Search Results (201)

(1) Background: Information diffusion models developed for graph-based platforms such as Reddit and broadcast architectures such as Telegram identify temporal features—particularly the timing of peak spread—as dominant predictors of coverage. Whether these predictors generalise to platforms where content is distributed through algorithmic recommendation rather than social-graph contagion remains an open question. (2) Methods: We analyse the YouNiverse dataset, comprising 133,364 English-language YouTube channels observed weekly from January 2015 to September 2019 (18.9 million observations). We derive channel-level diffusion features—including time-to-peak, post-peak decay rate, diffusion volatility, and upload frequency—and train three machine learning models (Linear Regression, Random Forest, and LightGBM) on two tasks: predicting peak weekly view growth (regression) and identifying viral channels (classification). A single-feature naive baseline (subscriber count alone) establishes the marginal contribution of the broader feature set beyond subscriber count alone, and a temporal split experiment (training on channels peaking before 2018, testing on 2018–2019) assesses cross-temporal stability. Because subscriber count and subscriber rank are measured at the October 2019 crawl, this is a retrospective characterisation rather than a strict real-time forecasting design. (3) Results: LightGBM achieves $R^2=0.776$ (5-fold CV: $0.778\pm 0.003$) compared with $R^2=0.548$ for the naive baseline, a net gain of $+0.228R^2$. Because subscriber rank and subscriber count are near-perfectly collinear, we interpret them jointly as a channel-size dimension (42.2% of total mean absolute SHAP attribution), rather than as independent effects. Time-to-peak ranks fourteenth (1.1%), in contrast to its dominant role on Reddit ($r=0.995$, rank #1). For virality classification, LightGBM achieves ROC-AUC $=0.967$. Under the temporal split, Random Forest ($R^2=0.703$) outperforms LightGBM ($R^2=0.683$), showing greater cross-temporal stability within this retrospective split. (4) Conclusions: Within the 2015–2019 data, the results are consistent with algorithmic recommendation weakening the relationship between temporal diffusion dynamics and coverage magnitude at the channel level. Time-to-peak is weakly informative in this setting, while generalisation to the current recommendation system requires validation on newer data. Full article

The losses caused by frequent natural disasters are increasing day by day, and the short-video platform has become the core digital space for the public to pay attention to disasters and express their demands. In the face of massive multimodal data, how to automatically extract features and quantify their impact on public behavior is a key technical challenge facing information systems and computational social sciences. To address this issue, this study proposes an automated multimodal data mining and modeling framework that integrates YOLOv11-based computer vision with BERT-based natural language processing for disaster short-video analysis. Real-world short-video and interaction data were automatically collected using web crawling. YOLOv11 was employed to identify and quantify two types of visual information—relief information and suffering information—while BERT was used to extract a text-based rescue satisfaction index. Then, the partial least squares structural equation model was used to explore the driving mechanism of information characteristics on public engagement. It was found that the content of relief information in videos has a significant positive impact on satisfaction but a significant negative impact on engagement. The content of suffering information has a significant negative impact on satisfaction but a significant positive impact on engagement. In addition, satisfaction has a significant negative impact on engagement and plays an intermediary role between the two types of information content and engagement. By integrating YOLOv11 and BERT into a unified short-video analytics framework, this study extends automated multimodal disaster information analysis and provides practical support for optimizing emergency communication and disaster information-release strategies. Full article

Arabic natural language processing (NLP) faces major difficulties due to the language’s rich morphological structure and the scarcity of high-quality datasets, especially for educational material distributed across diverse online platforms. Many existing large-scale corpus construction methods depend on extensive web crawling followed by substantial post-processing. This process may introduce irrelevant or low-quality data and often fails to represent the target domain adequately. As a result, a robust approach to developing corpora tailored for domain-sensitive educational NLP systems and linguistic depth is critical, as most current resources are inadequate. This paper presents ArabicEduCrawler, an AI-assisted focused crawling framework designed to improve the acquisition, discovery, and organization of Arabic educational web content. The framework integrates domain-aware source selection, in-crawl Arabic language detection using FastText, large language model (LLM)-assisted XPath extraction, and metadata retrieval to support corpus quality and traceability. Its two-layer architecture combines dynamic web crawling using Scrapy-Playwright with advanced NLP processing, including automatic linguistic annotation with GateNLP and Stanza and a sentence-aware chunking strategy designed for transformer-compatible token limits. Experiments across four major Arabic educational domains resulted in the creation of the Arabic Educational Web Corpus (AraEdu-WC), which consists of 101,770 documents segmented into approximately 286 k text chunks, with more than 50 million tokens, 289,778 sentences, and nearly 3.5 million named entities. The system achieved a harvest ratio of 95.25%, indicating its effectiveness in filtering and retaining relevant content. The sentence-aware chunking evaluation showed consistent improvements in top-ranked retrieval, achieving the highest Hit Rate@10 and MRR@10 across all four embedding models. In particular, the multilingual-E5-large model achieved a Hit Rate@10 of 70%, Precision@10 of 18%, and MRR@10 of 57%. These findings demonstrate that the proposed approach provides an effective balance between crawl efficiency, language purity, and content richness, offering a high-quality Arabic educational corpus for downstream NLP and retrieval research. Full article

The rapid development of AI Technology has significantly influenced digital art and triggered widespread discussion on social media platforms, particularly X. The use of AI in generating visual artworks and digital content has elicited diverse public responses, ranging from support for technological innovation to concerns regarding originality and the role of human artists. In this study, a total of 1737 tweets were collected through a data crawling process using relevant keywords and processed using RapidMiner through preprocessing stages to analyze user sentiment on the X platform toward the application of AI in digital art. The data include data cleaning, text normalization, and tokenization, before being classified into positive and negative sentiments. Three classification algorithms, Naïve Bayes, support vector machine (SVM), and decision tree, were applied to compare sentiment distributions. The results show that the Naïve Bayes model classified 30.5% of tweets as positive and 69.5% as negative, while the SVM and Decision Tree models showed a stronger bias toward negative sentiment, with 93.3% and 88.8% negative classifications, respectively. These findings indicate that negative sentiment toward AI in digital art is more dominant among users. Full article

Presently, video communities such as YouTube, bilibili and TikTok have emerged as core fields for information dissemination and public opinion generation. Their embedded user dynamic interaction data support research on public cognitive behavior and content dissemination laws. This study used web crawling technology to construct a complete dataset including 367 video metadata and 2.39 million comment records from Luo Xiang Speaks on Criminal Law—a prominent legal popularization account on the bilibili platform—and systematically explored the temporal evolution patterns of comment interactions in video communities. By establishing a four-dimensional feature system alongside the k-means++ clustering algorithm, this study successfully identified three distinct comment growth patterns (p < 0.001): the burst–decay, the multi-wave oscillation, and the delayed peak. The results of non-parametric tests showed that these three patterns have significant differences in core features (e.g., peak delay time, skewness) and are systematically related to user grade structure, content interaction depth, and release timing. In addition, the user interaction networks of different videos demonstrate significant structural heterogeneity and disassortative mixing, characterized by a highly active minority dominating the discourse, while peripheral nodes gravitate toward high-profile hubs. These findings offer researchers deeper insights into the micro-mechanisms of information dissemination. Full article

In the rapidly evolving era, with the advancement of AI technology in education, Chat Generative Pre-trained Transformer (ChatGPT) is widely used in education to help students simplify the learning process. In other words, the implementation of ChatGPT makes the learning process more efficient and relevant. This study was conducted to analyze sentiment from social media platforms such as X to determine the impact of ChatGPT’s use in higher education in Indonesia. The research method involves data collection using the data crawling method for the X platform, which is integrated with the RapidMiner application. This sentiment analysis aims to identify trends in positive, negative, and neutral sentiment towards the use of ChatGPT in higher education in Indonesia and Thailand by using the Naive Bayes Classifier classification method and the Cross-Industry Standard Process for Data Mining method to design, execute, and evaluate data analytics projects. This analysis is expected to provide an initial overview of emerging sentiment trends as well as insights into how ChatGPT is perceived in the higher education environment. Overall, the results of this study provide an overview of public perception regarding the influence of ChatGPT in higher education in Indonesia and serve as a foundation for developing policies related to more responsible AI implementation in the academic environment. Full article

Real-time navigation systems are increasingly used to provide optimal driving routes together with accurate travel time predictions that reflect dynamic urban traffic conditions. Recent advances have focused on integrating structured traffic data from traditional APIs with unstructured, context-rich information extracted via semantic crawling of news websites and social media platforms. This survey reviews state-of-the-art approaches that combine these heterogeneous data sources to improve route planning and travel time estimation, with special attention to the challenges posed by incident detection, event extraction, and multimodal data fusion. We discuss core methodologies including natural language processing techniques for event recognition, machine learning models for traffic prediction, and graph-based routing algorithms, highlighting their advantages and limitations. Finally, we outline open research directions for building context-aware navigation systems able to adapt to real urban mobility conditions. Full article

With the melting of Arctic sea ice and extended navigable windows, polar navigation has gained prominent commercial and strategic value but faces challenges like strong ice reflection, high target texture similarity, and large obstacle scale variation. Aiming at scarce polar-specific datasets, poor adaptability of general algorithms, and disconnection between identification and navigation decisions, this study constructed a technical system integrating “dataset construction–algorithm improvement–system development”. A purpose-built polar dataset with 1342 images (covering drift ice, iceberg, ice channel, and ship) was built via web crawling, video frame extraction, and data augmentation. A dual-path optimization scheme for lightweight YOLO models was proposed: the YUV + CLAHE module suppresses strong reflection, and the IceTextureAttention module enhances discriminability of similar targets, with SCConv optimizing computational efficiency. A visual intelligent system embedded with a Polar Code-based risk assessment module was developed to output three-level risks and navigation suggestions. Experimental results show the optimized YOLOv8n + YUV + CLAHE model achieves an overall mAP@0.5 of 0.858 and a recall rate of 0.821. The system runs stably on shipborne equipment with an average image processing latency of 85 ms and a practical detection accuracy of 84.3%, effectively reducing crew workload and improving polar navigation safety. Full article

With the rapid advancement in Generative Artificial Intelligence (GenAI), AI-generated content (AIGC) lacking human cognitive oversight is increasingly permeating open web environments and academic communication systems. This study integrates longitudinal retraction data (Retraction Watch Database, 1990–2026), web-scale analyses of AI-content penetration (Common Crawl, 2013–2026), and bibliometric mapping of governance scholarship (Web of Science Core Collection, Scopus, Google Scholar, 2020–2026) to diagnose the cross-level misalignment between synthetic-content diffusion, AI-related misconduct pressure, and governance attention. On this basis, it proposes a Normalized Coverage Index (NCI) to measure the relative relationship between scholarly attention to AI-related academic misconduct governance and the level of misconduct pressure observed through retraction data across disciplines. The results reveal pronounced asymmetries at the disciplinary level. Fields such as chemistry (0.04), physics, mathematics & statistics (0.11), and life sciences & biology (0.34) exhibit clear governance gaps, whereas Education shows a comparatively excessive level of attention (NCI = 29.26). Since 2022, AIGC has expanded rapidly across open web corpora, accompanied by a sharp rise in AI-related retractions, which also exhibit a longer detection lag than traditional forms of misconduct (2.77 years vs. 1.91 years). Although the volume of academic governance-related research has grown rapidly, its proportion within the broader body of AI-related research has declined, suggesting that scholarly attention to governance has not kept pace with technological diffusion. Consequently, a structural misalignment in governance—closely tied to the allocation of attention—has emerged within the academic system in the era of GenAI. This misalignment may pose potential risks to the robustness of the knowledge production system. Addressing it requires rebuilding epistemic infrastructure through provenance transparency, auditable workflows, and governance-aware seed corpora aligned with empirically concentrated risks. Full article

Despite having approximately 24 million native speakers, Romanian remains a low-resource language for automatic speech recognition (ASR), with few accurate and publicly available systems. To address this gap, this study explores the challenges of adapting modern speech recognition models, such as wav2vec 2.0 and Conformer, to Romanian. Our investigation is a comprehensive analysis of the two models, their capabilities to adapt to Romanian data, and the performance of the trained models. The research also focuses on unique attributes of the Romanian language, data collection techniques, including weakly supervised learning, and processing methodologies. Building on the previously introduced Echo dataset of 378 h, we release CRoWL (Crawled Romanian Weakly Labeled), a weakly supervised dataset of 9000 h created via automatic transcription. We obtain strong results that, to the best of our knowledge, are competitive with or exceed publicly reported results for Romanian under comparable open evaluation settings, with Conformer attaining 3.01% WER on Echo + CRoWL and wav2vec 2.0 reaching 4.04% (Echo) and 4.17% (Echo + CRoWL). In addition to the datasets, we also release our most capable models as open source, along with their training plans, thereby providing a solid foundation for researchers interested in languages with limited representation. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

Urban flood information enables managers to rapidly synthesize comprehensive flood event profiles, serving as critical evidence for flood control decision making. Compared with traditional methods, public data offer unprecedented spatiotemporal granularity due to its high volume, multidimensionality, and real-time nature. In this paper, we investigated public data’s usefulness and generalizability of spatial feature differences using multi-source social media data as an entry point. We selected rainstorm events that occurred in three cities located in the North China Plain, the Southeast Coastal Region, and the Western Region of China, with vastly different developmental statuses in 2023. Then, multi-platform data from the events were collected and analyzed through crawling and topic mining. The results indicate that: (1) social media data from different sources are complementary to each other and can collectively extract plenty of neglected waterlogging points to supplement official data, with a supplementary rate reaching 171% on average; and (2) social media data has significant value in spatial characterization, which means that its availability remains constant despite geographical differences and can self-adapt to local geography, inhabitant profiles and social development levels. To address the issues of limited available data and essential information lacking during the analysis process, we propose recommendations for data processing and city managers to enhance the scientific value of social media data utilized in practice. Full article

To achieve high-precision trajectory tracking control for an underwater hexapod robot, this paper proposes a hierarchical control architecture. Firstly, a multi-rigid-body dynamic model for the robot is established based on the Newton-Euler method and reasonably simplified. Secondly, a Central Pattern Generator (CPG) network with the Hopf oscillator as its core is designed to generate stable and coordinated crawling gaits. By introducing a steering parameter, a kinematic model connecting the CPG output is constructed. Furthermore, based on this dynamic and kinematic model, an upper-layer Model Predictive Controller (MPC) is designed. The optimized control quantities output by the MPC are mapped into the rhythmic parameters of the CPG network via a transfer function established by fitting experimental data, thus forming the complete MPC-CPG controller. Finally, the proposed method is validated through simulations of circular trajectory tracking. The results show that even in the presence of initial errors, the controller can converge rapidly, with trajectory position error consistently maintained within −0.1 m~0.1 m, and heading angle error confined to the range of −15~15°. The experiments fully demonstrate the effectiveness of the proposed MPC-CPG controller in ensuring trajectory tracking accuracy, motion smoothness, and system stability. Full article

Smart contracts are deployed and represented as bytecodes in blockchain networks, and these bytecodes are machine-readable codes. Only a small number of deployed smart contracts have their verified human-readable code publicly accessible to blockchain users. To improve the understandability of deployed smart contracts, we explored rule-based classification of smart contracts using iterative integration of fingerprints of relevant function interfaces and keywords. Our classification system included categories for standard contracts such as ERC20, ERC721, and ERC1155, and non-standard contracts like FinDApps, cross-chain, governance, and proxy. To do this, we first identified the core function fingerprints for all ERC token contracts. We then used an adapted header extractor tool to verify that these fingerprints occurred in all of the implemented functions within the bytecode. For the non-standard contracts, we took an iterative approach, identifying contract interfaces and relevant fingerprints for each specific category. To classify these contracts, we created a rule that required at least two occurrences of a relevant fingerprint keyword or interface. This rule was stricter for standard contracts: the 100% occurrence requirement ensures that we only identify compliant token contracts. For non-standard contracts, we required a minimum of two relevant fingerprint occurrences to prevent hash collisions and the unintentional use of keywords. After developing the classifier, we evaluated its performance on sample datasets. The classifier performed very well, achieving an F1 score of over 99% for standard contracts and a solid 93% for non-standard contracts. We also conducted a risk analysis to identify potential vulnerabilities that could reduce the classifier’s performance, including hash collisions, an incomplete rule set, manual verification bottlenecks, outdated data, and semantic misdirection or obfuscation of smart contract functions. To address these risks, we proposed several solutions: continuous monitoring, continuous data crawling, and extended rule refinement. The classifier’s modular design allows for these manual updates to be easily integrated. While semantic-based risks cannot be completely eliminated, symbolic execution can be used to verify the expected behavior of ERC token contract functions with a given set of inputs to identify malicious contracts. Lastly, we applied the classifier on contracts deployed Ethereum main network. Full article

Background/Objectives: The rapidly evolving crisis of Novel Synthetic Opioids (NSOs) poses a serious and growing threat for global public health. NSOs include prescription/non-prescription opioids (fentanyl, non-fentanyl analogues), herbal derivatives, and other emerging analogues that are of critical concern due to their high potency, misuse potential, and addiction and intoxication risk. There remains an important gap in real-time, systematic monitoring of NSOs emergence, especially in online communities where these substances appear for the first time. This study aimed to employ the NPSfinder^® automated web-crawling tool to detect, monitor, analyse, and evaluate the evolving NSOs scene. Methods: Data were collected during two time-periods, i.e., 2017–2019 and 2023, from selected high traffic psychonaut online platforms to better understand trends in opioids market evolution and adaptability and compare NPSfinder^® findings with other well-known Early Warning Systems (EWSs) databases to assess detection overlap and early identification capacity. Results: Within the selected time-periods, a total of 446 NSOs were detected by NPSfinder^®; fentanyl analogues (n = 249) were dominant, with a notable rise in non-fentanyl analogues, especially nitazene-like compounds, in 2023. Over 57% of these NSOs were not captured by any of the other EWSs, confirming the tool’s strong capacity to identify early threats. Conclusions: Overall, the low overlap across EWS databases underscores the global challenges in comprehensive opioid detection and surveillance. Future studies should integrate web-crawler findings with real-world datasets. It will be vital to combine these efforts with toxicological, mortality, and clinical outcome analyses, especially for emerging research compounds, to inform targeted harm-reduction strategies. Full article