Frontiers in Big Data最新文献

Editorial: Responsible and robust evaluation for real-world recommendation and search systems. 社论：对现实世界的推荐和搜索系统进行负责任和稳健的评估。

IF 3.8

Frontiers in Big Data Pub Date : 2026-10-05 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1995917

Alejandro Bellogín, Pablo Sánchez, Laura Sebastiá

引用次数: 0

Big data analytics and sentiment analysis for health misinformation detection: a systematic literature review of influencing factors, implementation challenges and a data-driven adoption framework. 健康错误信息检测的大数据分析和情感分析：影响因素、实施挑战和数据驱动的采用框架的系统文献综述。

IF 3.8

Frontiers in Big Data Pub Date : 2026-10-05 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1948398

Khurram Shahzad, Asfa Muhammed Din Javeed, Shakil Ahmad, Mujahid Latif, Kashaf Saleem

{"title":"Big data analytics and sentiment analysis for health misinformation detection: a systematic literature review of influencing factors, implementation challenges and a data-driven adoption framework.","authors":"Khurram Shahzad, Asfa Muhammed Din Javeed, Shakil Ahmad, Mujahid Latif, Kashaf Saleem","doi":"10.3389/fdata.2026.1948398","DOIUrl":"https://doi.org/10.3389/fdata.2026.1948398","url":null,"abstract":"The study aimed to identify the factors influencing big data analytics (BDA) and sentiment analysis (SA) for health misinformation detection and associated challenges for the adoption of BDA and SA across health ecosystems. The systematic literature review (SLR) methodology was applied for addressing the study's objectives. Thirty most relevant seminal studies (peer-reviewed articles and conference proceedings) were retrieved from 11 digital platforms to conduct the SLR. Results of the study showed that advanced deep learning architectures, feature engineering, sentiment intelligence, real-time analytics, and human-centered verification were key factors that influenced big data analytics and sentiment analysis based health misinformation detection. It was also identified that annotation limitations, scalability constraints, domain adaptation challenges, ethical concerns, and sustainability challenges negatively impacted the adoption of big data and sentiment driven health misinformation detection systems. The study has developed a data-driven framework to effectively adopt and sustain big data analytics and sentiment analysis to detect health misinformation. Systematic review registration: https://osf.io/wjrm6.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1948398"},"PeriodicalIF":3.8,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Controlled knowledge updating in memory-enabled AI agents: beyond model editing and recall. 支持记忆的人工智能代理中的受控知识更新：超越模型编辑和召回。

IF 3.8

Frontiers in Big Data Pub Date : 2026-09-28 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1938137

Gabriel Chavira Juárez, Eder Jahir Gonzalez Bravo, Guadalupe Esmeralda Rivera García, Javier A Arcos Espinosa, Salvador W Nava-Díaz

{"title":"Controlled knowledge updating in memory-enabled AI agents: beyond model editing and recall.","authors":"Gabriel Chavira Juárez, Eder Jahir Gonzalez Bravo, Guadalupe Esmeralda Rivera García, Javier A Arcos Espinosa, Salvador W Nava-Díaz","doi":"10.3389/fdata.2026.1938137","DOIUrl":"10.3389/fdata.2026.1938137","url":null,"abstract":"Large language model agents that persist across sessions, tools, users, and changing environments do more than answer isolated prompts; they accumulate state. When new evidence arrives, the central question is where that change should live: transient context, external memory, tool or workflow definitions, activation states, or model parameters. Recent benchmarks compare update mechanisms and assess memory over time, while emerging architectures coordinate multiple memory types. In this Perspective, we examine how a decision layer should select among update substrates and evaluate whether its choices remain appropriate. A controlled update is not merely a successful edit or a recalled fact; it is a decision to alter the least invasive substrate sufficient for the claim's scope, expected persistence, and evidentiary strength, including when the correct action is not to write persistent state. We propose a substrate-aware view of controlled knowledge updating organized by three principles: substrate proportionality, temporal defeasibility, and auditable continuity. This framing treats knowledge updating as a longitudinal control problem and motivates evaluation criteria that include update selection, temporal consistency, interference, reversibility, efficiency, and robustness. The resulting agenda connects memory, knowledge editing, agent evolution, and benchmarking under a single practical question: how should an agent decide what to change so that future behavior improves without accumulating uncontrolled state?","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1938137"},"PeriodicalIF":3.8,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13654771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149166599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine-assisted adaptive feedback consensus method for large-scale group decision making based on reinforcement learning. 基于强化学习的大规模群体决策的机器辅助自适应反馈共识方法。

IF 3.8

Frontiers in Big Data Pub Date : 2026-09-24 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1921037

Hongyu Yu, Xuanhua Xu, Weiwei Zhang

{"title":"Machine-assisted adaptive feedback consensus method for large-scale group decision making based on reinforcement learning.","authors":"Hongyu Yu, Xuanhua Xu, Weiwei Zhang","doi":"10.3389/fdata.2026.1921037","DOIUrl":"10.3389/fdata.2026.1921037","url":null,"abstract":"Introduction: Traditional consensus-reaching processes in large-scale group decision making (LSGDM) rely heavily on empirically determined feedback parameters and have difficulty balancing consensus efficiency with the preservation of expert opinions. To address this problem, this paper proposes a reinforcement learning-based machine-assisted adaptive feedback consensus method.Methods: First, a hesitation degree is introduced to improve the score function of hesitant fuzzy linguistic term sets (HFLTSs), thereby reducing information loss during linguistic information transformation. Second, a comprehensive relationship matrix integrating preference similarity and social trust relationships is constructed, and K-Means clustering is employed to divide large-scale expert groups. On this basis, the consensus-reaching process is modeled as a Markov decision process (MDP), and the LSGDM consensus-reaching process is modeled as a dynamic sequential decision problem. A deep deterministic policy gradient (DDPG) agent is utilized to learn feedback adjustment strategies, enabling feedback parameters to be dynamically adjusted according to the evolution state of group opinions. Meanwhile, network text data and the TF-IDF method are combined to determine attribute weights and improve decision objectivity. Finally, an emergency decision-making case of the \"Beijing-Tianjin-Hebei rainstorm\" is conducted to verify the proposed method.Results: The results show that the proposed method can achieve the preset consensus threshold within fewer discussion rounds while effectively balancing the group consensus level and expert opinion retention.Discussion/conclusion: These findings verify the effectiveness and feasibility of the proposed method, indicating that it can provide effective decision support for adaptive feedback consensus reaching in LSGDM emergency decision-making.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1921037"},"PeriodicalIF":3.8,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13645702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anticipating user desires: predicting software product feature demand through consumer behavioral analytics. 预测用户需求：通过消费者行为分析预测软件产品的功能需求。

IF 3.8

Frontiers in Big Data Pub Date : 2026-09-24 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1931695

Abdullah A Aldaeej

{"title":"Anticipating user desires: predicting software product feature demand through consumer behavioral analytics.","authors":"Abdullah A Aldaeej","doi":"10.3389/fdata.2026.1931695","DOIUrl":"10.3389/fdata.2026.1931695","url":null,"abstract":"In this study, we introduce the Dynamic Behavioral Feature Predictor (DBFP), a machine learning model designed to predict feature demands for software products based on consumer behavioral analytics. The model leverages advanced techniques such as Deep Convolutional Neural Networks (1D CNN) and Long Short-Term Memory (LSTM) networks to identify complex user behavior patterns. We demonstrate that DBFP achieves a high accuracy of 99%, outperforming established models such as LSTM and 1D CNN in extensive comparative experiments. Although the model shows promising performance, we note that its accuracy may be sensitive to data quality, and further research is needed to evaluate its robustness in real-world applications. DBFP excels at identifying diverse user behavior types, offering tailored recommendations for software features that align with individual user preferences. This paper highlights the potential of behavioral analytics in personalizing software development, enhancing the user experience, and improving the efficiency of feature prioritization. By bridging the fields of machine learning, big data (BD), and software personalization, DBFP lays the foundation for future advancements in user-centric software engineering. This study not only contributes to the field of predictive analytics but also opens new avenues for applying behavioral insights to software design and development.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1931695"},"PeriodicalIF":3.8,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13645530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating the reach and equity of public spaces in Mexico City using mobility data: the case of the Utopías in Iztapalapa. 利用交通数据评估墨西哥城公共空间的覆盖范围和公平性：以伊斯塔帕拉帕的Utopías为例。

IF 3.8

Frontiers in Big Data Pub Date : 2026-09-23 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1942458

Ollin D Langle-Chimal, Natalia Cadavid-Aguilar, Alberto Meouchi-Vélez, Roberto Ponce-López

{"title":"Evaluating the reach and equity of public spaces in Mexico City using mobility data: the case of the Utopías in Iztapalapa.","authors":"Ollin D Langle-Chimal, Natalia Cadavid-Aguilar, Alberto Meouchi-Vélez, Roberto Ponce-López","doi":"10.3389/fdata.2026.1942458","DOIUrl":"10.3389/fdata.2026.1942458","url":null,"abstract":"Cities invest in social infrastructure to improve well-being, but the benefits depend on who can use these spaces and who actually does. Access is therefore not only a matter of whether people live close enough to reach a facility, but also of who travels to it and from how far. We examine this realized access using high-resolution human mobility data from Location-Based Services (LBS) for the Utopías, a network of thirteen community centers offering free recreation, culture, and care in Iztapalapa, an underserved borough of 1.8 million residents in eastern Mexico City. We combine six months of mobility traces with public transit data (GTFS), a georeferenced crime registry, and a catalog of facility activities to identify the factors associated with each site's visitors. Three factors capture distinct dimensions of reach. Transit connectivity tracks the number of visitors a site draws. Facilities with more distinctive activities, such as a planetarium or a dinosaur park, show a greater increase in visitor travel distance at weekends. By contrast, a higher share of violent crime in the surrounding area is associated with more localized and lower-income visitor catchments. Together, these findings show that the reach of social infrastructure varies not only with proximity, but also with transport, the distinctiveness of what facilities offer, and local safety.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1942458"},"PeriodicalIF":3.8,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13642205/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149129075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Humans as infrastructure: is AI-initiated steganography in social media possible? 人类作为基础设施：人工智能发起的社交媒体隐写术可能吗？

IF 3.8

Frontiers in Big Data Pub Date : 2026-09-18 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1923373

Daniele Ortu

引用次数: 0

From crisis to new routine: shifts in urban shopping mobility and socioeconomic inequality during and after the COVID-19 shock. 从危机到新常态：2019冠状病毒病冲击期间和之后城市购物流动性和社会经济不平等的变化

IF 3.8

Frontiers in Big Data Pub Date : 2026-09-15 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1776172

Yilun Xu, Mohsen Bahrami, Alex Pentland

{"title":"From crisis to new routine: shifts in urban shopping mobility and socioeconomic inequality during and after the COVID-19 shock.","authors":"Yilun Xu, Mohsen Bahrami, Alex Pentland","doi":"10.3389/fdata.2026.1776172","DOIUrl":"10.3389/fdata.2026.1776172","url":null,"abstract":"Introduction: This study examines the impact of the COVID-19 pandemic on revealed shopping-location patterns and their persistence during the early recovery period. It focuses on urban shopping mobility in New York City and investigates whether observed store-selection patterns returned to, or remained different from, their pre-pandemic baseline by 2021 across different socioeconomic communities.Methods: We use large-scale mobility and place datasets together with census information to analyze visits to general merchandise and department stores in New York City. A modified Huff gravity model and a metaheuristic calibration method are used to quantify temporal shifts in store-selection patterns. We also use unsupervised learning techniques and statistical inference models to examine heterogeneity across socioeconomic communities and evaluate changes before, during, and after the COVID-19 shock.Results: The proposed model captures the dynamics of shopping-location decisions and temporal visit patterns under changing pandemic-era constraints. The results suggest that New Yorkers' revealed store-selection patterns changed substantially during the COVID-19 shock, with increased emphasis on store area, chain loyalty, and nearby points of interest, and reduced sensitivity to customer-store distance. These patterns did not fully revert to their 2019 baseline by 2021, although 2021 is interpreted as an early recovery and reopening period rather than a fully post-pandemic equilibrium.Discussion: The findings highlight the societal dimension of urban shopping mobility disruptions during crises and show that mobility-based store-selection patterns vary across socioeconomic communities. The results can help urban planners, managers, and marketers better understand heterogeneous shifts in shopping mobility and adapt strategies under crisis and recovery conditions.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1776172"},"PeriodicalIF":3.8,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13619519/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149082339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Voluntary disclosure, banking stability, and AI-augmented forensic accounting: an exploratory econometric and machine-learning study of Palestinian banks. 自愿披露、银行稳定性和人工智能增强法务会计：巴勒斯坦银行的探索性计量经济学和机器学习研究。

IF 3.8

Frontiers in Big Data Pub Date : 2026-09-14 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1914133

Bahaa Subhi Razia, Najwan Ibrahim Jadallah, Qasim Zureigat, Reem Khamis, Bahaa Subhi Awwad

{"title":"Voluntary disclosure, banking stability, and AI-augmented forensic accounting: an exploratory econometric and machine-learning study of Palestinian banks.","authors":"Bahaa Subhi Razia, Najwan Ibrahim Jadallah, Qasim Zureigat, Reem Khamis, Bahaa Subhi Awwad","doi":"10.3389/fdata.2026.1914133","DOIUrl":"10.3389/fdata.2026.1914133","url":null,"abstract":"Introduction: Information asymmetry between bank managers and external stakeholders is a common relationship between financial-statement fraud and banking instability.Methods: This study combines an AI-augmented forensic accounting framework with voluntary disclosure, banking stability indicators, and exploratory machine learning (ML) approaches. The study integrates fixed-effects regression with Logistic Regression, Random Forest, XGBoost, Isolation Forest, and SHAP-based explainability using panel data from the whole population of seven banks listed on the Palestine Exchange (2019-2025).Results: According to the econometric results, there is a conditional rather than a uniform relationship between voluntary disclosure and financial stability, with variation by bank size, age, and leverage. Additionally, the exploratory machine-learning analyses indicate that nonlinear approaches could help find unusual bank-year records and instability-risk patterns that are not fully captured by traditional linear models. SHAP analysis enhanced the interpretability of model classifications, and ensemble approaches outperformed Logistic Regression in cross-validation within this small sample. The machine-learning results are considered as exploratory proof-of-concept evidence rather than externally confirmed predictive outcomes due to the small sample size and lack of independently verified fraud labels.Discussion: Overall, the study shows how AI-augmented forensic accounting can enhance supervisory prioritization, instability-risk screening, and the expert assessment of anomalous observations in institutionally unstable banking contexts, thereby complementing traditional econometric analysis.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1914133"},"PeriodicalIF":3.8,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13616643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An empirical test of antecedents and perceived performance of big data analytics in telecommunication companies. 电信公司大数据分析前因和感知绩效的实证检验。

IF 3.8

Frontiers in Big Data Pub Date : 2026-09-14 eCollection Date: 2026-01-01 DOI: 10.3389/fdata.2026.1763520

Ibrahim Magboul, Diaeldin Osman, Fadi Herzallah, Alnour Nadir Osman, Dexter Gittens

{"title":"An empirical test of antecedents and perceived performance of big data analytics in telecommunication companies.","authors":"Ibrahim Magboul, Diaeldin Osman, Fadi Herzallah, Alnour Nadir Osman, Dexter Gittens","doi":"10.3389/fdata.2026.1763520","DOIUrl":"10.3389/fdata.2026.1763520","url":null,"abstract":"The emergence of Data concepts, sources, types, and analysis mechanisms has changed dramatically in recent years. Nowadays, big data analytics (BDA) dominates the attention of scholars and practitioners. Since this emergence, different business sectors have started deploying BDA in various endeavors to help improve decision-making and gain a competitive advantage. Recently, some organizations reported that BDA has not yielded the expected business value. In addition, researchers have focused so far on BDA usage in developed countries. Antecedents of BDA adoption have been widely researched; nevertheless, this study is among the first to collate selected antecedents and perceived performance of BDA adoption in one integrated and validated model in the telecommunication sector. Using a purposive sampling technique, thestudy deployed structural equation modeling (SEM) to analyze 201 observations. The findings reveal that six of the eight antecedents of BDA significantly impact BDA, while two showed no significant effect. Furthermore, this research finds that BDA has a positive impact on perceived performance, thus contributing to the technology adoption literature by offering valuable insights for researchers and practitioners in a developing country like Sudan.","PeriodicalId":52859,"journal":{"name":"Frontiers in Big Data","volume":"9 ","pages":"1763520"},"PeriodicalIF":3.8,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13616730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0