Decision Support Systems最新文献_第7页

A cross-platform rumor detection framework considering data privacy protection and different detection capabilities of online social platforms 一个考虑数据隐私保护和网络社交平台不同检测能力的跨平台谣言检测框架

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-08-28 DOI: 10.1016/j.dss.2025.114524

Xuelong Chen , Jinchao Pan

The anonymity and widespread popularity of online social platforms (OSPs) allow users to share uncertain posts freely, leading to numerous rumors. Similar rumors spread widely across OSPs, resulting in frequent cross-platform rumors (CPRs). Owing to the unique nature of the cross-platform spread, the dual challenges of data privacy protection constraints and differences in the data and detection capabilities of OSPs exacerbate the difficulty of CPR detection. Thus, to detect CPRs effectively, we designed and implemented a novel deep learning framework named Cross Platform Rumor Detection based on Improved Federated Learning (CPRDIFL), which integrates and improves federated learning and the pre-trained Masked and Contextualized BERT (MacBERT). Our framework uses FL to analyze data from OSPs independently, thus avoiding the need for data integration and ensuring the data privacy protection of OSPs. Moreover, MacBERT is deployed on the clients of CPRDIFL to extract contextual features from posts and dynamically update local weights based on the data and detection performance. Weight parameters are dynamically shared between clients and servers and between clients to achieve complementary advantages across OSPs. Our framework was used in six comprehensive experiments in different scenarios, and the experimental results showed that it achieved the best results in CPR detection. This study not only provides an effective solution for CPR detection but also marks a significant step toward the automated detection of cross-OSP information pollution.

网络社交平台的匿名性和广泛普及使得用户可以自由地分享不确定的帖子，从而导致大量谣言。类似的谣言在各个osp中广泛传播，导致跨平台谣言（cpr）频繁出现。由于跨平台传播的独特性，数据隐私保护约束的双重挑战以及各平台数据和检测能力的差异加剧了心肺复苏检测的难度。因此，为了有效地检测谣言，我们设计并实现了一种新的深度学习框架，称为基于改进联邦学习的跨平台谣言检测（CPRDIFL），该框架集成并改进了联邦学习和预训练的蒙面和情境化BERT （MacBERT）。我们的框架使用FL对来自osp的数据进行独立分析，从而避免了数据集成的需要，保证了osp的数据隐私保护。此外，在CPRDIFL的客户端部署MacBERT，从帖子中提取上下文特征，并根据数据和检测性能动态更新局部权重。权重参数在客户端和服务器之间以及客户端之间动态共享，实现跨osp优势互补。我们的框架在不同场景下进行了6次综合实验，实验结果表明，该框架在心肺复苏检测中取得了最好的效果。本研究不仅为CPR检测提供了有效的解决方案，而且标志着跨osp信息污染的自动化检测迈出了重要的一步。

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引用次数: 0

How manipulating information affects information diffusion during disasters: The effects of modifying falsehoods versus corrections 在灾难中操纵信息如何影响信息扩散：修改虚假与更正的效果

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-08-13 DOI: 10.1016/j.dss.2025.114523

Kelvin K. King

Information evolves as it is disseminated on social media. However, studies have largely overlooked a major aspect of the diffusion process: how information is modified, the various dimensions of these modifications, and their roles in the diffusion process. To fill these research gaps, we utilize the Information Manipulation Theory (IMT) as a theoretical lens and a unique panel dataset of 71 falsehoods, propagated during five disasters, to investigate how modifying information affects its diffusion. Our exploratory analysis suggests that at least 65 % of the messages shared are half-truths. Although falsehoods had a higher modification rate for the first 700 h, corrections were modified more aggressively and for 100 h longer after that period, owing to competition. Our empirical analysis suggests that modified information, i.e., information that includes unrelated responses such as deflections, self-referents, additional details, and more information, is generally shared more frequently than unmodified information.

Furthermore, for falsehoods, a one-unit increase in these modifications increases diffusion; however, when manner and quantity modifications increase by one unit for corrections, sharing increases by 115.1 % and 102.2 %, respectively. Although relation modifications from corrections cause an over 149 % increase in sharing at the information diffusion introduction stages, they do not occur in the maturity and decline stages, and are counterproductive in the growth stages. We also find that negatively charged corrections stimulate virality more than positive ones.

These findings have important implications for researchers and decision-makers.

信息随着在社交媒体上的传播而演变。然而，研究在很大程度上忽视了传播过程的一个主要方面：信息是如何被修改的，这些修改的各个方面，以及它们在传播过程中的作用。为了填补这些研究空白，我们利用信息操纵理论（IMT）作为理论透镜和在五次灾难中传播的71个虚假信息的独特面板数据集，来研究修改信息如何影响其传播。我们的探索性分析表明，至少65%的分享信息是半真半假的。虽然谎言在前700小时有较高的修改率，但由于竞争，在此之后更正的修改更积极，并且持续时间更长100小时。我们的实证分析表明，修改后的信息，即包含不相关反应的信息，如偏转、自我指涉、附加细节和更多信息，通常比未修改的信息共享得更频繁。此外，对于谎言，这些修改每增加一个单位，就会增加传播；然而，当修正的方式和数量增加一个单位时，共享分别增加115.1%和102.2%。虽然修正带来的关系修正在信息扩散引入阶段会使共享增加149%以上，但在成熟期和衰退期不会发生，在成长阶段会产生反效果。我们还发现，带负电荷的修正比带正电荷的修正更能刺激病毒式传播。这些发现对研究人员和决策者具有重要意义。

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引用次数: 0

Data disclosure strategy: Navigating the balance between privacy and profit in a dynamic system 数据披露策略：动态系统中隐私与利润的平衡

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-08-11 DOI: 10.1016/j.dss.2025.114510

Cheng-Han Wu

Digital platforms play a crucial role in our interconnected society, relying on user-disclosed data to enhance advertising revenue and user experiences and provide free services. While data accumulation benefits both platforms and users, it raises privacy concerns. This study explores the interaction between user data disclosure strategies and profitability for a platform and a developer, considering three strategies: mandatory data disclosure with free-to-use, mandatory disclosure with pay-to-use, and user-selective disclosure, allowing payment without data sharing. We formulate a dynamic optimization problem to capture how user data accumulation evolves and influences firm decisions. This framework also degenerates into a static setting for comparison, allowing us to assess the impact of dynamic evolution. Our findings reveal that while static models favor payment-based strategies, dynamic models entail a transition from a free-to-use model, facilitating early-stage data accumulation, to a selective disclosure model that balances privacy concerns and profitability. These findings offer guidance for managers in developing adaptive data disclosure strategies that optimize profitability while addressing user privacy concerns.

数字平台在我们这个互联的社会中扮演着至关重要的角色，依靠用户披露的数据来提高广告收入和用户体验，并提供免费服务。虽然数据积累对平台和用户都有利，但它引发了隐私问题。本研究探讨了用户数据披露策略与平台和开发商盈利能力之间的相互作用，考虑了三种策略：免费使用的强制性数据披露，付费使用的强制性数据披露，以及用户选择性披露，允许在不共享数据的情况下付费。我们制定了一个动态优化问题来捕捉用户数据积累如何演变和影响公司决策。这个框架也退化为一个静态的比较设置，允许我们评估动态进化的影响。我们的研究结果表明，静态模式有利于基于付费的策略，而动态模式则需要从免费使用模式（促进早期数据积累）过渡到平衡隐私问题和盈利能力的选择性披露模式。这些发现为管理人员开发适应性数据披露策略提供了指导，这些策略可以在解决用户隐私问题的同时优化盈利能力。

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引用次数: 0

A friend or a foe? The effect of generative artificial intelligence on creator contributions on original work sharing platforms 朋友还是敌人？生成式人工智能对原创作品分享平台创作者贡献的影响

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-08-07 DOI: 10.1016/j.dss.2025.114513

Shan Liu , Wenxuan Hu , Baojun Gao

While generative artificial intelligence (GAI) is increasingly used to create content, it is often criticized for collecting and training private data and induces potential copy infringement issue. This dilemma leaves a question of whether GAI increases or decreases creators' work sharing. Drawn on protection motivation theory, this study examines how the launch of a GAI system affects creators' contributions on an original work sharing platform. We discover that GAI poses a threat to drawing-category creators, leading to a significant crowding-out effect on their contributions. Specifically, compared with that of non-drawing-category creators, the work sharing of drawing-category creators decreases by 19.64 % and 14.29 % within a short period after the launch and removal of the GAI system, respectively. We discover that creators' protective behavior is driven by GAI-related copyright infringement. Compared with creators without copyright protection, those with copyright protection are more inclined to cease contributions or even leave the platform. We further find that among copyright-protected creators, top creators, evidenced by their acquisition of a large number of supporters or platform honor titles, exhibit more pronounced responses to protect their works due to their higher coping efficacy. Notably, this threat reduces creators' sharing behavior or even lead to their exit from the platform. Nevertheless, such reduction is likely to gradually recover once the threat subsides. Overall, our findings have important implications for whether and how platform managers adopt GAI systems, especially in an original work sharing context.

虽然生成式人工智能（GAI）越来越多地用于创建内容，但它经常因收集和训练私人数据而受到批评，并引发潜在的复制侵权问题。这种困境带来了一个问题，即GAI是增加还是减少了创作者的作品分享。本研究运用保护动机理论，考察GAI制度的推出对创作者在原创作品分享平台上的贡献有何影响。我们发现GAI对绘画类创作者构成了威胁，导致他们的贡献受到明显的挤出效应。具体而言，与非绘图类创作者相比，在GAI系统上线和下线后的短时间内，绘图类创作者的作品分享率分别下降了19.64%和14.29%。我们发现创作者的保护行为是由与人工智能相关的版权侵权驱动的。与没有版权保护的创作者相比，有版权保护的创作者更倾向于停止创作甚至离开平台。我们进一步发现，在受版权保护的创作者中，获得大量支持者或平台荣誉称号的顶级创作者由于其更高的应对效能而表现出更明显的保护作品的反应。值得注意的是，这种威胁减少了创作者的分享行为，甚至导致他们退出平台。然而，一旦威胁消退，这种减少可能会逐渐恢复。总的来说，我们的发现对于平台管理者是否以及如何采用GAI系统具有重要意义，特别是在原始工作共享环境中。

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引用次数: 0

Exploring users' post-adoption use of generative AI: An attitudinal ambivalence perspective 探索用户采用生成式人工智能后的使用：态度矛盾的观点

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-08-05 DOI: 10.1016/j.dss.2025.114521

Jing Zhang , Zhen Shao , Lin Zhang , Jose Benitez

As generative AI (genAI) has advanced, the intricate interplay of its technical potential and ethical perils has become more pronounced, fostering a growing ambivalence in users' attitudes towards genAI technology. Drawing upon the attitudinal ambivalence perspective (i.e., the simultaneous occurrence of positive and negative evaluations of genAI use) and cognitive appraisal theory of emotion, our study proposes and tests an integrative research model to understand how users' attitudinal ambivalence towards genAI technology navigates their negative and positive emotional responses and shapes their post-adoption behaviors. We surveyed 530 genAI users and employed the structural equation modeling approach to test our research model. We find that attitudinal ambivalence is significantly associated with users' extended use and avoidance through the mediation of user trust and fear. Additionally, transparency significantly moderates the effects of attitudinal ambivalence on user trust and fear. Our study advances nature and consequences of attitudinal ambivalence towards genAI and provides insights for practitioners contemplating deploying genAI.

随着生成式人工智能（genAI）的发展，其技术潜力和伦理风险之间错综复杂的相互作用变得更加明显，导致用户对基因人工智能技术的态度日益矛盾。基于态度矛盾观（即对基因人工智能的正面和负面评价同时出现）和情绪认知评价理论，本研究提出并检验了一个综合研究模型，以了解用户对基因人工智能技术的态度矛盾如何引导他们的消极和积极情绪反应，并影响他们的采用后行为。我们调查了530名genAI用户，并采用结构方程建模方法对我们的研究模型进行了测试。研究发现，通过用户信任和恐惧的中介，态度矛盾心理与用户的扩展使用和回避显著相关。此外，透明度显著调节态度矛盾心理对用户信任和恐惧的影响。我们的研究推进了对基因人工智能态度矛盾的本质和后果，并为考虑部署基因人工智能的从业者提供了见解。

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引用次数: 0

Blockchain-based token system for incentivizing peer review: A design science approach 基于区块链的激励同行评审的代币系统：一种设计科学方法

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-31 DOI: 10.1016/j.dss.2025.114514

Chad Anderson , Pratiksha Shrestha , Suman Bhunia , Arthur Carvalho , Younghwa Lee

Peer review is an essential component of the evaluation and dissemination of new scientific knowledge. The peer review process can be viewed as a decision support framework relying on scholarly review systems, where decision-makers (editors) solicit input from experts (reviewers) to make editorial decisions on submitted manuscripts. Unfortunately, the challenges editors face in securing sufficient reviewers are well-documented, leading to prolonged review times and potentially diminished review quality. We explore and validate this trend through a literature review and interviews with scholars. We then employ a design science research methodology to design, develop, and evaluate potential incentive mechanisms to reverse that trend. In addition to proposing formal design principles that such mechanisms should follow, we suggest a concrete blockchain-based token system that enables editors to offer review incentives while enabling reviewers to flexibly utilize these incentives to meet their needs. We also explain how different types of tokens can be connected to practical submission and reward policies that journals may adopt. Our cost analysis, along with a survey-based field study and qualitative interviews with academics, highlight the effectiveness of our solution. Finally, we propose a formal design theory framework that designers of peer review systems can follow to create meaningful incentives to attract reviewers.

同行评议是评价和传播新科学知识的重要组成部分。同行评议过程可以被视为一个依赖学术评议系统的决策支持框架，其中决策者（编辑）征求专家（审稿人）的意见，以对提交的手稿做出编辑决定。不幸的是，编辑在确保足够的审稿人方面面临的挑战是充分记录的，这会导致审稿时间延长，并潜在地降低审稿质量。我们通过文献综述和对学者的访谈来探索和验证这一趋势。然后，我们采用设计科学研究方法来设计、开发和评估潜在的激励机制，以扭转这一趋势。除了提出这些机制应该遵循的正式设计原则外，我们还建议一个具体的基于区块链的令牌系统，使编辑能够提供审查激励，同时使审稿人能够灵活地利用这些激励来满足他们的需求。我们还解释了不同类型的代币如何与期刊可能采用的实际提交和奖励政策相关联。我们的成本分析，以及基于调查的实地研究和与学者的定性访谈，突出了我们解决方案的有效性。最后，我们提出了一个正式的设计理论框架，同行评议系统的设计者可以遵循这个框架来创建有意义的激励机制来吸引评议者。

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引用次数: 0

How does AI-assisted diagnosis decision support systems influence doctors' coping styles and work outcomes? Bright and dark sides of AI in the workplace 人工智能辅助诊断决策支持系统如何影响医生的应对方式和工作成果？人工智能在工作场所的光明面和阴暗面

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-28 DOI: 10.1016/j.dss.2025.114512

Zhaohua Deng , Dan Song , Shan Liu

Artificial intelligence (AI), specifically AI-assisted diagnosis decision support systems (DSSs), have been integrated into doctors' work in substituted or complementary ways. From the perspective of doctors, the impact of AI roles on work outcomes is a double-edged sword that may induce both positive and negative consequences and even create ethical issues related to work. However, little is known on why and how the dual effects take place. To address this knowledge gap, we draw on coping theory and explore the roles of AI-assisted diagnosis DSSs in doctors' work meaningfulness and core work capability through their coping style. We employ a sequential mixed-methods design to develop a theoretical framework and test the research model. Results indicate that perceived complementation and substitution for non-core tasks are positively associated with work specialization (bright side), promoting work meaningfulness and core work capability. By contrast, perceived substitution for core tasks is positively associated with a threat to human distinctiveness (dark side), which harms work meaningfulness and core work capability. Our findings contribute to the emerging literature on AI's impact in the doctors' workplace and provide ethical suggestions for practitioners.

人工智能（AI），特别是人工智能辅助诊断决策支持系统（dss），已经以替代或补充的方式融入到医生的工作中。从医生的角度来看，人工智能角色对工作结果的影响是一把双刃剑，可能会产生积极和消极的后果，甚至会产生与工作相关的伦理问题。然而，人们对这种双重效应发生的原因和方式知之甚少。为了解决这一知识缺口，我们借鉴应对理论，通过应对方式探讨人工智能辅助诊断DSSs在医生工作意义和核心工作能力中的作用。我们采用顺序混合方法设计来建立理论框架并检验研究模型。结果表明，对非核心任务的补充和替代感知与工作专业化（积极面）、工作意义和核心工作能力呈正相关。相反，核心任务的感知替代与对人类独特性（黑暗面）的威胁呈正相关，这损害了工作的意义和核心工作能力。我们的研究结果为人工智能对医生工作场所的影响的新兴文献做出了贡献，并为从业者提供了道德建议。

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引用次数: 0

Boosting the underdogs: Unraveling how prevailing streamer visits drive revenue for emerging streamers on livestreaming entertainment platforms 推动弱者：揭示主流流媒体访问如何推动直播娱乐平台上新兴流媒体的收入

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-25 DOI: 10.1016/j.dss.2025.114511

Huijing Guo , Xin Bao , Le Wang , Xin (Robert) Luo

Livestreaming entertainment (LSE) platforms have become increasingly popular for real-time social interaction. While high-status actors (prevailing streamers) attract large audiences, new streamers often struggle with visibility and earnings. This study examines how social capital transmission from high-status actors affect emerging streamers' live revenue, using Social Capital Theory and Arousal Theories as frameworks. We analyzed data from 52,010 emerging streamers over two weeks on a major LSE platform. The research shows that visits from established streamers significantly increase new streamers' revenue. This positive effect is notably stronger when new streamers have shown good past performance and belong to top guilds and visiting established streamers have strong performance records and actively interact during their visits. Our findings contribute to LSE platform research by highlighting the supportive role of established streamers. These insights can help platforms develop strategies to enhance platform vitality, diversify content, support emerging streamers' growth, and foster a more sustainable streaming ecosystem.

直播娱乐（LSE）平台在实时社交互动方面越来越受欢迎。虽然高地位的演员（流行的流媒体）吸引了大量的观众，但新的流媒体经常在知名度和收入方面挣扎。本研究以社会资本理论和激励理论为框架，探讨了社会资本传播对新兴流媒体直播收入的影响。我们在LSE的一个主要平台上分析了两周内来自52010个新兴流媒体的数据。研究表明，来自老牌主播的访问量显著增加了新主播的收入。当新的主播过去表现良好，并且属于顶级公会，并且访问的老牌主播有良好的表现记录并在访问期间积极互动时，这种积极的影响就会明显增强。我们的研究结果通过强调知名主播的支持作用，为伦敦政治经济学院的平台研究做出了贡献。这些见解可以帮助平台制定战略，增强平台活力，使内容多样化，支持新兴流媒体的发展，并培养一个更可持续的流媒体生态系统。

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引用次数: 0

Sentiment-aware cross-modal semantic interaction model for harmful meme detection 有害模因检测的情感感知跨模态语义交互模型

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-25 DOI: 10.1016/j.dss.2025.114509

Yuxiao Duan, Xiang Zhao, Hao Guo

The increasing proliferation of harmful memes has a serious negative impact on society, rendering the detection of such memes a formidable challenge. Prior research has predominantly concentrated on the modal and semantic attributes of memes while neglecting the significance of cross-modal interactions and detailed semantic information. Although some approaches have incorporated large language models, they often have the problem of harmful avoidance due to ethical constraints. To address these issues, we propose a novel sentiment-aware cross-modal semantic interaction detector, which delves into the profound implications through three principal dimensions: semantic extraction, modal interaction, and sentiment polarity assessment. In the semantic extraction module, Visual Question-Answering is utilized to incorporate detailed knowledge and descriptions. For modal interaction, the positional relationships between meme objects and texts are investigated, and a distance-based attentional multimodal detector is established. In the sentiment polarity module, the sentiment polarity of the text is judged. These components are integrated to form a cohesive joint detection system. Extensive experiments across three benchmark datasets demonstrate SSID significantly outperforms state-of-the-art baselines, enhancing detection accuracy and exhibiting robustness.

有害模因的日益泛滥对社会产生了严重的负面影响，对这些模因的检测是一项艰巨的挑战。以往的研究主要集中在模因的模态和语义属性上，而忽视了模因跨模态交互作用和详细语义信息的重要性。尽管一些方法结合了大型语言模型，但由于伦理约束，它们往往存在有害回避的问题。为了解决这些问题，我们提出了一种新的情感感知跨模态语义交互检测器，该检测器通过三个主要维度：语义提取、模态交互和情感极性评估来深入研究其深远影响。在语义提取模块中，采用可视化问答的方式，将详细的知识和描述融合在一起。对于模态交互，研究模因对象与文本之间的位置关系，建立基于距离的注意多模态检测器。在情感极性模块中，判断文本的情感极性。这些组件被整合成一个有凝聚力的联合检测系统。在三个基准数据集上进行的广泛实验表明，SSID显著优于最先进的基线，提高了检测精度并表现出鲁棒性。

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引用次数: 0

Sparse-enhanced additive interaction neural network for interpretable credit decision 可解释信用决策的稀疏增强加性交互神经网络

IF 6.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems

Pub Date : 2025-07-22 DOI: 10.1016/j.dss.2025.114507

Xingyu Lan , Hong Fan , Wanan Liu , Meng Xia , Kai Guo

Intelligent credit decision systems are crucial for financial institutions’ risk management, aiming to mitigate credit risk. While deep learning models offer high predictive accuracy, their opacity hinders decision support. Neural Additive Models (NAMs) offer feature-level interpretability but fail to capture complex interactions among credit risk factors. To enhance both accuracy and interpretability, we propose the Sparse-Enhanced Additive Interaction Neural Network (SAINTNet) for explainable credit scoring. SAINTNet advances NAM’s framework with dual-node additive modules and adaptive sparse feature selection, enabling autonomous feature learning. Leveraging entmax sparsity and optimized temperature settings, SAINTNet: (1) maintains interpretability, particularly for credit feature interactions; (2) achieves superior accuracy compared to black-box models. Experiments on four credit datasets demonstrate SAINTNet’s superior performance and systematic interpretability through global feature importance, local decision analysis, and interaction visualization, improving decision audits in high-risk credit scenarios.

智能信贷决策系统是金融机构风险管理的关键，其目的是降低信贷风险。虽然深度学习模型提供了很高的预测准确性，但它们的不透明性阻碍了决策支持。神经加性模型（NAMs）提供特征级的可解释性，但无法捕获信用风险因素之间复杂的相互作用。为了提高准确性和可解释性，我们提出了稀疏增强的加性交互神经网络（SAINTNet）用于可解释的信用评分。SAINTNet通过双节点加性模块和自适应稀疏特征选择改进了NAM框架，实现了自主特征学习。利用entmax稀疏性和优化的温度设置，SAINTNet:(1)保持可解释性，特别是对于信用特征交互；(2)与黑箱模型相比，精度更高。在四个信用数据集上进行的实验表明，SAINTNet通过全局特征重要性、局部决策分析和交互可视化等方法，具有卓越的性能和系统的可解释性，改善了高风险信用场景下的决策审计。

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引用次数: 0