Information Processing & Management最新文献

{"title":"Mechanism of online public opinion formation in major risk events in China: A qualitative comparative analysis","authors":"Bingqin Han ,&nbsp;Shuang Song ,&nbsp;Diyi Liu ,&nbsp;Jiapei Mo","doi":"10.1016/j.ipm.2024.103924","DOIUrl":"10.1016/j.ipm.2024.103924","url":null,"abstract":"<div><div>Understanding societal attitudes toward major risk events after they occur poses a significant challenge for governments. This study employs fuzzy set qualitative comparative analysis (fsQCA) to examine 88 cases of major risk events in China from 2019 to 2023, categorized into natural disasters, accidents, social security threats, and public health crises. We propose an integrated theoretical framework combining information ecology theory and social mentality theory, aiming to uncover the driving pathways that shape positive and negative online social mentalities during these events. The findings reveal the following insights: (1) Media and government significantly influence online community attitudes during major natural disasters. (2) In major accidents, the social environment predominantly shapes stable aspects of societal mentality, yet media and government also adapt dynamically, influencing online societal attitudes accordingly. (3) Major social security events exhibit a diverse trajectory in online social mentality, underscoring the intricate factors affecting public sentiment. The study emphasizes the role of free agents in generating negative online attitudes. (4) During major public health crises, the scale of the event and media coverage exert considerable influence, with media responsiveness varying with shifts in event magnitude. Furthermore, coordinated ecological factors influence the trajectory of online societal attitude changes. These findings offer valuable insights and strategies for managing public opinion during significant risk events.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103924"},"PeriodicalIF":7.4,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417983","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":"Perceptions of Edinburgh: Capturing neighbourhood characteristics by clustering geoparsed local news","authors":"Andreas Grivas ,&nbsp;Claire Grover ,&nbsp;Richard Tobin ,&nbsp;Clare Llewellyn ,&nbsp;Eleojo Oluwaseun Abubakar ,&nbsp;Chunyu Zheng ,&nbsp;Chris Dibben ,&nbsp;Alan Marshall ,&nbsp;Jamie Pearce ,&nbsp;Beatrice Alex","doi":"10.1016/j.ipm.2024.103910","DOIUrl":"10.1016/j.ipm.2024.103910","url":null,"abstract":"<div><div>The communities that we live in affect our health in ways that are complex and hard to define. Moreover, our understanding of the place-based processes affecting health and inequalities is limited. This undermines the development of robust policy interventions to improve local health and well-being.</div><div>News media provides social and community information that may be useful in health studies. Here we propose a methodology for characterising neighbourhoods by using local news articles. More specifically, we show how we can use Natural Language Processing (NLP) to unlock further information about neighbourhoods by analysing, geoparsing and clustering news articles.</div><div>Our work is novel because we combine street-level geoparsing tailored to the locality with clustering of full news articles, enabling a more detailed examination of neighbourhood characteristics. We evaluate our outputs and show via a confluence of evidence, both from a qualitative and a quantitative perspective, that the themes we extract from news articles are sensible and reflect many characteristics of the real world. This is significant because it allows us to better understand the effects of neighbourhoods on health. Our findings on neighbourhood characterisation using news data will support a new generation of place-based research which examines a wider set of spatial processes and how they affect health, enabling new epidemiological research.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103910"},"PeriodicalIF":7.4,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maximizing discrimination masking for faithful question answering with machine reading","authors":"Dong Li,&nbsp;Jintao Tang,&nbsp;Pancheng Wang,&nbsp;Shasha Li,&nbsp;Ting Wang","doi":"10.1016/j.ipm.2024.103915","DOIUrl":"10.1016/j.ipm.2024.103915","url":null,"abstract":"<div><div>Despite recent advancements, like Large Language Models (LLMs), in Question Answering with Machine Reading (QAMR), improving the factuality and faithfulness of QAMR models remains a significant challenge. QAMR models require both language knowledge and world knowledge to answer questions. Language knowledge encompasses syntax, semantics, pragmatics, and other language-specific elements. The extent of language knowledge reflects the model’s language understanding capabilities. World knowledge, which refers to people’s cognition of the world, may be parameterized knowledge of the pre-trained language models or textual knowledge of passages. We conduct a comparative study on these two kinds of knowledge and find that language knowledge is stable, while only part of world knowledge is stable and reliable. This motivates us to utilize textual knowledge of passages and avoid parameterized unstable world knowledge of pre-trained language models for QAMR task. To this end, this paper introduces the concept of <em>Answerable without relying on unstable world knowledge external to the passage (AUKE) to determine whether a question can be answered without using parameterized unstable world knowledge of pre-trained language models</em>. We then define <em>evidence</em> as the simplest substring in the passage that supports AUKE. Based on <em>evidence</em>, we introduce a novel faithfulness metric for the QAMR task. We propose a methodology that combines automated processes with manual refinement to augment QAMR datasets with evidence annotations to facilitate faithfulness evaluations. We apply this method to the Chinese QAMR dataset CMRC 2018 and DRCD to extend two datasets that support evidence-based faithfulness evaluation, CMRCFF (CMRC with Faithfulness) and DRCDFF (CMRC with Faithfulness). To alleviate the potential factuality and faithfulness issues induced by unstable world knowledge, we propose a method called Maximizing Discrimination Masking (MDM), which masks the word with the highest degree of distinguishability. MDM is an approximation method designed to circumvent the reliance on parameterized unstable world knowledge embedded within pre-trained language models utilized by QAMR systems. We conduct experiments under the fine-tune setting and few-shot setting on CMRCFF and DRCDFF. The results verify that our MDM approach can effectively improve the factuality and faithfulness of the models.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103915"},"PeriodicalIF":7.4,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417981","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":"Bridging insight gaps in topic dependency discovery with a knowledge-inspired topic model","authors":"Yi-Kun Tang ,&nbsp;Heyan Huang ,&nbsp;Xuewen Shi ,&nbsp;Xian-Ling Mao","doi":"10.1016/j.ipm.2024.103911","DOIUrl":"10.1016/j.ipm.2024.103911","url":null,"abstract":"<div><div>Discovering intricate dependencies between topics in topic modeling is challenging due to the noisy and incomplete nature of real-world data and the inherent complexity of topic dependency relationships. In practice, certain basic dependency relationships have been manually annotated and can serve as valuable knowledge resources, enhancing the learning of topic dependencies. To this end, we propose a novel topic model, called Knowledge-Inspired Dependency-Aware Dirichlet Neural Topic Model (KDNTM). Specifically, we first propose Dependency-Aware Dirichlet Neural Topic Model (DepDirNTM), which can discover semantically coherent topics and complex dependencies between these topics from textual data. Then, we propose three methods to leverage accessible external dependency knowledge under the framework of DepDirNTM to enhance the discovery of topic dependencies. Extensive experiments on real-world corpora demonstrate that our models outperform 12 state-of-the-art baselines in terms of topic quality and multi-labeled text classification in most cases, achieving up to a 14% improvement in topic quality over the best baseline. Visualizations of the learned dependency relationships further highlight the benefits of integrating external knowledge, confirming its substantial impact on the effectiveness of topic modeling.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103911"},"PeriodicalIF":7.4,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417982","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":"NNEnsLeG: A novel approach for e-commerce payment fraud detection using ensemble learning and neural networks","authors":"Qingfeng Zeng ,&nbsp;Li Lin ,&nbsp;Rui Jiang ,&nbsp;Weiyu Huang ,&nbsp;Dijia Lin","doi":"10.1016/j.ipm.2024.103916","DOIUrl":"10.1016/j.ipm.2024.103916","url":null,"abstract":"<div><div>The proliferation of fraud in online shopping has accompanied the development of e-commerce, leading to substantial economic losses, and affecting consumer trust in online shopping. However, few studies have focused on fraud detection in e-commerce due to its diversity and dynamism. In this work, we conduct a feature set specifically for e-commerce payment fraud, around transactions, user behavior, and account relevance. We propose a novel comprehensive model called Neural Network Based Ensemble Learning with Generation (NNEnsLeG) for fraud detection. In this model, ensemble learning, data generation, and parameter-passing are designed to cope with extreme data imbalance, overfitting, and simulating the dynamics of fraud patterns. We evaluate the model performance in e-commerce payment fraud detection with &gt;310,000 pieces of e-commerce account data. Then we verify the effectiveness of the model design and feature engineering through ablation experiments, and validate the generalization ability of the model in other payment fraud scenarios. The experimental results show that NNEnsLeG outperforms all the benchmarks and proves the effectiveness of generative data and parameter-passing design, presenting the practical application of the NNEnsLeG model in e-commerce payment fraud detection.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103916"},"PeriodicalIF":7.4,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417979","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":"Discovering technology opportunities of latecomers based on RGNN and patent data: The example of Huawei in self-driving vehicle industry","authors":"Runzhe Zhang ,&nbsp;Xiang Yu ,&nbsp;Ben Zhang ,&nbsp;Qinglan Ren ,&nbsp;Yakun Ji","doi":"10.1016/j.ipm.2024.103908","DOIUrl":"10.1016/j.ipm.2024.103908","url":null,"abstract":"<div><div>Emerging technologies provide competitive opportunities for latecomers to catch up with leading giants. As most of the extant literature indicated, types of single-dimensional relations from patent data have been revealed in technology opportunity discovery (TOD) research. Still, few have been aware of the more complex characteristics extracted from higher-dimensional patent information such as the patentee-technology relation. To derive this valuable relation for more robust results, this article introduces a novel TOD method, utilizing a recursive graph neural network (RGNN) to transform this high-dimensional information into measures of heterogeneity as internal capability, and combining it with external challenges evaluated by the competitiveness index, to identify technological opportunities. Taking the self-driving vehicle (SDV) industry with 33,347 patent families from 2010 to 2021 as the initial dataset, it shows significant performance promotions compared to previous analogous TOD models. Meanwhile, tested by recent filing patent data, the predicted opportunities are consistent with Huawei and other enterprises. Upon illuminating the intense technological competition situation among the preeminent SDV firms worldwide as a case exploration, this research contributes theoretical and practical views to the TOD research and network analysis.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103908"},"PeriodicalIF":7.4,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417980","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":"Robust annotation aggregation in crowdsourcing via enhanced worker ability modeling","authors":"Ju Chen ,&nbsp;Jun Feng ,&nbsp;Shenyu Zhang ,&nbsp;Xiaodong Li ,&nbsp;Hamza Djigal","doi":"10.1016/j.ipm.2024.103914","DOIUrl":"10.1016/j.ipm.2024.103914","url":null,"abstract":"<div><div>Truth inference in crowdsourcing, which studies how to aggregate noisy and biased annotations from workers with varied expertise, is a fundamental technology powering the quality of crowdsourced annotations. Generally, confusion-matrix-based methods are more promising and worker better, as they model each worker’s ability using a confusion matrix rather than a single real value. However, the imbalanced classes and the insufficient training data caused by the <span><math><mrow><mi>K</mi><mo>×</mo><mi>K</mi></mrow></math></span> pattern (<span><math><mi>K</mi></math></span> refers to the number of classes) are still two major issues for the learning of confusion matrices, which call for a robust modeling structure of workers’ confusion matrices. In this article, we propose in response a Fine-Grained Bayesian Classifier Combination model (FGBCC), in which a combination of <span><math><mi>K</mi></math></span> univariate Gaussian distributions and the standard softmax function is exploited with an aim to improve the estimation of workers’ abilities. Compared to existing methods, FGBCC is capable of learning extensive worker behaviors and is less susceptible to these issues that previous methods suffer from, owing to its stronger generalization ability. Moreover, Considering the exact solution to the complex posterior is unavailable, we devise a computationally efficient algorithm to approximate the posterior. Extensive experiments on 24 real-world datasets covering a wide range of domains, verify the clear advantages of FGBCC over 11 state-of-the-art benchmark methods.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103914"},"PeriodicalIF":7.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417978","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":"Explainable reasoning over temporal knowledge graphs by pre-trained language model","authors":"Qing Li,&nbsp;Guanzhong Wu","doi":"10.1016/j.ipm.2024.103903","DOIUrl":"10.1016/j.ipm.2024.103903","url":null,"abstract":"<div><div>Temporal knowledge graph reasoning (TKGR) has been considered as a crucial task for modeling the evolving knowledge, aiming to infer the unknown connections between entities at specific times. Traditional TKGR methods try to aggregate structural information between entities and evolve representations of entities over distinct snapshots, while some other methods attempt to extract temporal logic rules from historical interactions. However, these methods fail to address the continuously emerging unseen entities over time and ignore the historical dependencies between entities and relations. To overcome these limitations, we propose a novel method, termed TPNet, which introduces historical information completion strategy (HICS) and pre-trained language model (PLM) to conduct explainable inductive reasoning over TKGs. Specifically, TPNet extracts reliable temporal logical paths from historical subgraphs using a temporal-correlated search strategy. For unseen entities, we utilize HICS to sample or generate paths to supplement their historical information. Besides, a PLM and a time-aware encoder are introduced to jointly encode the temporal paths, thereby comprehensively capturing dependencies between entities and relations. Moreover, the semantic similarity between the query quadruples and the extracted paths is evaluated to simultaneously optimize the representations of entities and relations. Extensive experiments on entity and relation prediction tasks are conducted to evaluate the performance of TPNet. The experimental results on four benchmark datasets demonstrate the superiority of TPNet over state-of-the-art TKGR methods, achieving improvements of 14.35%, 23.08%, 6.75% and 5.38% on MRR, respectively.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103903"},"PeriodicalIF":7.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418002","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 joint extraction of fact-condition statement and super relation in scientific text with table filling method","authors":"Qizhi Chen ,&nbsp;Hong Yao ,&nbsp;Diange Zhou","doi":"10.1016/j.ipm.2024.103906","DOIUrl":"10.1016/j.ipm.2024.103906","url":null,"abstract":"<div><div>The fact-condition statements are of great significance in scientific text, via which the natural phenomenon and its precondition are detailly recorded. In previous study, the extraction of fact-condition statement and their relation (super relation) from scientific text is designed as a pipeline that the fact-condition statement and super relation are extracted successively, which leads to the error propagation and lowers the accuracy. To solve this problem, the table filling method is firstly adopted for joint extraction of fact-condition statement and super relation, and the Biaffine Convolution Neural Network model (BCNN) is proposed to complete the task. In the BCNN, the pretrained language model and Biaffine Neural Network work as the encoder, while the Convolution Neural Network is added into the model as the decoder that enhances the local semantic information. Benefiting from the local semantic enhancement, the BCNN achieves the best F1 score with different pretrained language models in comparison with other baselines. Its F1 scores in GeothCF (geological text) reach 73.17% and 71.04% with BERT and SciBERT as pretrained language model, respectively. Moreover, the local semantic enhancement also increases its training efficiency, via which the tags’ distribution can be more easily learned by the model. Besides, the BCNN trained with GeothCF also exhibits the best performance in BioCF (biomedical text), which indicates that it can be widely applied for the information extraction in all scientific domains. Finally, the geological fact-condition knowledge graph is built with BCNN, showing a new pipeline for construction of scientific fact-condition knowledge graph.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103906"},"PeriodicalIF":7.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417752","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":"Crowdsourced auction-based framework for time-critical and budget-constrained last mile delivery","authors":"Esraa Odeh ,&nbsp;Shakti Singh ,&nbsp;Rabeb Mizouni ,&nbsp;Hadi Otrok","doi":"10.1016/j.ipm.2024.103888","DOIUrl":"10.1016/j.ipm.2024.103888","url":null,"abstract":"<div><div>This work addresses the problem of Last Mile Delivery (LMD) under time-critical and budget-constrained environments. Given the rapid growth of e-commerce worldwide, LMD has become a primary bottleneck to the efficiency of delivery services due to several factors, including travelling distance, service cost, and delivery time. Existing works mainly target optimizing travelled distance and maximizing gained profit; however, they do not consider time-critical and budget-limited tasks. The deployment of UAVs and the development of crowdsourcing platforms have provided a range of solutions to advance performance in LMD frameworks, as they offer many crowdworkers at varying locations ready to perform tasks instead of having a single point of departure. This work proposes a Hybrid, Crowdsourced, Auction-based LMD (HCA-LMD) framework with a dynamic allocation mechanism for optimized delivery of time-sensitive and budget-limited tasks. The proposed framework allocates tasks to workers as soon as they are submitted, given their urgency level and dropoff location, while considering the price, rating, and location of available workers. This work was compared against two benchmarks to assess the framework’s performance in dynamic environments in terms of on-time deliveries, average delay, and profit. Extensive simulation results showed an outstanding performance of the proposed state-of-the-art LMD framework by accomplishing almost 92% on-time deliveries under varying time- and budget-constrained scenarios, outperforming the first benchmark in the on-time allocation rate by fulfiling an additional 24% of the tasks the benchmark failed, with around 50% drop in average delay time and up to x5.8 gained profit when compared against the second benchmark.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103888"},"PeriodicalIF":7.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356984","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}