Search Results (103)

Detecting fake news is essential in natural language processing to verify news authenticity and prevent misinformation-driven social, political, and economic disruptions targeting specific groups. A major challenge in multimodal fake news detection is effectively integrating textual and visual modalities, as semantic gaps and contextual variations between images and text complicate alignment, interpretation, and the detection of subtle or blatant inconsistencies. To enhance accuracy in fake news detection, this article introduces an ensemble-based framework that integrates textual and visual data using ViLBERT’s two-stream architecture, incorporates VADER sentiment analysis to detect emotional language, and uses Image–Text Contextual Similarity to identify mismatches between visual and textual elements. These features are processed through the Bi-GRU classifier, Transformer-XL, DistilBERT, and XLNet, combined via a stacked ensemble method with soft voting, culminating in a T5 metaclassifier that predicts the outcome for robustness. Results on the Fakeddit and Weibo benchmarking datasets show that our method outperforms state-of-the-art models, achieving up to 96% and 94% accuracy in fake news detection, respectively. This study highlights the necessity for advanced multimodal fake news detection systems to address the increasing complexity of misinformation and offers a promising solution. Full article

Social media serves as a vital channel for emotional expression, yet mandatory IP location disclosure raises concerns about how reducing anonymity affects users’ shared emotions, particularly in privacy-sensitive contexts such as mental health discussions. In 2022, all Chinese social media platforms implemented this disclosure feature. This study examines the emotional and behavioral consequences of Sina Weibo’s mandatory IP location disclosure policy, which took effect on 28 April 2022. We collected 193,761 Weibo posts published under the topic of depression from 1 March to 30 June 2022, and applied sentiment analysis combined with regression discontinuity in time (RDiT) to estimate causal effects around the policy threshold. Results indicate that the policy significantly intensified negative emotional expression: the estimated discontinuity is −1.3506 (p < 0.01), meaning posts became more negative immediately after implementation. In contrast, the effect on positive sentiment was comparatively weak and mostly statistically insignificant. Behavioral changes were also observed: both average daily posting volume and average text length are declined. These findings demonstrate that mandatory disclosure can suppress self-disclosure and amplify negative emotional tone in privacy-sensitive settings, offering practical guidance for users, platform designers, and policymakers on implementing transparency features responsibly. Full article

The dynamic evolution of collective emotions across the news dissemination life-cycle is a powerful yet underexplored signal in affective computing. While phenomena like the spread of fake news depend on eliciting specific emotional trajectories, existing methods often fail to capture these crucial dynamic affective cues. Many approaches focus on static text or propagation topology, limiting their robustness and failing to model the complete emotional life-cycle for applications such as assessing veracity. This paper introduces C-STEER (Cycle-aware Sentiment-Temporal Emotion Evolution), a novel framework grounded in communication theory, designed to model the characteristic initiation, burst, and decay stages of these emotional arcs. Guided by Diffusion of Innovations Theory, C-STEER first segments an information cascade into its life-cycle phases. It then operationalizes insights from Uses and Gratifications Theory and Emotional Contagion Theory to extract stage-specific emotional features and model their temporal dependencies using a Bidirectional Long Short-Term Memory (BiLSTM). To validate the framework’s descriptive and predictive power, we apply it to the challenging domain of fake news detection. Experiments on the Weibo21 and Twitter16 datasets demonstrate that modeling life-cycle emotion dynamics significantly improves detection performance, achieving F1-macro scores of 91.6% and 90.1%, respectively, outperforming state-of-the-art baselines by margins of 1.6% to 2.4%. This work validates the C-STEER framework as an effective approach for the computational modeling of collective emotion life-cycles. Full article

Considering the significant role that emergency infrastructure projects (EIPs) play globally in responding to emergency events, public sentiment towards EIPs has become an increasingly important factor to consider. However, limited studies have analysed the public’s sentiment specifically towards EIPs in emergency and urgent circumstances. This study analyses public sentiment characteristics by collecting objective big data from popular posts and comments related to EIPs on Sina Weibo. Sentiment information was extracted using text mining methods, and sentiment was measured using a long short-term memory (LSTM) model. Findings indicate that (1) Positive sentiment predominates in the data. (2) Public sentiment of temporary EIPs remains relatively stable, while long-term adaptive EIPs earn more pronounced sentiment fluctuation. (3) There are regional differences in public sentiment; Hebei, Shandong and Shanghai exhibit slightly lower stability with positive sentiment being slightly lower than or equal to neutral sentiment. The findings contribute to the literature by focusing innovatively on the public perspective of EIPs under urgent circumstances by exploring public sentiment characteristics and evolution and are of particular significance for related government departments and project managers in decision-making and construction management. Full article

With the rapid development of social networks, online news has gradually surpassed traditional paper media and become a main channel for information dissemination. However, the proliferation of fake news also poses a serious threat to individuals and society. Since online news often involves multimodal content such as text and images, multimodal fake news detection has become increasingly important. To address the challenges of feature extraction and cross-modal fusion in this task, this study presents a new multimodal fake news detection model. The model uses a GPT-style encoder to extract text semantic features, a ResNet backbone to extract image visual features, and dynamically captures correlations between modalities through a context-aware multimodal fusion module. In addition, a joint optimization strategy combining contrastive loss and cross-entropy loss is designed to enhance modal alignment and feature discrimination while optimizing classification performance. Experimental results on the Weibo and PHEME datasets show that the proposed model outperforms baseline methods in accuracy, precision, recall, and F1-score, effectively captures correlations between modalities, and improves the quality of feature representation and overall model performance. This study suggests that the proposed approach may serve as a useful approach for fake news detection on social platforms. Full article

The widespread dissemination of multimodal disinformation, which combines inflammatory text with manipulated images, poses a severe threat to society. Existing detection methods typically process textual and visual features in isolation or perform simple fusion, failing to capture the sophisticated semantic inconsistencies commonly found in false information. To address this, we propose a novel framework: Emotion-Topic Injection and Consistency Detection Network (ETICD-Net). First, a large language model (LLM) extracts structured sentiment and topic-guided signals from news texts to provide rich semantic clues. Second, unlike previous approaches, this guided signal is injected into the feature extraction processes of both modalities: it enhances text features from BERT and modulates image features from ResNet, thereby generating sentiment-topic-aware feature representations. Additionally, this paper introduces a hierarchical consistency fusion module that explicitly evaluates semantic coherence among these enhanced features. It employs cross-modal attention mechanisms, enabling text to query image regions relevant to its statements, and calculates explicit dissimilarity metrics to quantify inconsistencies. Extensive experiments on the Weibo and Twitter benchmark datasets demonstrate that ETICD-Net outperforms or matches state-of-the-art methods, achieving accuracy and F1 scores of 90.6% and 91.5%, respectively. Full article

Fake news may obscure the truth or mislead readers through subtle manipulations of textual content, such as modifying a few keywords or adjusting syntactic structures. Such local-level alterations are often difficult for detection models to capture, which undermines their overall performance. To address the limitations in processing fine-grained textual details, we propose a novel fake news detection framework—BCCU, which integrates a pre-trained language model, capsule networks, and a co-attention mechanism. Specifically, BCCU employs BERT to extract global semantic representations from news text, leverages Capsule Network to identify subtle local patterns, and synergistically fuses these two feature streams via a Co-Attention mechanism. Additionally, it incorporates User attributes as auxiliary features to further enhance detection accuracy. We evaluate the BCCU framework on three benchmark datasets—Twitter15, Twitter16, and Weibo—achieving accuracies of 0.864, 0.851, and 0.945, respectively, outperforming existing baseline models. The results demonstrate that by effectively combining global and local textual features and integrating user profile information, BCCU can robustly detect fake news even when relying solely on the unimodal text modality. Full article

Recent advances in Chinese Named Entity Recognition (CNER) have integrated lexical features and factual knowledge into pretrained language models. However, existing lexicon-based methods often inject knowledge as restricted, isolated token-level information, lacking rich semantic and structural context. Knowledge graphs (KGs), comprising relational triples, offer explicit relational semantics and reasoning capabilities, while Graph Convolutional Networks (GCNs) effectively capture complex sentence structures. We propose KGGCN, a unified KG-enhanced GCN framework for CNER. KGGCN introduces external factual knowledge without disrupting the original word order, employing a novel end-append serialization scheme and a visibility matrix to control interaction scope. The model further utilizes a two-phase GCN stack, combining a standard GCN for robust aggregation with a multi-head attention GCN for adaptive structural refinement, to capture multi-level structural information. Experiments on four Chinese benchmark datasets demonstrate KGGCN’s superior performance. It achieves the highest F1-scores on MSRA (95.96%) and Weibo (71.98%), surpassing previous bests by 0.26 and 1.18 percentage points, respectively. Additionally, KGGCN obtains the highest Recall on OntoNotes (84.28%) and MSRA (96.14%), and the highest Precision on MSRA (95.82%), Resume (96.40%), and Weibo (72.14%). These results highlight KGGCN’s effectiveness in leveraging structured knowledge and multi-phase graph modeling to enhance entity recognition accuracy and coverage across diverse Chinese texts. Full article

Research on coastal recreational activities has grown substantially, yet studies focusing on user perceptions of these spaces—critical for optimizing tourism experiences and management—remain fragmented and underdeveloped. This study addresses this gap by examining tourist sentiment in Xiamen, a renowned coastal city in China, using social media data. Text mining tools were utilized to process the Weibo contents through text segmentation, frequency analysis and cluster analysis. The Two-way Neural Network Fusion Model Based on the BERT (TNNFMB) deep learning approach was employed using transfer learning for sentiment analysis, while the Latent Dirichlet Allocation (LDA) model was used to uncover latent thematic patterns. Sentiment polarity analysis revealed that positive comments constituted 56.47%, negative comments only 16.3%, and neutral comments 27.2%, confirming a generally positive perception of visitors’ coastal experiences. Tourists’ social media posts primarily revolve around five core themes in coastal areas: coastal waters, waterfronts, adjacent environments, culture and creativity, and reputation and expectation. The spatial and temporal changes in sentiment scores were discovered. Areas emphasizing sea–land landscapes, cultural theme reinforcement, and open public activities generally achieved high and stable sentiment scores. Natural and natural–artificial mixed coastlines experienced significant seasonal variations in sentiment. The recommendations of this study, generated from a sentiment perspective, include shaping a harmonious coastal environment by improving coastal management and support services to enhance the comfort of the tourist experience. This study advances understanding of user-centric coastal tourism dynamics, providing evidence-based tools for managers to enhance tourist experiences and spatial quality. Full article

Cultural heritage museums, as integral components of the urban built environment and public cultural space, not only preserve historical memory but also subtly shape visitors’ psychological experiences and well-being. Yet the mechanisms linking museum environmental quality with visitor mental experiences remain insufficiently explored. Drawing on 10,684 visitor reviews collected from Dianping, Weibo, and Ctrip, this study applies text mining and semantic analysis to construct an evaluation framework of visitor behavioral preferences and psychological experiences in heritage museums. The findings show that attention to spatial remains, historical artifacts, and cultural symbols is closely associated with positive emotions such as mystery, awe, and beauty, while adverse environmental conditions such as queuing and crowding often trigger negative feelings including fatigue, disappointment, and boredom. Further analysis reveals a clear pathway linking objects, behaviors, and experiences: spatial remains evoke psychological resonance through immersive perceptions of authenticity; artifacts are primarily linked to visual pleasure and emotional comfort; and cultural symbols are transformed into cognitive gains and spiritual meaning through interpretation and learning. Cross-regional comparison highlights significant differences among museums with distinct cultural backgrounds in terms of architectural aesthetics, educational value, and emotional resonance. This study not only offers a practical framework for the refined management and spatial optimization of heritage museums, but also demonstrates that high-quality cultural environments can promote mental health and emotional restoration. The results extend the interdisciplinary framework of museum research and provide empirical evidence for environmental improvement and public health promotion in cultural heritage spaces in the digital era. Full article

Named entity recognition (NER) is one of the fundamental tasks in building knowledge graphs. For some domain-specific corpora, the text descriptions exhibit limited standardization, and some entity structures have entity nesting. The existing entity recognition methods have problems such as word matching noise interference and difficulty in distinguishing different entity labels for the same character in sequence label prediction. This paper proposes a span-based feature reuse stacked bidirectional long short term memory network (BiLSTM) nested named entity recognition (SFRSN) model, which transforms the entity recognition of sequence prediction into the problem of entity span suffix category classification. Firstly, character feature embedding is generated through bidirectional encoder representation of transformers (BERT). Secondly, a feature reuse stacked BiLSTM is proposed to obtain deep context features while alleviating the problem of deep network degradation. Thirdly, the span feature is obtained through the dilated convolution neural network (DCNN), and at the same time, a single-tail selection function is introduced to obtain the classification feature of the entity span suffix, with the aim of reducing the training parameters. Fourthly, a global feature gated attention mechanism is proposed, integrating span features and span suffix classification features to achieve span suffix classification. The experimental results on four Chinese-specific domain datasets demonstrate the effectiveness of our approach: SFRSN achieves micro-F1 scores of 83.34% on ontonotes, 73.27% on weibo, 96.90% on resume, and 86.77% on the supply chain management dataset. This represents a maximum improvement of 1.55%, 4.94%, 2.48%, and 3.47% over state-of-the-art baselines, respectively. The experimental results demonstrate the effectiveness of the model in addressing nested entities and entity label ambiguity issues. Full article

This study investigates the evolution of online public opinion during the COVID-19 pandemic by integrating topic mining with sentiment analysis. To overcome the limitations of traditional short-text models and improve the accuracy of sentiment detection, we propose a novel hybrid framework that combines a GloVe-enhanced Biterm Topic Model (BTM) for semantic-aware topic clustering with a RoBERTa-TextCNN architecture for deep, context-rich sentiment classification. The framework is specifically designed to capture both the global semantic relationships of words and the dynamic contextual nuances of social media discourse. Using a large-scale corpus of more than 550,000 Weibo posts, we conducted comprehensive experiments to evaluate the model’s effectiveness. The proposed approach achieved an accuracy of 92.45%, significantly outperforming baseline transformer-based baseline representative of advanced contextual embedding models across multiple evaluation metrics, including precision, recall, F1-score, and AUC. These results confirm the robustness and stability of the hybrid design and demonstrate its advantages in balancing precision and recall. Beyond methodological validation, the empirical analysis provides important insights into the dynamics of online public discourse. User engagement is found to be highest for the topics directly tied to daily life, with discussions about quarantine conditions alone accounting for 42.6% of total discourse. Moreover, public sentiment proves to be highly volatile and event-driven; for example, the announcement of Wuhan’s reopening produced an 11% surge in positive sentiment, reflecting a collective emotional uplift at a major turning point of the pandemic. Taken together, these findings demonstrate that online discourse evolves in close connection with both societal conditions and government interventions. The proposed topic–sentiment analysis framework not only advances methodological research in text mining and sentiment analysis, but also has the potential to serve as a practical tool for real-time monitoring online opinion. By capturing the fluctuations of public sentiment and identifying emerging themes, this study aims to provide insights that could inform policymaking by suggesting strategies to guide emotional contagion, strengthen crisis communication, and promote constructive public debate during health emergencies. Full article

Web3.0 aims to foster a trustworthy environment enabling user trust and content verifiability. However, the proliferation of fake news undermines this trust and disrupts social ecosystems, making the effective alignment of visual-textual semantics and accurate content verification a pivotal challenge. Existing methods still struggle with deep cross-modal interaction and the adaptive calibration of discrepancies. To address this, we introduce the Bidirectional Semantic Enhancement and Adversarial Network (BSEAN). BSEAN first extracts features using large pre-trained models: a hybrid encoder for text and the Swin Transformer for images. It then employs a Bidirectional Modality Mapping Network, governed by cycle consistency, to achieve preliminary semantic alignment. Building on this, a Semantic Enhancement and Calibration Network explores inter-modal dependencies and quantifies semantic deviations to enhance discriminative capability. Finally, a Dual Adversarial Learning framework bolsters event generalization and representation consistency through adversarial training with event and modality discriminators. Experiments on public Weibo and Twitter datasets validate BSEAN’s superior performance across all metrics, demonstrating its efficacy in tackling the complex challenges of deep cross-modal interaction and dynamic modality calibration within Web3.0 social networks. Full article

With the rapid development of social media, the spread of fake news has become a significant issue affecting social stability. To address the problems of incomplete feature extraction and simplistic loss function design in traditional fake news detection, this paper proposes a BBHL model based on hybrid loss optimization. The model achieves deep extraction of text features by integrating BERT, Bi-LSTM, and attention mechanisms, and innovatively fuses binary cross-entropy (BCE) loss with contrastive loss to enhance feature discriminability and the model’s generalization ability. Experiments on the Weibo, Twitter, and Pheme datasets demonstrate that the BBHL model significantly outperforms baseline models such as EANN and MCNN in metrics including accuracy and F1-score. Ablation experiments verify the effectiveness of contrastive loss, providing a robust and generalizable solution for fake news detection. Full article

The sustainable development of traditional villages faces a core challenge stemming from the disconnect between public perception and spatial planning. To address this issue, this study, taking Baishe Village—a national-level traditional village—as a case study, constructs and applies a “Digital Humanities + Spatial Analysis” research paradigm that integrates text mining, sentiment analysis, visual coding, and spatial analysis based on multimodal social media data (Sina Weibo and Xiaohongshu) from 2013 to 2023. It aims to conduct an in-depth analysis of tourists’ rural image perception structure, emotional tendencies, and their spatial differentiation characteristics, and subsequently propose spatial optimization pathways that promote the revitalization of its cultural landscape and sustainable land use. The main findings reveal the following: (1) In terms of cognitive structure, the rural image presents a ‘settlement-dominated’ four-dimensional structure, with settlement elements such as pit kilns (accounting for more than 70%) as the absolute core. (2) In terms of emotional tendencies, a cognitive tension is formed between the high recognition of architectural heritage value (positive sentiment: 57.44%) and significant dissatisfaction with service facilities. (3) In terms of spatial patterns, a “dual-core-driven” pattern of perceived hotspots emerges, with 83% of tourist activities concentrated in the central–southern main road area, revealing a “revitalization gap” in village spatial utilization. The contribution of this study lies in translating abstract public perceptions into quantifiable spatial insights, thereby constructing and validating a “Digital Humanities + Spatial Analysis” paradigm that fuses multimodal data and links abstract perception with concrete space. This provides a crucial theoretical basis and practical guidance for the living conservation of cultural landscapes, the enhancement of land use efficiency, and refined spatial governance. Full article