Decision Support Systems最新文献_第2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

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

Xuelong Chen , Jinchao Pan

{"title":"A cross-platform rumor detection framework considering data privacy protection and different detection capabilities of online social platforms","authors":"Xuelong Chen , Jinchao Pan","doi":"10.1016/j.dss.2025.114524","DOIUrl":"10.1016/j.dss.2025.114524","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"198 ","pages":"Article 114524"},"PeriodicalIF":6.8,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Kelvin K. King

{"title":"How manipulating information affects information diffusion during disasters: The effects of modifying falsehoods versus corrections","authors":"Kelvin K. King","doi":"10.1016/j.dss.2025.114523","DOIUrl":"10.1016/j.dss.2025.114523","url":null,"abstract":"<div><div>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.</div><div>Furthermore, for falsehoods, a one-unit increase in these modifications increases diffusion; however, when <em>manner</em> and <em>quantity</em> modifications increase by one unit for corrections, sharing increases by 115.1 % and 102.2 %, respectively. Although <em>relation</em> 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.</div><div>These findings have important implications for researchers and decision-makers.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"197 ","pages":"Article 114523"},"PeriodicalIF":6.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Cheng-Han Wu

{"title":"Data disclosure strategy: Navigating the balance between privacy and profit in a dynamic system","authors":"Cheng-Han Wu","doi":"10.1016/j.dss.2025.114510","DOIUrl":"10.1016/j.dss.2025.114510","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"197 ","pages":"Article 114510"},"PeriodicalIF":6.8,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A 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

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

Shan Liu , Wenxuan Hu , Baojun Gao

{"title":"A friend or a foe? The effect of generative artificial intelligence on creator contributions on original work sharing platforms","authors":"Shan Liu , Wenxuan Hu , Baojun Gao","doi":"10.1016/j.dss.2025.114513","DOIUrl":"10.1016/j.dss.2025.114513","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"197 ","pages":"Article 114513"},"PeriodicalIF":6.8,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144828542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Jing Zhang , Zhen Shao , Lin Zhang , Jose Benitez

{"title":"Exploring users' post-adoption use of generative AI: An attitudinal ambivalence perspective","authors":"Jing Zhang , Zhen Shao , Lin Zhang , Jose Benitez","doi":"10.1016/j.dss.2025.114521","DOIUrl":"10.1016/j.dss.2025.114521","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"197 ","pages":"Article 114521"},"PeriodicalIF":6.8,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Blockchain-based token system for incentivizing peer review: A design science approach","authors":"Chad Anderson , Pratiksha Shrestha , Suman Bhunia , Arthur Carvalho , Younghwa Lee","doi":"10.1016/j.dss.2025.114514","DOIUrl":"10.1016/j.dss.2025.114514","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"197 ","pages":"Article 114514"},"PeriodicalIF":6.8,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Zhaohua Deng , Dan Song , Shan Liu

{"title":"How does AI-assisted diagnosis decision support systems influence doctors' coping styles and work outcomes? Bright and dark sides of AI in the workplace","authors":"Zhaohua Deng , Dan Song , Shan Liu","doi":"10.1016/j.dss.2025.114512","DOIUrl":"10.1016/j.dss.2025.114512","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"197 ","pages":"Article 114512"},"PeriodicalIF":6.8,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144749732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

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

{"title":"Boosting the underdogs: Unraveling how prevailing streamer visits drive revenue for emerging streamers on livestreaming entertainment platforms","authors":"Huijing Guo , Xin Bao , Le Wang , Xin (Robert) Luo","doi":"10.1016/j.dss.2025.114511","DOIUrl":"10.1016/j.dss.2025.114511","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"197 ","pages":"Article 114511"},"PeriodicalIF":6.8,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

Yuxiao Duan, Xiang Zhao, Hao Guo

{"title":"Sentiment-aware cross-modal semantic interaction model for harmful meme detection","authors":"Yuxiao Duan, Xiang Zhao, Hao Guo","doi":"10.1016/j.dss.2025.114509","DOIUrl":"10.1016/j.dss.2025.114509","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"196 ","pages":"Article 114509"},"PeriodicalIF":6.8,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

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

{"title":"Sparse-enhanced additive interaction neural network for interpretable credit decision","authors":"Xingyu Lan , Hong Fan , Wanan Liu , Meng Xia , Kai Guo","doi":"10.1016/j.dss.2025.114507","DOIUrl":"10.1016/j.dss.2025.114507","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"197 ","pages":"Article 114507"},"PeriodicalIF":6.8,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0