The rise of conversational artificial intelligence (AI) bots such as ChatGPT highlights users’ anxieties and high expectations. This study aims to explore consumers’ views of AI conversational bots and examines their societal implications, emphasizing public perception as a fundamental factor in their acceptance and integration. Design/methodology/approach This study combines manual and automated thematic analysis to understand public sentiment by analyzing 45,844 YouTube comments. The comments are collected from the top five nonsponsored English-language YouTube videos on ChatGPT, with comments extracted using Octoparse. Key themes and their relationships are identified through manual coding and further analyzed using Leximancer to enhance the depth and accuracy of the analysis by detecting patterns in large data sets. Findings The analysis reveals three primary areas: empowerment through AI-enhanced capabilities, anxiety over AI-induced societal shifts and negotiating human–AI collaboration. Concerns are expressed about misinformation, privacy and the impact of AI on employment and human skills. Conversely, positive perceptions highlight AI’s role in education, personal productivity and medical diagnosis. These themes categorize public sentiment into techno-skepticism, techno-realism and techno-optimism, demonstrating the complex and diverse opinions on AI technology. Originality/value This research bridges AI’s technical aspects with its social and ethical dimensions, providing a comprehensive understanding of public sentiment towards ChatGPT. It underscores the importance of examining consumer views as a foundational step in understanding AI’s broader societal impacts.

The COVID-19 pandemic has been sweeping across the United States of America since early 2020. The whole world was waiting for vaccination to end this pandemic. Since the approval of the first vaccine by the U.S. CDC on 9 November 2020, nearly 67.5% of the US population have been fully vaccinated by 10 July 2022. While quite successful in controlling the spreading of COVID-19, there were voices against vaccines. Therefore, this research utilizes geo-tweets and Bayesian-based method to investigate public opinions towards vaccines based on (1) the spatiotemporal changes in public engagement and public sentiment; (2) how the public engagement and sentiment react to different vaccine-related topics; (3) how various races behave differently. We connected the phenomenon observed to real-time and historical events. We found that in general the public is positive towards COVID-19 vaccines. Public sentiment positivity went up as more people were vaccinated. Public sentiment on specific topics varied in different periods. African Americans’ sentiment toward vaccines was relatively lower than other races.

Mpox was declared a Public Health Emergency of International Concern by the World Health Organization in August 2024, following an outbreak in Africa. Public engagement on YouTube provides insights into public perceptions during such crises. We analyzed public discourse and sentiments related to Mpox, focusing on thematic trends in YouTube comments. A qualitative synthesis employing thematic content analysis of YouTube comments. The YouTube API retrieved 50 videos each for "Mpox" and "Monkeypox." After exclusions, 50 relevant videos remained, and the top 10 by views were analyzed. From 10,567 comments extracted, 2826 were analyzed using Latent Dirichlet Allocation modeling to identify themes. Key themes included geopolitical concerns, disease spread, conspiracy theories, public health measures, and religious interpretations. Comments revealed mixed views on vaccines, lockdowns, and mistrust in authorities. Effective health communication must address scientific, cultural, and geopolitical dimensions while countering misinformation and fostering trust.

This research aims to analyze public sentiment regarding the performance of the General Election Commission after the 2024 presidential election using the BERT (Bidirectional Encoder Representations from Transformers) model. Given the General Election Commission's crucial role in maintaining election integrity and the importance of transparency in Indonesian democracy, understanding public opinion through sentiment analysis is essential. Data was collected from YouTube comments, a platform increasingly popular for public expression. The analysis process began with data preprocessing, including case folding, text cleaning, tokenization, and stop word removal. The BERT model was then applied to classify the sentiment of the comments, with the model's performance evaluated using 10-fold cross-validation. The evaluation results showed that the first fold (k=1) achieved the best performance with an accuracy of 96%, precision of 96%, recall of 96%, and an F1-score of 96%, indicating the model's effectiveness in accurately classifying sentiment. In contrast, the ninth fold (k=9) exhibited the lowest accuracy at 86% with other metrics also lower, suggesting performance instability potentially caused by data variability. Accuracy and loss graphs confirmed that the first fold experienced consistent accuracy improvements and significant loss reduction, while the ninth fold showed performance fluctuations. This study provides valuable insights into public sentiment regarding the General Election Commission performance, with BERT demonstrating significant potential for sentiment analysis on social media platforms like YouTube.

The 2024 Presidential Election (Pilpres) in Indonesia is a significant event involving vice-presidential candidate debates (Cawapres). This study aims to analyze sentiment and comment networks on KompasTV's YouTube debate video using a Social Network Analysis (SNA) approach. Data were collected from YouTube comments, analyzed using Netlytic and Gephi to identify public sentiment and key actors. Results show that interactions between actors are relatively easy, with positive comments dominating. The word "Gibran" appears most frequently, indicating attention on one candidate. The user fachrurrozinoorkhomarudin1253 has the highest degree centrality, while Lena_Palma has the highest betweenness centrality, indicating significant influence within the network. Conclusion: The Cawapres debate generates active interactions on YouTube, with public sentiment tending to be positive.

This paper examines the integrated management of Geoparks through a holistic lens, emphasizing the delicate balance between preservation and utilization. The research analyzes three interconnected pillars: geological heritage conservation, scientific research, and educational outreach. Through case studies and empirical evidence, we validate that Geoparks serve as living laboratories for earth sciences, offering unique opportunities for deep time perception and immersive learning experiences in extraordinary contexts that need both preservation and study. The research explores different strategies for scientific investigation and educational engagement and highlights how these areas represent key witnesses of Earth's history and human impact, serving as natural archives that enhance our understanding of geological processes and their influence on human development. Special attention is given to the role of Geoparks in facilitating the comprehension of Earth's complex dynamics and the importance of preserving these outdoor classrooms for scientific research and education. The study also addresses emerging challenges, including climate change impacts and anthropogenic pressure, suggesting adaptive management solutions. This comprehensive approach ensures the longterm viability of Geoparks while maximizing their scientific, educational, and cultural benefits for future generations.

The COVID pandemic disrupted the schooling of students worldwide resulting in many having had a period of at-home learning. Many parents found themselves assuming responsibility for supporting their children’s at-home learning. Parents often find it difficult to support their children’s mathematics learning compared with other curriculum areas. There has been limited research exploring parental engagement in mathematics education generally, and little into parental engagement in mathematics education during the COVID pandemic. This paper examines how parents supported their child’s mathematics education during the school closures and identifies the factors that impacted this engagement. The Ecologies of Parental Engagement (EPE) model was used to help describe the engagement of different parents in mathematics education during the school closures and to examine the way the home space and available capital shaped parental engagement. Eight parents were selected from a larger Australian study that explored the impact of the pandemic-induced period of at-home schooling on primary school mathematics and science. One-on-one narrative interviews were conducted online with participants. Analysis identified three categories of parental engagement: monitors, facilitators, and enhancers. Parents in each category responded to their role in at-home learning differently, and accessed and activated different capital to support their child’s at-home learning in mathematics during the pandemic. Results highlight the value of emotional capital, as well as knowledge of mathematics and mathematics education, with implications for schools hoping to engage parents in mathematics learning. The study offers a typology to be explored in future research concerning parental engagement in mathematics education.

Emotionality is a well-established strategy for boosting audience engagement on social media. While fact-checking is positioned to provide objective information, fact-checking posts on social media often involve heightened emotionality. How much emotionality is present and how emotionality influences audience engagement and public sentiment toward fact-checked targets remain largely understudied. Informed by social psychological frameworks explicating message-level factors influencing public engagement and sentiment, the present study examines emotionality in 49,270 fact-checking posts created by 10 United States fact-checking organizations on Facebook from 2017 to 2022. Results showed that emotionality in fact-checking posts significantly increased by 13.5% over the years. Editorial fact-checkers (e.g., Washington Post) used higher levels of emotionality than independent fact-checkers (e.g., snopes.com). Emotionality positively indicated public engagement as predicted. However, in both fact-checked true and false information, emotionality was negatively associated with the public’s sentiment toward fact-checked targets, suggesting a potential spillover effect on stories verified to be true. This study reveals that emotionality in fact-checking posts boosts social media engagement yet with the potential of compromising fact-checking effectiveness.

Introduction: In this study, it was aimed to examine the expectations of elementary and secondary school parents from the mathematics education and their engagements in the education and the mathematics homework habits of their children. Methods: The research data was collected by “A Scale to Determine Parents’ Expectation from Mathematics Education”, “Mathematics Homework Behavior Scale” and the “Personal Information Form” prepared by the researcher. The data of this study executed and conducted by survey model was analyzed by SPSS 16. Results: In the study, it is revealed that the expectations of parents from Mathematics education and the mathematics homework habit of their children are high. There is no difference based on the levels of the children and parentage status of the parents, regardless of being mother or father, the mathematics homework habit of the children who favor mathematics lesson and at the same time thrive on is more favorable and positive than the ones who do not favor mathematics lesson and at the same time fail to thrive on, the homework habit of the children whom are supported sufficiently in the mathematics lesson is more favorable and positive. Last but not least, there is no correlation between the expectations of the parents from Mathematics education and the homework habits of their children. Discussion: High expectations of parents from mathematics classes may suggest that they trust their children and their teachers. It may also suggest that they are involved in the education process and that they find it sufficient. Based on the findings of this study, according to which the level of homework habits of the parents’ children is high, it can be assumed that the students do their homework willingly and they have no problems with doing their daily homework. Parents’ help their children’s with homework occasionally to make them feel that they are not alone in this process. Lower expectations from their children and lower engagement of parents at upper levels may be caused by the fact that they cannot support their children sufficiently due to the complexity of subjects. In elementary schools, since their children are smaller in terms of age, parents may think that their children need more help and they can be more active in education because the subjects in elementary school are not as complicated as in higher classes. The math homework habits do not differ according to the education level of students’ but, based on the scores, we can say that they are more favourable in the elementary school since the children are younger and besides, in Turkey, children are assigned homework more regularly and the homework habits start to emerge at the elementary school level. Just depending on the scores, it is interesting to note that the expectations of fathers from mathematics education and their engagement in the process are higher than those of mothers. This may suggest that the expectations of fathers from their children may be due to the higher goals they set for them and perhaps since they are more perfectionist, they are more involved in the children’s education than mothers. To like a lesson, can be considered a precondition for doing the assigned homework more willingly. Children do their homework more willingly in the courses at which they consider themselves successful. That is why the results of this study are not surprising. The homework habits of the children sufficiently supported in mathematics are expected to be more favourable. The expectations of parents from mathematics lesson were not related to their children’s homework habits. The absence of homework habits, in the parents’ expectation from mathematics lesson, may be due to not getting a clear answer from the parents with respect to the question whether homework should be assigned in education or not. Limitations: These research and data collection tools used are limited only by the thoughts of parents of primary and middle school students in Turkey. Conclusions: The child, being aware that he is not alone in the process, will be more confident if he knows that there is a family support behind him in overcoming mathematics.

Analog missions are integrated, multi-disciplinary activities that test key features of future human space exploration missions in an integrated fashion to gain a deeper understanding of system-level interactions and operations early in conceptual development. These tests often are conducted in remote and extreme environments that are representative in one or more ways to that of future spaceflight destinations. They may also be conducted at NASA facilities, using advanced modeling and human-in-the-loop scenarios. As NASA develops a capability driven framework to transport crew to a variety of space environments, it will use analog missions to gather requirements and develop the technologies necessary to ensure successful exploration beyond low Earth orbit. NASA s Advanced Exploration Systems (AES) Division conducts these high-fidelity integrated tests, including the coordination and execution of a robust education and public outreach (EPO) and engagement program for each mission. Conducting these mission scenarios in unique environments not only provides an opportunity to test the EPO concepts for the particular future-mission scenario, such as the best methods for conducting events with a communication time delay, but it also provides an avenue to deliver NASA s human space exploration key messages. These analogs are extremely exciting to students and the public, and they are performed in such a way that the public can feel like part of the mission. They also provide an opportunity for crew members to obtain training in education and public outreach activities similar to what they would perform in space. The analog EPO team is responsible for the coordination and execution of the events, the overall social media component for each mission, and public affairs events such as media visits and interviews. They also create new and exciting ways to engage the public, manage and create website content, coordinate video footage for missions, and coordinate and integrate each activity into the mission timeline. In 2012, the AES Analog Missions Project performed three distinct missions - NASA Extreme Environment Mission Operations (NEEMO), which simulated a mission to an asteroid using an undersea laboratory; In-Situ Resource Utilization (ISRU) Field Test, which simulated a robotic mission to the moon searching and drilling for water; and Research and Technology Studies (RATS) integrated tests, which also simulated a mission to an asteroid. This paper will discuss the education and public engagement that occurred during these missions.

BackgroundPolycystic ovary syndrome (PCOS) is the most common endocrinopathy in women, resulting in substantial burden related to metabolic, reproductive, and psychological complications. While attempts have been made to understand the themes and sentiments of the public regarding PCOS at the local and regional levels, no study has explored worldwide views, mainly due to financial and logistical limitations. YouTube is one of the largest sources of health-related information, where many visitors share their views as questions or comments. These can be used as a surrogate to understand the public’s perceptions.ObjectiveWe analyzed the comments of all videos related to PCOS published on YouTube from May 2011 to April 2023 and identified trends over time in the comments, their context, associated themes, gender-based differences, and underlying sentiments.MethodsAfter extracting all the comments using the YouTube application programming interface, we contextually studied the keywords and analyzed gender differences using the Benjamini-Hochberg procedure. We applied a multidimensional approach to analyzing the content via association mining using Mozdeh. We performed network analysis to study associated themes using the Fruchterman-Reingold algorithm and then manually screened the comments for content analysis. The sentiments associated with YouTube comments were analyzed using SentiStrength.ResultsA total of 85,872 comments from 940 PCOS videos on YouTube were extracted. We identified a specific gender for 13,106 comments. Of these, 1506 were matched to male users (11.5%), and 11,601 comments to female users (88.5%). Keywords including diagnosing PCOS, symptoms of PCOS, pills for PCOS (medication), and pregnancy were significantly associated with female users. Keywords such as herbal treatment, natural treatment, curing PCOS, and online searches were significantly associated with male users. The key themes associated with female users were symptoms of PCOS, positive personal experiences (themes such as helpful and love), negative personal experiences (fatigue and pain), motherhood (infertility and trying to conceive), self-diagnosis, and use of professional terminology detailing their journey. The key themes associated with male users were misinformation regarding the “cure” for PCOS, using natural and herbal remedies to cure PCOS, fake testimonies from spammers selling their courses and consultations, finding treatment for PCOS, and sharing perspectives of female family members. The overall average positive sentiment was 1.6651 (95% CI 1.6593-1.6709), and the average negative sentiment was 1.4742 (95% CI 1.4683-1.4802) with a net positive difference of 0.1909.ConclusionsThere may be a disparity in views on PCOS between women and men, with the latter associated with non–evidence-based approaches and misinformation. The improving sentiment noticed with YouTube comments may reflect better health care services. Prioritizing and promoting evidence-based care and disseminating pragmatic online coverage is warranted to improve public sentiment and limit misinformation spread.

Introduction Social media’s pervasive role in daily life provides a unique avenue for understanding public sentiments. This study utilizes YouTube comments on an MSNBC video about the Affordable Care Act (ACA) to explore public opinions on the policy and its political implications. Methods A qualitative analysis of YouTube comments on an MSNBC video was conducted. Netlytic, a scraping tool, gathered 1,949 comments. Thematic analysis, based on a coding framework derived from Semetko and Valkenburg’s frames, identified three predominant frames: Responsibility, Human Interest, and Economic Consequences. Results Of the 1949 comments posted in response to the ACA video on YouTube, 488 were analyzed. Three media frames were used to categorize the comments. Among the analyzed comments, 42% fell within the Responsibility Frame, indicating that 205 comments attributed responsibility for ACA-related issues, healthcare access, and costs to political parties, individual politicians, and government entities. The Human-Interest Frame accounted for 11% of the comments, with 53 remarks reflecting individual experiences related to healthcare access and costs. Additionally, 26% of the comments fell within the Economic Consequences Frame, signifying those 125 comments included remarks concerning the economic impacts of the ACA, such as increased healthcare access, taxes, and penalties. The remaining 21% of comments did not align with the three frames. Conclusions This study embarked on a comprehensive exploration of public discourse surrounding the Affordable Care Act (ACA) within the context of the 2024 presidential election, utilizing YouTube comments as a rich source of qualitative data. Through a meticulous methodological approach, which included thematic framing analysis and intercoder reliability assessments, valuable insights into the diverse perspectives and sentiments expressed by social media users regarding this critical healthcare legislation were gained. Key messages • Social media allows for real-time understanding of people’s thoughts, beliefs, opinions, and ideas, as well as serves as a useful tool that fosters broad, unfiltered data/content. • Researchers conducted a qualitative analysis of public discourse on YouTube to explore the range of sentiments regarding the ACA.

This study analyzes public sentiment toward Indonesia’s free school lunch policy using sentiment classification on YouTube comments. Data were collected from 5,640 videos, resulting in 485,097 comments, with 392,576 comments used for training and testing. The dataset was preprocessed through cleaning, tokenization, normalization, stopword removal, and stemming. Word2Vec was used for word embedding, and sentiment classification was performed using an LSTM neural network. The model achieved 82.56% accuracy on training data but 57.00% on manually labeled test data. The final sentiment distribution shows that negative sentiment slightly dominates, reflecting public skepticism about budget use and program effectiveness. Frequent keywords such as Indonesia, Prabowo, school, and corruption highlight key concerns. These results provide valuable insights for policymakers to improve communication and address public concerns. Future research should expand data sources, refine labeling, and test hybrid deep learning models to enhance classification performance.