Information Processing & Management最新文献_第5页

Enriching object-aware image–text highlight information for visual question generation 丰富对象感知图像-文本高亮信息，用于视觉问题生成

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2025-09-04 DOI: 10.1016/j.ipm.2025.104379

Seungyeon Lee, Dong-Gyu Lee

{"title":"Enriching object-aware image–text highlight information for visual question generation","authors":"Seungyeon Lee, Dong-Gyu Lee","doi":"10.1016/j.ipm.2025.104379","DOIUrl":"10.1016/j.ipm.2025.104379","url":null,"abstract":"<div><div>Visual question generation is a challenging task of comprehensively interpreting images and expressing them in natural language. Generating visual questions requiring detailed information depends on identifying key objects and their context when interpreting images with a highlight on the target object. Conventionally, methods rely on global image information or generate captions for the entire image to use as text for question generation. However, these methods often lack focus on target objects and missing key details. In this paper, we propose an object-aware highlighted visual question generation method that enhances question generation by emphasizing target object features in both image and text representations. Our method consists of two key modules: (1) an image feature extraction and transformation module that extracts and highlights relevant object-specific information, and (2) a visual question generation module that uses this highlighted information to generate more specific and contextually enriched questions. We further introduce mutual information loss to enhance the correlation between generated questions and image content. Extensive experiments on K-VQG, VQA v2.0, and OK-VQA show that our method outperforms state-of-the-art models, especially with a 28.25% BLEU score improvement on K-VQG, highlighting its effectiveness.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 2","pages":"Article 104379"},"PeriodicalIF":6.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The innovator's balance between academic social capital and disruptive innovation 创新者在学术社会资本与颠覆性创新之间的平衡

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2025-09-03 DOI: 10.1016/j.ipm.2025.104382

Xingpeng Liu, Jianlin Zhou, An Zeng, Xiaohua Cui

{"title":"The innovator's balance between academic social capital and disruptive innovation","authors":"Xingpeng Liu, Jianlin Zhou, An Zeng, Xiaohua Cui","doi":"10.1016/j.ipm.2025.104382","DOIUrl":"10.1016/j.ipm.2025.104382","url":null,"abstract":"<div><div>Disruptive innovation, which plays an essential role in advancing scientific research by expanding the frontiers of human knowledge, has experienced a sustained decline, yet consensus on its causes remains elusive. Drawing on co-authorship networks derived from 647,218 publications by 307,460 researchers in the American Physical Society (APS) dataset (1893–2020), our social capital-based approach reveals an increasing dependence of scientific research on social capital and its progressive concentration within scientific elites. The encouraging finding is the observed decrease in the assortativity of the collaboration networks, suggesting the improving access to academic social resources for newcomers. However, the disruptiveness of collaborations between new and established researchers falls short of expectations. The superior performance of newcomer researchers in disruptive innovation compared to their senior counterparts, coupled with the inherent trade-off between innovativeness and peer acknowledgment, highlights a fundamental dilemma for young researchers: they must strive to balance leveraging academic social resources and maintaining disruptive innovation. And their adherence to authoritative collaborators might be one of the key determinants in the decline of disruptive innovation.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 2","pages":"Article 104382"},"PeriodicalIF":6.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards evidence-aware retrieval-augmented generation via self-corrective chain-of-thought 通过自我纠正思维链走向循证检索增强一代

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2025-09-03 DOI: 10.1016/j.ipm.2025.104369

Yining Li , Wenjun Ke , Jiajun Liu , Peng Wang , Jianghan Liu , Yao He

引用次数: 0

Whom to pity, whom to scold? Effects of empathetic and normative AI-assisted interventions on aggressive Reddit users with different activity profiles 该怜悯谁，该责备谁？移情和规范人工智能辅助干预对不同活动概况的攻击性Reddit用户的影响

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2025-09-03 DOI: 10.1016/j.ipm.2025.104316

Patrycja Tempska , Rafał Urbaniak , Maria Dowgiałło , Michal Ptaszynski , Alan Zajączkowski , Maja Milewska , Gniewosz Leliwa , Michał Marcińczuk , Maciej Brochocki , Michał Wroczyński

{"title":"Whom to pity, whom to scold? Effects of empathetic and normative AI-assisted interventions on aggressive Reddit users with different activity profiles","authors":"Patrycja Tempska , Rafał Urbaniak , Maria Dowgiałło , Michal Ptaszynski , Alan Zajączkowski , Maja Milewska , Gniewosz Leliwa , Michał Marcińczuk , Maciej Brochocki , Michał Wroczyński","doi":"10.1016/j.ipm.2025.104316","DOIUrl":"10.1016/j.ipm.2025.104316","url":null,"abstract":"<div><div>During a six-month experiment conducted on Reddit, we studied the impact of counter-speech interventions against personal attacks sent by 440 users regularly attacking others. We used two types of interventions, normative—which referred to social norms, and empathetic—which referred to emotions and encouraged perspective-taking. We employed a collective intelligence approach—the collaboration between human and machine intelligence. Artificial Intelligence was used to detect verbal aggression and notify human volunteers, who then performed the interventions, providing a level of context understanding and realistic human involvement not achieved by potentially automated responses. We analyzed the data from three perspectives. We used time series models of (1) the short-term impact of individual interventions, (2) of the cumulative impact of interventions received as the experiment progressed. We also (3) used aggregated data for a long-term before/after analysis. The short-term effect of interventions is damaging: users tend to be on average around 26% more aggressive the next day, but the effect does not last beyond two days. The cumulative effect of interventions is helpful: each intervention (up to around 8–10 total, the effectiveness of more interventions tends to be lower) decreases daily aggression by 4% on average, and the effects accumulate and balance out the short-term effect in the long run. The effectiveness of normative interventions seems overall higher, except for the less aggressive offenders, for whom empathetic interventions might be equally or more useful.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 2","pages":"Article 104316"},"PeriodicalIF":6.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A cognitive-affective chain-driven framework for emotion understanding 情感理解的认知-情感链驱动框架

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2025-09-03 DOI: 10.1016/j.ipm.2025.104367

Shuaipu Chen , Zhenghao Liu , Zhijian Zhang , Ke Qin , Yuxing Qian , Feicheng Ma

{"title":"A cognitive-affective chain-driven framework for emotion understanding","authors":"Shuaipu Chen , Zhenghao Liu , Zhijian Zhang , Ke Qin , Yuxing Qian , Feicheng Ma","doi":"10.1016/j.ipm.2025.104367","DOIUrl":"10.1016/j.ipm.2025.104367","url":null,"abstract":"<div><div>When users seek help on online health platforms, their expressions are diverse, unstructured, and cognitively complex, posing significant challenges for fine-grained users’ emotion understanding. Existing approaches typically rely on commonsense associations to statically model relationships between emotions and events, overlooking dynamic cognitive processes underlying these connections. To address this shortcoming, we proposed the Cognitive–Affective Chain framework, grounded in Social Support Theory, the Theory of Mind, and the James–Lange Theory of Emotion, to analyze users’ expression from a cognitive perspective. Based on this, we defined a novel task, Cognitive-aware Contextual Emotion Understanding (CCEU), which adapts Aspect-Based Sentiment Analysis to better capture the multidimensional and cognition-driven emotional content. To ensure fair and meaningful evaluation of large language models (LLMs) in cognitively demanding tasks, we introduced the Hybrid Generation and Classification Score (HGCS), a metric combining generation quality and classification reliability. Experimental results showed that LLMs can outperform baselines on HGCS by 15.56 %, even when F1 score drops by 2.12 %, demonstrating that HGCS can better reflect the capabilities of generative models in complex emotion understanding. Next, inspired by Dual Process Theory, we designed prompting strategies that simulate human-like reasoning, improving LLMs’ performance in CCEU task. However, behavioral analysis revealed a bias toward information support over emotional support, exposing the gap between machine inference and human empathy. Taking depression as an example, this study established a cognitively grounded paradigm for emotion modeling in mental health support, also contributing to the development of fair, socially responsive, and cognitively aligned AI systems.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 2","pages":"Article 104367"},"PeriodicalIF":6.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Zoning out in health management: Exploring information non-behaviour among older Chinese immigrants with diabetes in Canada 健康管理中的疏离：加拿大老年华人移民糖尿病患者的信息疏离研究

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2025-09-02 DOI: 10.1016/j.ipm.2025.104386

Xiaoqian Zhang

{"title":"Zoning out in health management: Exploring information non-behaviour among older Chinese immigrants with diabetes in Canada","authors":"Xiaoqian Zhang","doi":"10.1016/j.ipm.2025.104386","DOIUrl":"10.1016/j.ipm.2025.104386","url":null,"abstract":"<div><div>Current healthcare practices often assume that individuals actively seek and use health information; however, little is known about why some people do not engage in these behaviours. To address this gap, our study aims to explore information non-behaviour and examine the reasons for disengagement. A qualitative approach was employed, involving recruiting 20 participants through snowball sampling. The participants were older Chinese immigrants with diabetes in Canada. Data were collected through semi-structured, one-on-one interviews and analyzed using reflexive thematic analysis. The study revealed that participants disengaged from active information behaviour by avoiding new information, passively accepting or ignoring what they encountered, failing to apply or critically assess it, and seldom sharing it with others. This non-behaviour was influenced by contextual factors (e.g., environmental barriers, access challenges, and the impact of the COVID-19 pandemic), social factors (e.g., viewing diabetes as a non-topic, social support networks, and doctor-patient interactions), and individual factors (e.g., health condition, coping mechanisms, perceived usefulness of information, and health literacy). The findings highlight the complexity of human behaviour and point to new avenues for future research. Practically, this research contributes to designing patient-centred educational programs, developing accessible and relevant communication tools, and fostering environments that encourage active information engagement.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 2","pages":"Article 104386"},"PeriodicalIF":6.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-transformer learning network for abnormal crowd human behavior detection from UAV captured images 基于交叉变换学习网络的无人机捕获图像异常人群行为检测

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2025-09-01 DOI: 10.1016/j.ipm.2025.104374

Min Zhu , Dengyin Zhang

{"title":"Cross-transformer learning network for abnormal crowd human behavior detection from UAV captured images","authors":"Min Zhu , Dengyin Zhang","doi":"10.1016/j.ipm.2025.104374","DOIUrl":"10.1016/j.ipm.2025.104374","url":null,"abstract":"<div><div>The detection of abnormal behavior in public environments is crucial for maintaining public safety and optimizing surveillance systems. With the growing deployment of unmanned aerial vehicles (UAVs) for aerial monitoring, accurately identifying abnormal crowd behavior from UAV-captured images has become a significant challenge due to occlusions, high-density scenes, and limited spatial resolution. Traditional approaches struggle with real-time adaptability and accuracy under these complex conditions. Hence, the research proposes a Cross-Transformer Learning Network that integrates spatio-temporal attention mechanisms and dynamic boundary adaptation to enhance anomaly detection in UAV surveillance data. The novel model enables pattern boundary cross-matching and feature distributions to accurately identify behavioral anomalies across high-density and occluded environments. The model iteratively ines the learned representations until the maximum responsive pixel region is identified, effectively minimizing variations, boundary detection, and pattern extraction. The model retains critical spatial-temporal correlations across frames and improves the detection of nuanced abnormalities. Through training input correlations, precise patterns are identified for the object/human/crowd boundaries to detect abnormalities. Experiments conducted on benchmark datasets, such as UCSD and Abnormal High-Density Crowds, show that the suggested approach significantly outperforms conventional models, including ConvLSTM and Hidden Markov Models (HMM). In particular, it achieves an accuracy gain of 12.31 % and a recall increase of 13.09 %, thereby emphasizing its implementation in challenging UAV surveillance scenarios. The proposed framework addresses a crucial gap in UAV-based surveillance by offering a scalable and highly precise method for detecting abnormal human behavior in complex environments, thereby paving the way for a more responsive and intelligent public safety monitoring system.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 2","pages":"Article 104374"},"PeriodicalIF":6.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144921827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning hierarchical time–frequency representation for long-term time series forecasting 学习长期时间序列预测的分层时频表示

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2025-09-01 DOI: 10.1016/j.ipm.2025.104358

Zhongju Wang , Zhenhong Sun , Yatao Bian , Huadong Mo , Daoyi Dong

引用次数: 0

Measuring policy diffusion intensity: A text-driven analysis of government documents 衡量政策扩散强度：政府文件的文本驱动分析

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2025-08-30 DOI: 10.1016/j.ipm.2025.104383

Jinglong Chen , Junyi Wen , Yufeng Deng , Mingwen Chen , Feicheng Ma

{"title":"Measuring policy diffusion intensity: A text-driven analysis of government documents","authors":"Jinglong Chen , Junyi Wen , Yufeng Deng , Mingwen Chen , Feicheng Ma","doi":"10.1016/j.ipm.2025.104383","DOIUrl":"10.1016/j.ipm.2025.104383","url":null,"abstract":"<div><div>Accurately measuring policy diffusion intensity is crucial for understanding innovation dissemination mechanisms, yet existing approaches face limitations in capturing its dynamic nature. By applying text-driven methods to government documents, this research constructs a two-dimensional quantitative indicator that integrates both hierarchical effectiveness and textual intensity to capture policy diffusion dynamics. Using the panel data analysis of 9091 government documents from 2007 to 2022, we systematically examine the diffusion mechanisms of China's low-carbon policies. Our findings reveal that learning, imitation, and coercion are the primary mechanisms driving the intensity of low-carbon policy diffusion, while economic competition plays an insignificant role. Furthermore, urban carbon emission levels and public environmental awareness promote policy diffusion, whereas energy consumption dependency inhibits it. By demonstrating an effective application of text mining techniques in measuring policy diffusion intensity, this study provides new methodological and empirical insights for understanding policy diffusion within multi-level governance systems.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 2","pages":"Article 104383"},"PeriodicalIF":6.9,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The impact of disclosed status capital on health knowledge provider performance in common Q&A platforms: Capturing the dilution effect of platform certification 公共问答平台中披露状态资本对健康知识提供者绩效的影响：捕捉平台认证的稀释效应

IF 6.9 1区管理学

Information Processing & Management Pub Date : 2025-08-29 DOI: 10.1016/j.ipm.2025.104372

Mohan Wang, Fei Wan, Sishi Tang, Yuwei Zhang

{"title":"The impact of disclosed status capital on health knowledge provider performance in common Q&A platforms: Capturing the dilution effect of platform certification","authors":"Mohan Wang, Fei Wan, Sishi Tang, Yuwei Zhang","doi":"10.1016/j.ipm.2025.104372","DOIUrl":"10.1016/j.ipm.2025.104372","url":null,"abstract":"<div><div>Online Q&A platforms have become increasingly important channels for health information exchange, distinguished from professional online health communities (OHCs) by their flexible identity disclosure mechanisms. While OHCs require standardized credential displays, Q&A platforms allow knowledge providers to strategically self-disclose their professional status through usernames while also undergoing platform certification. Although existing research has examined the effects of status capital in structured OHCs, little attention has been paid to how health knowledge providers’ self-disclosed status capital influences online consultation performance on Q&A platforms and whether a dilution effect exists when the platform certification coexists. To address these issues, we construct a zero-inflated model using a unique panel dataset from a leading online Q&A platform in China and its paid consultation service. The results show that (1) providers’ self-disclosed status capital significantly improves their consultation performance; (2) this relationship is mediated by an inverted U-shaped pricing mechanism; and (3) platform certification paradoxically weakens the positive impact of self-disclosed status, which demonstrates a clear dilution effect. Robustness checks confirm these patterns across alternative model specifications. By uncovering the nuanced dynamics of providers’ disclosed status capital and their performance, this study contributes to a deeper understanding of health knowledge consumer behavior in health communities within common Q&A contexts and provides practical insights for optimizing information presentation strategies within the industry.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 2","pages":"Article 104372"},"PeriodicalIF":6.9,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144912614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0