Information Processing & Management最新文献

{"title":"Managing rumors on electronic interaction platforms: How management responses affect investor reaction","authors":"Runyu Wang ,&nbsp;Zili Zhang ,&nbsp;Keng Leng Siau ,&nbsp;Ziqiong Zhang","doi":"10.1016/j.ipm.2025.104162","DOIUrl":"10.1016/j.ipm.2025.104162","url":null,"abstract":"<div><div>This study investigates how listed firms respond to investors’ rumor-related inquiries and examines the impact of these responses on investor reactions, as indicated by subsequent daily abnormal stock returns (ARs). Using a unique dataset of question-and-answer (Q&amp;A) interactions from China’s major e-interaction platforms, established by the stock exchanges, our study provides insights into regulated firm-investor communications in a structured Q&amp;A setting. Unlike informal social media channels, these platforms enable official responses from firm representatives, typically board secretaries, under direct regulatory oversight. By analyzing rumor-related Q&amp;A pairs with regression models and several robustness checks, we find that firms can benefit from strategic response messaging. Our results show that prompt responses from management have a positive effect on the stock market. Interestingly, responses that closely match the questions do not necessarily lead to improved ARs. Responses that convey more positive emotions are associated with higher subsequent gains in ARs. Further, we investigate the role of specific conditions, including prior stock performance, investor sentiment in questions, and question topics, in the relationship between response behavior and investor reaction. These findings offer new insights into the consequences of management responses to unconfirmed events and highlight the potential value of Q&amp;A communication between firm managers and potential investors.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 5","pages":"Article 104162"},"PeriodicalIF":7.4,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768225","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}

{"title":"Integrative modeling enables ChatGPT to achieve average level of human counselors performance in mental health Q&A","authors":"Yinghui Huang ,&nbsp;Weijun Wang ,&nbsp;Jinyi Zhou ,&nbsp;Liang Zhang ,&nbsp;Jionghao Lin ,&nbsp;Hui Liu ,&nbsp;Xiangen Hu ,&nbsp;Zongkui Zhou ,&nbsp;Wanghao Dong","doi":"10.1016/j.ipm.2025.104152","DOIUrl":"10.1016/j.ipm.2025.104152","url":null,"abstract":"<div><div>Recent advancements in generative artificial intelligence (GenAI), particularly ChatGPT, have demonstrated significant potential in addressing the persistent treatment gap in mental health care. Systematic evaluation of ChatGPT’s capabilities in addressing mental health questions is essential for its large-scale application. The current study introduces a computational evaluation framework centered on perceived information quality (PIQ) to quantitatively assess ChatGPT’s capabilities. Leveraging datasets of question-answer pairs generated by both humans and ChatGPT, the framework integrates predictive modeling, explainable modeling, and prompt-engineering-based validation to identify intrinsic evaluation metrics and enable automated assessments. Results revealed that unprompted ChatGPT’s PIQ is significantly lower than that of human counselors overall, with notable deficiencies such as insufficient conversational length, lower text diversity, and reduced professionalism. Despite not matching the top 25% of human counselors, our evaluation framework improved ChatGPT’s mean PIQ by 8.91% to 11.67% across four risk levels. Prompted ChatGPT performed comparably to human counselors in severe (<em>p</em> = 0.0561) and moderate-risk questions (<em>p</em> = 0.7851), and significantly outperformed them in low- and no-risk categories by 6.80% and 4.63%, respectively (<em>p</em> &lt; 0.001). However, undesirable verbal behaviors still persist in <em>text diversity</em> and <em>professionalism</em>. These findings validate ChatGPT’s capabilities to address mental health questions while cautioning that further researches are necessary for LLM-based mental health systems to deliver services comparable to human experts.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 5","pages":"Article 104152"},"PeriodicalIF":7.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768224","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}

{"title":"Improving multimodal fake news detection by leveraging cross-modal content correlation","authors":"Jiao Qiao ,&nbsp;Xianghua Li ,&nbsp;Chao Gao ,&nbsp;Lianwei Wu ,&nbsp;Junwei Feng ,&nbsp;Zhen Wang","doi":"10.1016/j.ipm.2025.104120","DOIUrl":"10.1016/j.ipm.2025.104120","url":null,"abstract":"<div><div>The widespread presence of multimodal fake news on social media platforms has severely impacted public order, making the automatic detection and filtering of such content a pressing issue. Although existing studies have attempted to integrate multimodal data for this task, they often struggle to effectively model cross-modal correlations. Most approaches focus on the global features of each modality and compute scalar similarities, which limits their capacity to learn and process comprehensive samples. To address this challenge, this paper introduces a novel cross-modal content correlation network. This method leverages salient objects from images and nouns from the text as the multimodal content, utilizing CLIP to extract generalizable features for similarity measurement, thereby enhancing cross-modal interaction. By applying convolution to the similarity matrix between nouns and image crops, the model captures learnable patterns of cross-modal content correlations that facilitate news classification, without relying on predefined scalar similarities or requiring supplementary information or auxiliary tasks. Experiments on two real-world datasets reveal that our method outperforms previous methods, achieving 3.1% and 1.9% gains in overall accuracy on Weibo and Twitter, respectively. The source code is available at <span><span>https://github.com/cgao-comp/C3N</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 5","pages":"Article 104120"},"PeriodicalIF":7.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759890","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}

{"title":"Bibliometric feature identification and analysis of retracted papers in biomedicine: An interpretable machine learning perspective","authors":"Jiaqi Liu ,&nbsp;Xiaoxue Wang ,&nbsp;Xiao Liang","doi":"10.1016/j.ipm.2025.104176","DOIUrl":"10.1016/j.ipm.2025.104176","url":null,"abstract":"<div><div>Nowadays, paper retraction is becoming a serious problem in academia, particularly in biomedicine. Previous studies in this area have examined various features of retracted papers. Based on these findings, the aim of this paper is to construct a model to predict potential retraction cases, and analyze the retraction features over time and across fields. Specifically, we construct an XGBoost model using 9424 normal and retracted biomedical papers published between the year 1983 and the year 2023 from the Web of Science Core Collection database. This model has an accuracy of 87 %. Nine important features are identified, ranked, and their contributions to the model are discussed using interpretable machine learning techniques. Moreover, heterogeneity analysis by publication year reveals that the importance of these features has changed over time. The generalizability of the model is validated in the field of computer science (98.12 %) and telecommunication (74.92 %). Finally, we analyze the similarities/differences in these features among the three fields. The result of this study confirms the features identified by previous studies. Further, the way that these features describe and predict whether a paper is retracted or not is revealed by interpretable machine learning techniques. This has not been discussed much in previous studies. Additionally, this study provides details on how these features change over time and across disciplines in predicting retractions. Finally, the results of this study may shed some new light on further research. It may also be used as a reference in science policy-making.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 5","pages":"Article 104176"},"PeriodicalIF":7.4,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759889","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}

{"title":"The memory cycle of time-series public opinion data: Validation based on deep learning prediction","authors":"Qing Liu ,&nbsp;Yanfeng Liu ,&nbsp;Hosung Son","doi":"10.1016/j.ipm.2025.104168","DOIUrl":"10.1016/j.ipm.2025.104168","url":null,"abstract":"<div><div>This study explored the response mechanism of time-series public opinion data to sustained external stimuli based on COVID-19 pandemic and online news data (<em>N</em> = 39,753). We employed multiple deep learning models (CNN1D,CNN2D,LSTM,CNN1D-LSTM, and CNN2D-LSTM), using pandemic spread data over varying time windows (window ∈ [5–100]) as input to predict public opinion trends at different lag periods (lag ∈ [1–100]). Subsequently, we investigated the response mechanism of time-series public opinion data to external stimuli by observing the distribution patterns of effective combinations of input windows and lag periods. The findings indicate that statistical data on COVID-19 transmission contain latent knowledge about future public opinion trends. In effective prediction patterns, the size of the data input window and effective lag periods exhibit stable periodicities. This periodicity presents different timescales in the short-, medium-, and long-term, highlighting the public's near-term feedback, ongoing concern, and long-term review regarding major social issues.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 4","pages":"Article 104168"},"PeriodicalIF":7.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747773","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}

{"title":"Struggling or Shifting? Deciphering potential influences of cyberbullying perpetration and communication overload on mobile app switching intention through social cognitive approach","authors":"Hua Pang ,&nbsp;Yu Zhao ,&nbsp;Yilu Yang","doi":"10.1016/j.ipm.2025.104167","DOIUrl":"10.1016/j.ipm.2025.104167","url":null,"abstract":"<div><div>Despite the burgeoning corpus of research examining mobile app switching intention and the expansion of related studies, the specific influences and motivations behind young people's mobile app switching intention caused by environmental factors such as cyberbullying remain underexplored. Drawing on social cognitive perspective, this empirical study establishes a conceptual research model to explore how individual states (depressive mood and mobile app fatigue) mediate environmental factors (cyberbullying perpetration and communication overload) that lead to mobile app switching intention among young generation. Additionally, the study delves into the intricate interplay among these variables. Analysis of data collected from 866 young people demonstrates that cyberbullying perpetration and communication overload are important factors contributing to depressive mood and mobile app fatigue. Furthermore, depressive mood and mobile app fatigue are found to positively influence mobile app switching intention. This research addresses a significant lacuna in our understanding of mobile app switching dynamics within younger demographics, thus providing fresh perspectives on the foundational mechanisms of such behaviors. The insights gleaned from this study not only deepen our understanding from a social cognitive approach but also provide mobile app developers with actionable strategies to enhance app functionalities and foster user retention in an increasingly saturated mobile app marketplace.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 4","pages":"Article 104167"},"PeriodicalIF":7.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747770","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}

{"title":"The attention inequality of scientists: A core-periphery structure perspective","authors":"Haoyang Wang ,&nbsp;Win-bin Huang ,&nbsp;Yi Bu","doi":"10.1016/j.ipm.2025.104170","DOIUrl":"10.1016/j.ipm.2025.104170","url":null,"abstract":"<div><div>This study investigates the dynamics of scientific attention within author citation networks, utilizing the Microsoft Academic Graph dataset. Three author citation networks were constructed within the domains of nanoscience, chemical physics, and human-computer interaction. We apply analytical measurements to reveal core-periphery structures, indicating a growing disparity in scientific interactions. Our analysis highlights a concerning trend: while connections among prominent authors are strengthening, interactions among “ordinary” scientists remain relatively weak. This trend is further corroborated by the application of the network percolation method. After removing the prominent authors in the citation networks, multilayered and complex relationships among authors are revealed. We observe a decreasing trend of connection strength among relatively “ordinary” authors. The observed inequality of attention raises significant concerns about neglecting diverse voices within the scientific community. In response to these phenomena, our research emphasizes the importance of cultivating an inclusive scientific environment for early-career and underrepresented scholars, aiming for long-term sustainability in the scientific community.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 4","pages":"Article 104170"},"PeriodicalIF":7.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747772","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}

{"title":"Unraveling topic switching and innovation in science","authors":"Alex J. Yang","doi":"10.1016/j.ipm.2025.104171","DOIUrl":"10.1016/j.ipm.2025.104171","url":null,"abstract":"<div><div>The selection of research topics shapes both individual scientific trajectories and the broader evolution of knowledge. Despite its critical role, a systematic investigation into the dynamics of topic switching among scientists and its relationship with scientific innovation remains limited. Drawing on a comprehensive dataset encompassing the career trajectories of 1.4 million scientists and 27.6 million publications from 1950 to 2020, I use a field-free and finely-grained framework to quantify shifts in research direction by measuring the knowledge distance between a paper's references and those of prior works. To account for systemic biases, I construct a null model that captures expected patterns of topic selection. My analysis reveals three key findings: (1) Scientists exhibit lower-than-expected levels of topic switching, with a decline before 2000 followed by a rising trend thereafter; (2) Early-career researchers, female scientists, and non-elite scientists demonstrate higher levels of topic switching compared to their counterparts; and (3) Increased topic switching correlates with greater research novelty, interdisciplinarity, and disruptive potential. These findings provide valuable insights into the mechanisms underlying scientific exploration and their implications for innovation, with broad relevance for research policy and talent development.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 4","pages":"Article 104171"},"PeriodicalIF":7.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747771","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}

{"title":"MCCI: A multi-channel collaborative interaction framework for multimodal knowledge graph completion","authors":"Xiquan Zhang ,&nbsp;Jianwu Dang ,&nbsp;Yangping Wang ,&nbsp;Shuyang Li","doi":"10.1016/j.ipm.2025.104156","DOIUrl":"10.1016/j.ipm.2025.104156","url":null,"abstract":"<div><div>Multimodal knowledge graph completion (MKGC) aims to leverage multimodal information to predict missing fact triplets. However, existing MKGC approaches largely ignore the heterogeneity and interaction complexity between modal details, resulting in a lack of balance in the intra- and inter-modal expression. To address the above challenges, we propose a novel multi-channel collaborative interaction (MCCI) framework for MKGC, which is composed of feature encoding, dual-flow alignment, and decision fusion modules. Specifically, in the encoding stage, information filtering and visual enhancement-based methods are used to capture high-quality multimodal features. Furthermore, the dual-flow alignment module expands the potential correlations between different modalities, thereby facilitating the interaction frequency of the information. In the fusion stage, dynamically allocate modality weights and generate prediction outcomes. Experimental results show that compared with the state-of-the-art approaches, the proposed MCCI framework has an improvement of 5.7% and 19.8% in Hits@10 and MR, respectively.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 4","pages":"Article 104156"},"PeriodicalIF":7.4,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747769","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}

{"title":"Heterogeneous network for Hierarchical Fine-Grained Domain Fake News Detection","authors":"Yue Wang ,&nbsp;Shizhong Yuan ,&nbsp;Weimin Li ,&nbsp;Yifan Feng ,&nbsp;Xiao Yu ,&nbsp;Fangfang Liu ,&nbsp;Can Wang ,&nbsp;Quanke Pan","doi":"10.1016/j.ipm.2025.104141","DOIUrl":"10.1016/j.ipm.2025.104141","url":null,"abstract":"<div><div>Fake news on social media has significant negative consequences for individuals and society. However, existing multi-domain detection methods exhibit two primary limitations: dependence on precise domain annotation and inherent bias arising from single-domain categorization. To address these challenges, this paper introduces the Domain-Specific Narrow-Coverage Tree-Based Taxonomy (<span><math><msup><mrow><mtext>DNT</mtext></mrow><mrow><mn>2</mn></mrow></msup></math></span>), which enables more precise domain classification and domain relationship elucidation through refined categories. The constructed dataset is annotated with multiple labels by Large Language Models (LLMs), mitigating reliance on manual efforts and reducing annotation costs while maintaining annotation quality. Furthermore, a Hierarchical Fine-Grained Domain (HFGD) Fake News Detection Method is proposed, which explicitly employs a heterogeneous network to model multi-relationships. This method can mitigate domain bias and comprehensively capture news diversity and domain interactions. Specifically, domain cohesion based on news semantics is designed to reflect the relevance of news within a domain. News items are integrated as intersection nodes into the tree structure of multilevel domains to construct the heterogeneous network. Graph representation learning then fuses directly or indirectly connected news and domain information during feature enhancement. Finally, a composite loss is designed for news and domain node classification. HFGD captures potential differences and commonalities in domains and enhances label adaptation through domain interactions. Experiments on our dataset demonstrate that HFGD outperforms state-of-the-art methods by 1.08% and 0.91% in overall accuracy and macro-F1 score, respectively. Specifically, in the education and military domains with limited sample sizes, HFGD achieves 5.74% and 4.1% improvements in macro-F1 score over the second-best method. The results demonstrate our method’s effectiveness in mitigating domain bias and enhancing detection performance, providing valuable insights for practical multi-domain fake news detection systems.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 4","pages":"Article 104141"},"PeriodicalIF":7.4,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747768","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}