Search Results (63)

Numerous valuable information is available on the Internet, and many individuals rely on mass media as their primary source of information. Various views, comments, expressions, and opinions on social networks have been a tremendous source of information. Harvesting free, resourceful information through social media makes text mining a powerful tool for analyzing public opinions on various issues across diverse social networks. Various research projects have implemented text sentiment analysis through machine and deep learning approaches. Social media text often expresses sentiment through complex syntax and negation (e.g., implicit and double negation and nested clauses), which many classifiers mishandle. We propose hybrid negation, a clause-aware approach that combines (i) explicit/implicit/double-negation rules, (ii) dependency-based scope detection, (iii) a TextBlob back-off for phrase polarity, and (iv) an MLP-learned clause-weighting module that aggregates clause-level scores. Across 156,539 tweets (three-class sentiment), we evaluate six negation strategies and 228 model configurations with and without SMOTE (applied strictly within training folds). Hybrid Negation achieves 98.582% accuracy, 98.196% precision, 98.189% recall, and 98.193% F1 with BERT, outperforming rule-only and antonym/synonym baselines. Ablations show each component contributes to the model’s performance, with dependency scope and double negations offering the largest gains. Per-class results, confidence intervals, and paired tests with multiple-comparison control confirm statistically significant improvements. We release code and preprocessing scripts to support reproducibility. Full article

The rise in social media has transformed Online Travel Agencies (OTAs) into platforms where users actively share their experiences and opinions. However, conventional opinion mining approaches often fail to capture nuanced emotional expressions or connect them to user influence. To address this gap, this study introduces an ontology-driven opinion mining framework that integrates multi-class emotion classification, aspect-based analysis, and influence modeling using Indonesian-language discussions from the social media platform X. The framework combines an OTA-specific ontology that formally represents service aspects such as booking support, financial, platform experience, and event with fine-tuned IndoBERT for emotion recognition and sentiment polarity detection, and Social Network Analysis (SNA) enhanced by entropy weighting and TOPSIS to quantify and rank user influence. The results show that the fine-tuned IndoBERT performs strongly with respect to identification and sentiment polarity detection, with moderate results for multi-class emotion classification. Emotion labels enrich the ontology by linking user opinions to their affective context, enabling the deeper interpretation of customer experiences and service-related issues. The influence analysis further reveals that structural network properties, particularly betweenness, closeness, and eigenvector centrality, serve as the primary determinants of user influence, while engagement indicators act as discriminative amplifiers that highlight users whose content attains high visibility. Overall, the proposed framework offers a comprehensive and interpretable approach to understanding public perception in Indonesian-language OTA discussions. It advances opinion mining for low-resource languages by bridging semantic ontology modeling, emotional understanding, and influence analysis, while providing practical insights for OTAs to enhance service responsiveness, manage emotional engagement, and strengthen digital communication strategies. Full article

Urban infrastructure planning is central to advancing sustainable cities, but project success increasingly depends on public acceptance as well as technical, economic, and environmental performance. This study develops a fuzzy–grey multi-criteria decision-making (MCDM) framework that embeds public opinion as a formal evaluation dimension. A novel POI, derived from online discourse data, integrates multi-dimensional emotions, polarization, and participation intensity to capture societal legitimacy. The framework employs entropy weighting and applies three established MCDM methods: TOPSIS, VIKOR, and EDAS, to evaluate project alternatives under uncertainty and incomplete information. An empirical case study in Nanjing demonstrates that incorporating Public Opinion Index (POI) significantly alters decision outcomes: the ecological park gained priority due to strong public support, while the wastewater treatment plant declined in ranking despite environmental benefits. These results underscore the decisive role of societal legitimacy in shaping sustainable infrastructure decisions. The framework contributes to sustainable urban planning by providing a replicable tool for balancing technical feasibility, environmental responsibility, and social acceptance in future infrastructure projects. Full article

In this paper, we introduce two novel, publicly available datasets that capture public opinion on two highly salient and timely topics: the perceived feasibility of artificial general intelligence (AGI) and the Hamas–Israel conflict (HIC). Collected from social media posts on X(formerly Twitter), the datasets were curated and annotated using a structured methodology designed to ensure robustness, consistency, and interpretability. Whereas prior research on public opinion has primarily relied on theoretical models, controlled experiments, or narrowly scoped datasets, such approaches often fail to capture the complexity and dynamism of real-world discourse. In contrast, the datasets presented in this work provide temporally fine-grained, large-scale empirical data that reflect the evolving nature of public sentiment on contemporary global issues. In addition to detailed sentiment annotations, these resources support longitudinal analyses of opinion dynamics, offering a foundation for empirical studies on consensus formation, polarization, and social influence in digital environments. By releasing these datasets, we aim to advance the empirical study of public opinion in the age of social media. Full article

School dropout and the recruitment of minors by criminal organizations have become deeply intertwined phenomena in Ecuador, particularly in territories marked by extreme violence and institutional fragility. This study investigates how Ecuadorian national media construct and frame these issues in 2025, using a qualitative content analysis of 85 opinion columns, editorials and analytical pieces published in leading outlets including El Comercio, El Universo, La Hora, Primicias, GK, Vistazo and Mercurio. Through a critical analysis of discursive patterns, the study identifies dominant narratives that reflect the normalization of violence, the erosion of schools as protective spaces, polarized portrayals of youth as victims or delinquents and a general critique of state inaction. Media narratives were found to vary ideologically, with some reinforcing stigma while others advocated for structural reform and rights-based approaches. The results highlight the role of media in shaping public understanding of educational exclusion and juvenile vulnerability in contexts of conflict. This research concludes that while Ecuadorian media serve as both mirrors and mediators of social crisis, their potential to influence educational policy and child protection efforts remains uneven. A more inclusive, critical and community-oriented media discourse is needed to confront the challenges of educational abandonment and youth recruitment. Full article

Despite the growing impacts of climate change worldwide, achieving consensus on climate action remains a challenge partly because of heterogeneity in perceptions of climate risks within and across countries. Lack of consensus has hindered global collective action. We use a system dynamics approach to examine how interactions among cultural, socio-political, psychological, and institutional factors shape public support or opposition for climate mitigation policy. We investigate the conditions under which the dominant public opinion about climate policy can shift within a 20-year time frame. We observed opinion shifts in 20% of simulations, primarily in individualistic cultural contexts with high perceived climate risk. Changing the dominant opinion was especially difficult to achieve in collectivistic cultures, as we observed no shifts in dominant opinion within the parameter ranges examined. Our study underscores the importance of understanding how cultural context mediates the approaches needed to effectively mobilize collective climate action. Full article

The rapid rise of social media has accelerated the evolution of public opinion, leading to frequent group polarization. Meanwhile, advancements in information science have enabled large-scale experiments, positioning dynamics as a crucial perspective for studying group polarization. This paper systematically reviews group polarization from a dynamics perspective. First, we outline its definitions and its explanatory theories. Then, we examine the role of dynamics in polarization research, summarize the current measurement methods of group polarization, and analyze intervention strategies based on elements of dynamics. Finally, we propose a logical framework for dynamics-based interventions. Our findings indicate that while research on group polarization from a dynamics perspective is relatively comprehensive, most intervention studies remain at the simulation level, requiring further validation for real-world applicability. This review provides a systematic overview of group polarization through a dynamics lens, offering insights for addressing challenges in network governance within the social media era. Full article

Existing approaches to social media sentiment analysis typically focus on static classification, offering limited foresight into how public opinion evolves. This study addresses that gap by introducing the Multi-Feature Sentiment-Driven Forecasting (MFSF) framework, a novel pipeline that enhances sentiment trend prediction by integrating rich contextual information from text. Using state-of-the-art transformer models on the Sentiment140 dataset, our framework extracts three concurrent signals from each tweet: sentiment polarity, aspect-based scores (e.g., ‘price’ and ‘service’), and topic embeddings. These features are aggregated into a daily multivariate time series. We then employ a SARIMAX model to forecast future sentiment, using the extracted aspect and topic data as predictive exogenous variables. Our results, validated on the historical Sentiment140 Twitter dataset, demonstrate the framework’s superior performance. The proposed multivariate model achieved a 26.6% improvement in forecasting accuracy (RMSE) over a traditional univariate ARIMA baseline. The analysis confirmed that conversational aspects like ‘service’ and ‘quality’ are statistically significant predictors of future sentiment. By leveraging the contextual drivers of conversation, the MFSF framework provides a more accurate and interpretable tool for businesses and policymakers to proactively monitor and anticipate shifts in public opinion. Full article

In order to address the pressing challenge posed by the proliferation of fake news in the digital age, we emphasize its profound and harmful impact on societal structures, including the misguidance of public opinion, the erosion of social trust, and the exacerbation of social polarization. Current fake news detection methods are largely limited to superficial text analysis or basic text–image integration, which face significant limitations in accurately identifying deceptive information. To bridge this gap, we propose the UC-CMAF framework, which comprehensively integrates news text, images, and user comments through an adaptive co-attention fusion mechanism. The UC-CMAF workflow consists of four key subprocesses: multimodal feature extraction, cross-modal adaptive collaborative attention fusion of news text and images, cross-modal attention fusion of user comments with news text and images, and finally, input of fusion features into a fake news detector. Specifically, we introduce multi-head cross-modal attention heatmaps and comment importance visualizations to provide interpretability support for the model’s predictions, revealing key semantic areas and user perspectives that influence judgments. Through the cross-modal adaptive collaborative attention mechanism, UC-CMAF achieves deep semantic alignment between news text and images and uses social signals from user comments to build an enhanced credibility evaluation path, offering a new paradigm for interpretable fake information detection. Experimental results demonstrate that UC-CMAF consistently outperforms 15 baseline models across two benchmark datasets, achieving F1 Scores of 0.894 and 0.909. These results validate the effectiveness of its adaptive cross-modal attention mechanism and the incorporation of user comments in enhancing both detection accuracy and interpretability. Full article

While most Spanish citizens recognize the urgency of climate change, opinions differ on the specific measures to mitigate it, which are grouped under the concept of ecological transition. The ecological transition policies put forward by states, parties, and political leaders have become a factor of political polarization, with the media—through their role as shapers of public discourse—playing a significant part. In this article, we examine the ecological transition from two perspectives. First, we explore the level of political polarization among Spanish society regarding how ecological transition is framed and how ecological transition measures are perceived. Second, we investigate how the media cover these measures and views to identify potential connections between their portrayal of the issue and the public’s perceptions. A two-pronged methodological approach is applied: a survey to assess citizens’ perceptions and a content analysis of four Spanish newspapers with diverse editorial leanings to evaluate the media treatment of the topic. Results show that left-wing respondents agree more with the idea that ecological transition is the solution to climate change, whereas right-wing respondents more frequently view it as a threat to current lifestyles and as a process imposed by governments. These results are also reflected in the press coverage of ecological transition. We conclude that the press emerges as an actor that reinforces the existing political polarization in society around ecological transition. Full article

The advent of generative artificial intelligence (GAI) technologies has significantly influenced the educational landscape. However, public perceptions and the underlying emotions toward artificial intelligence-generated content (AIGC) applications in education remain complex issues. To address this issue, this study employs LDA network public opinion topic mining and SnowNLP sentiment analysis to comprehensively analyze over 40,000 comments collected from multiple social media platforms in China. Through a detailed analysis of the data, this study examines the distribution of positive and negative emotions and identifies six topics. The study further utilizes visual tools such as word clouds and heatmaps to present the research findings. The results indicate that the emotional polarity across all topics is characterized by a predominance of positive emotions over negative ones. Moreover, an analysis of the keywords across the six topics reveals that each has its own emphasis, yet there are overlaps between them. Therefore, this study, through quantitative methods, also reflects the complex interconnections among the elements within the educational ecosystem. Additionally, this study integrates the six identified topics with the Technology–Organization–Environment (TOE) framework to explore the broad impact of AIGC on education from the perspectives of technology, organization, and environment. This research provides a novel perspective on the emotional attitudes and key concerns of the Chinese public regarding the use of AIGC in education. Full article

Studying public sentiment during crises is crucial for understanding how opinions and sentiments shift, resulting in polarized societies. We study Weibo, the most popular microblogging site in China, using posts made during the outbreak of the COVID-19 crisis. The study period includes the pre-COVID-19 stage, the outbreak stage, and the early stage of epidemic prevention. We use Llama 3 8B, a large language model, to analyze users’ sentiments on the platform by classifying them into positive, negative, sarcastic, and neutral categories. Analyzing sentiment shifts on Weibo provides insights into how social events and government actions influence public opinion. This study contributes to understanding the dynamics of social sentiments during health crises, fulfilling a gap in sentiment analysis for Chinese platforms. By examining these dynamics, we aim to offer valuable perspectives on digital communication’s role in shaping society’s responses during unprecedented global challenges. Full article

This study investigates how born-digital memes about high-profile events can serve as rich archival resources for understanding contemporary cultural phenomena and public sentiment by using a linked-data framework. Using a mixed-method approach, this study analyzes memes from a high-profile trial through web scraping and linked-data structures to map themes, sentiments, and cultural references. The linked-data frame includes data collection and integration, semantic web technologies, ontology development, and API data access. The findings point to dominant narratives and shifting sentiment, which further illustrate how such memes reflect and contribute to the polarization of the societal discourse concerning the event. This research is relevant for understanding digital culture, exploring the archival potential of born-digital materials, and assessing the dynamics of public opinion in widely publicized cases. By showing the efficiency of linked data methodologies in the analysis of born-digital discourse, we add valuable insights to both digital humanities and social sciences, offering a new approach of studying ephemeral online content as cultural artifacts. Full article

This paper applies the concept of symmetry to the design of a research methodology for public opinion evolution, emphasizing that both the construction and analysis processes of the method embody symmetrical principles. In today’s information age, dominated by social media, online platforms have become crucial venues for information dissemination. While the free flow of information promotes public participation, it also introduces certain challenges. Therefore, analyzing the evolution of public opinion and extracting public sentiment holds significant practical value for managing online public sentiment. This study takes the Zibo barbecue incident as a case study, utilizing the two-dimensional theory of emotion and Top2Vec for thematic analysis of public opinion comments. By combining sentiment dictionary methods with the RoBERTa model, we conduct a sentiment polarity analysis of public opinion comments. The results show that the RoBERTa model achieved an accuracy of 98.46% on the test set. The proposed method effectively uncovers public sentiment biases and the influencing factors on public emotions during the evolution of public opinion events, providing a more comprehensive understanding of the emotional dynamics throughout the development of public sentiment. This deeper insight aids in addressing issues related to public opinion more effectively. Full article

As the issues of the world’s overreliance on fossil fuels still remain unresolved, mainstream media play a central role in influencing public attitudes towards energy sources. This article aimed to consider Aristotle’s rhetorical appeals as manipulation means in the news coverage of the 2021–2022 global energy crisis. Using computer-aided text analysis, media framing, discourse, and rhetorical analyses, this paper analyzes 600 news articles published on the websites of the four mainstream media sources from the key countries that were affected by the crisis. The results confirmed emotionalization of the news coverage that occurred through the use of similar rhetorical appeals and emotive language means aimed at inducing positive or negative feelings and shaping public opinion. The UK and US mainstream media appeared to rely more on the opinions of political, business, and energy authorities, highlighting a high level of politicization of their coverage. The findings also indicated polarization of the attitudes in the coverage, with mainly negative narratives about fossil fuels and more positive narratives about renewable energies, which contributed to public opinion manipulation and energy decision-making. This study opens up perspectives for future research on media emotions and rhetorical appeals as powerful manipulation means in applied linguistics, rhetoric, and journalism. Full article