sPAN-pROP: Combatting Contextualized Social Media State-Linked Propaganda in the Middle East

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Aleroud, Ahmed; Melhem, Abdullah Bani; Alhussien, Nour; Albert, Craig

Abstract: Social Cyber-attacks such as propaganda, conspiracy theories, anger, and hate d ... Expand

Abstract: Social Cyber-attacks such as propaganda, conspiracy theories, anger, and hate discourse are very old phenomena that inflict harm to humans, organizations, national security, public officials, democratization efforts, careers, and policies. There have been significant efforts to identify anti-US speech on social media which includes propaganda. Such efforts largely ignore the attempts by other countries to manipulate social media in some regions including the Middle East. Research in this area is computational and solely focuses on fine tuning language models to detect general propaganda attacks. This paper addresses a new category of propaganda attacks that are tied to state-linked accounts that spread anti-US propaganda by taking advantages of specific geo-political crises in the Middle East. We investigated the role of general language models and training data to detect those forms of propaganda. Our study concludes that existing propaganda training data is unable to successfully detect targeted propaganda. We propose a contextualized span detection approach to identify these types of propaganda and show that our targeted training models work significantly better compared to the existing general propaganda detection approaches. Collapse

Topics: Twitter IT security threat social media armed conflict misinformation

Methods: language model experiment deep learning PyTorch literature study

A Bitter Pill to Swallow? The Consequences of Patient Evaluation in Online Health Question-and-Answer Platforms

2023 | Information Systems Research | Citations: 0

Authors: Chen, Chen; Walker, Dylan

Abstract: Online health question-and-answer (Q&A) platforms (OHQPs), where patients post h ... Expand

Abstract: Online health question-and-answer (Q&A) platforms (OHQPs), where patients post health-related questions, evaluate advice from multiple doctors, and direct a bounty (monetary reward) to their most preferred answer, have become a prominent channel for patients to receive medical advice in China. To explore the quality of medical advice on these platforms, we analyzed data on patients’ evaluation of ∼497,000 answers to ∼114,000 questions on one of the most popular OHQPs, 120ask.com, over a three-month period. We assembled a panel of independent physicians and instructed them to evaluate the quality of ∼13,000 answers. We found that the quality of medical advice offered on the platform was high on average, and that low-quality answers were rare (6%). However, our results also indicate that patients lacked the ability to discriminate advice quality. They were as likely to choose the best answer as the worst. The medical accuracy of patient evaluation was worse in critical categories (cancer, internal medicine) and for vulnerable subpopulations (pediatrics). Given that millions of patients seek medical advice from OHQPs in China annually, the social and economic implications of this finding are troubling. To understand how patients evaluate advice, we trained deep neural networks to think like patients, allowing us to identify patients’ positive and negative responses to different heurist cues. Although our results indicate that OHQPs perform well, we identified several concerns that should be addressed through platform design and policy changes. Because the Q&A process lacks peer review mechanisms, signals of advice quality are not conveyed to patients, forcing them to rely on heuristic cues, which cannot effectively guide them toward the best advice. We also found that the platform reputation metric was not correlated with the quality of the advice giver’s advice, may effectively encourage patients to select lesser quality medical advice, and increased the risk of moral hazard for malicious players to intentionally provide less accurate but more agreeable advice for personal gain. Our analysis revealed bad actors on the platform, including drug promoters and spammers. Finally, we found that OHQPs exacerbated care avoidance. We discuss several potential policy changes to address these shortcomings. History: Param Singh, Senior Editor; Idris Adjerid, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1158. Collapse

Topics: decision making local interpretable model agnostic explanation digital platform health information system pandemic

Methods: artificial neural network psychometrics survey deep learning natural language processing

Theories: lemon market theory

A privacy-preserving decentralized credit scoring method based on multi-party information

2023 | Decision Support Systems | Citations: 0

Authors: He, Haoran; Wang, Zhao; Jain, Hemant; Jiang, Cuiqing; Yang, Shanlin

Abstract: With society's wide-scale adoption of information technology, significant inform ... Expand

Abstract: With society's wide-scale adoption of information technology, significant information about borrowers is distributed across various parties, information that can be jointly used to improve credit scoring. However, use of such information faces many challenges, such as the problems of preserving privacy and information redun­ dancy. To address these challenges in leveraging multi-source information for credit scoring, we propose a decentralized multi-party method based on logistic regression. Specifically, we formulate a logistic regression model using the vertical federated learning paradigm. To preserve data privacy during multi-party collaborative model training, we use additively homomorphic encryption based on the second-order Taylor series expansion of the loss function and its gradient. To address information redundancy and to improve the performance of the credit scoring model, we use the proposed HE-DPGD algorithm to estimate the distributed coefficients in a privacy-preserving setting. Empirical evaluation shows that the proposed method can leverage information from multiple parties securely and effectively. Collapse

Topics: privacy homomorphic encryption logistics management online lending missing data

Methods: computational algorithm machine learning federated learning logistic regression experiment

Resource Allocation for Heterogeneous Computing Tasks in Wirelessly Powered MEC-enabled IIOT Systems

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Hu, Yixiang; Deng, Xiaoheng; Zhu, Congxu; Chen, Xuechen; Chi, Laixin

Abstract: Integrating wireless power transfer with mobile edge computing (MEC) has become ... Expand

Abstract: Integrating wireless power transfer with mobile edge computing (MEC) has become a powerful solution for increasingly complicated and dynamic industrial Internet of Things (IIOT) systems. However, the traditional approaches overlooked the heterogeneity of the tasks and the dynamic arrival of energy in wirelessly powered MEC-enabled IIOT systems. In this article, we formulate the problem of maximizing the product of the computing rate and the task execution success rate for heterogeneous tasks. To manage energy harvesting adaptively and select appropriate computing modes, we devise an online resource allocation and computation offloading approach based on deep reinforcement learning. We decompose this approach into two stages: an offloading decision stage and a stopping decision stage. The purpose of the offloading decision stage is to select the computing mode and dynamically allocate the computation round length for each task after learning from the channel state information and the task experience. This stage allows the system to support heterogeneous computing tasks. Subsequently, in the second stage, we adaptively adjust the number of fading slots devoted to energy harvesting in each round in accordance with the status of each fading slot. Simulation results show that our proposed algorithm can better allocate resources for heterogeneous tasks and reduce the ratio of failed tasks and energy consumption when compared with several existing algorithms. Collapse

Topics: resource allocation edge computing decision quality

Methods: computational algorithm reinforcement learning simulation experiment PyTorch

Moving Emergency Response Forward: Leveraging Machine-Learning Classification of Disaster-Related Images Posted on Social Media

2023 | Journal of Management Information Systems | Citations: 0

Authors: Johnson, Matthew; Murthy, Dhiraj; Robertson, Brett W.; Smith, William Roth; Stephens, Keri K.

Abstract: Social media platforms are increasingly used during disasters. In the United Sta ... Expand

Abstract: Social media platforms are increasingly used during disasters. In the United States, users often consider these platforms to be reliable news sources and they believe first responders will see what they publicly post. While having ways to request help during disasters might save lives, this information is difficult to find because non-relevant content on social media completely overshadows content reflective of who needs help. To resolve this issue, we develop a framework for classifying hurricane-related images that have been human-annotated. Our approach uses transfer learning and classifies each image using the VGG-16 convolutional neural network and multi-layer perceptron classifiers according to the urgency, relevance, and time period, in addition to the presence of damage and relief motifs. We find that our framework not only successfully functions as an accurate method for hurricane-related image classification but also that real-time classification of social media images using a small training set is possible. Collapse

Topics: social media Twitter hurricane natural hazard application programming interface

Methods: machine learning deep learning literature study convolutional neural network classification method

Improved healthcare disaster decision-making utilizing information extraction from complementary social media data during the COVID-19 pandemic

2023 | Decision Support Systems | Citations: 0

Authors: Kellner, Domenic; Lowin, Maximilian; Hinz, Oliver

Abstract: Managing an extreme event like a healthcare disaster requires accurate informati ... Expand

Abstract: Managing an extreme event like a healthcare disaster requires accurate information about the event's circum­ stances to comprehend the full consequences of acting. However, information quality is rarely optimal since it takes time to determine the information of relevance. The COVID-19 pandemic showed that even official data sources are far from optimal since they suffer from reporting delays that slow decision-making. To support decision-makers with timely information, we utilize data from online social networks to propose an adaptable information extraction solution to create indices helping to forecast COVID-19 case numbers and hospitalization rates. We show that combining heterogeneous data sources like Twitter and Reddit can leverage these sources' inherent complementarity and yield better predictions than those using a single data source alone. We further show that the predictions run ahead of the official COVID-19 incidences by up to 14 days. Additionally, we highlight the importance of model adjustments whenever new information becomes available or the underlying data changes by observing distinct changes in the presence of specific symptoms on Reddit. Collapse

Topics: Twitter Reddit social media pandemic website

Methods: BERT information extraction computational algorithm autoregressive integrated moving average time series analysis

Utilizing the omnipresent: Incorporating digital documents into predictive process monitoring using deep neural networks

2023 | Decision Support Systems | Citations: 0

Authors: Levich, Sergej; Lutz, Bernhard; Neumann, Dirk

Abstract: Predictive process monitoring (PPM) allows companies to improve the efficiency o ... Expand

Abstract: Predictive process monitoring (PPM) allows companies to improve the efficiency of their business processes by predicting aspects such as the process outcome, the next event, or the time until the next event. So far, existing studies have mainly focused on developing novel predictive models while using features solely from event logs. In this study, we aim to go beyond log data and increase the focus of PPM research towards external context information. To this end, we consider digital documents as they are omnipresent in many business processes and their inclusion can often be justified by a business rationale. However, incorporating digital documents into PPM models poses considerable challenges as they present unstructured data that can contain visual and textual cues of future process behavior, while manual feature extraction is generally not feasible. Therefore, we propose an approach that processes digital documents based on automated visual and textual feature extraction methods. Furthermore, we design a tailored integration module which transforms the extracted features from multiple document pages into a fixed-size representation that subsequently serves as input for the predictive models. Our evaluation, based on a real-world dataset of insurance claims from a mid-sized German insurance company, featuring 5131 process instances with 32,058 events and 39,242 document pages, shows that incorporating digital documents improves the performance by significant margins in predicting the damage type, the next event, and the time until the next event. Finally, we analyze how digital documents contribute to the model’s predictions in terms of Shapley additive explanations. Collapse

Topics: shapley additive explanation business process management artificial intelligence explainable artificial intelligence

Methods: BERT machine learning descriptive statistic artificial neural network business process modeling

A Graph Learning Model of Network Resources for Early Stage Startup Success Prediction

2023 | International Conference on Information Systems | Citations: 0

Authors: Liu, Mucan; Hu, Manting; Junming, Liu

Abstract: With high profitability accompanied by high risks, startups are driving industr ... Expand

Abstract: With high profitability accompanied by high risks, startups are driving industry evolution and innovation. However, due to high information asymmetry, startup success prediction remains challenging. From the perspective of network resources, we propose a variant heterogeneous graph attention network (ResourceNet) to model how a focal startup can access and leverage network resources from inter-organizational networks connected by investment, co-portfolio, and VC syndication relationships, for future success. We follow the design science paradigm to develop the node and link-aware attention mechanisms in graph network representations that jointly explore the impact of different mechanisms explaining the value of network resources, i.e., reach, richness, and receptivity. This project provides contributions to the startup success prediction studies by demonstrating the value of network resources in a topological interorganizational network, and also important managerial implications for startup companies (to seek network resources for future success) and VC (to pick the winners). Collapse

Methods: machine learning design science graph neural network artificial neural network simulation

Designing User-Centric Explanations for Medical Imaging with Informed Machine Learning

2023 | DESRIST | Citations: 0

Authors: Oberste, Luis; Rüffer, Florian; Aydingül, Okan; Rink, Johann; Heinzl, Armin

Abstract: A flawed algorithm released in clinical practice can cause unintended harm to pa ... Expand

Abstract: A flawed algorithm released in clinical practice can cause unintended harm to patient health. Risks, regulation, responsibility, and ethics shape the demand of clinical users to understand and rely on the outputs made by artificial intelligence. Explainable artificial intelligence (XAI) offers methods to render a model’s behavior understandable from different perspectives. Extant XAI, however, is mainly data-driven and designed to meet developers’ demands to correct models rather than clinical users’ expectations to reflect clinically relevant information. To this end, informed machine learning (IML) utilizes prior knowledge jointly with data to generate predictions, a promising paradigm to enrich XAI with medical knowledge. To explore how IML can be used to generate explanations that are congruent to clinical users’ demands and useful to medical decision-making, we conduct Action Design Research (ADR) in collaboration with a team of radiologists. We propose an IML-based XAI system for clinically relevant explanations of diagnostic imaging predictions. With the help of ADR, we reduce the gap between implementation and user evaluation and demonstrate the effectiveness of the system in a real-world application with clinicians. While we develop design principles of using IML for user-centric XAI in diagnostic imaging, the study demonstrates that an IML-based design adequately reflects clinicians’ conceptions. In this way, IML inspires greater understandability and trustworthiness of AI-enabled diagnostic imaging. Collapse

Topics: explainable artificial intelligence artificial intelligence usability knowledge base decision making

Methods: design artifact design principle qualitative interview design science action design research

Recommendation Systems with Causal Inference-based Regularization

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Padalkar, Nakul; Lima, Victor De

Abstract: Recommender systems have become integral to modern society, facilitating person ... Expand

Abstract: Recommender systems have become integral to modern society, facilitating personalized experiences across various platforms by allowing the offering of tailored content, products, and services. Current approaches for building such systems employ neural networks that overfit the training data and fail to generalize when tested on previously unseen data. Furthermore, recommender systems are fundamentally causal inference problems since they seek to forecast conditional relationships that describe a user's response to specific advertisements or products. In this paper, we comprehensively examine the matrix factorization model for recommendation systems using the MovieLens 25M dataset. We demonstrate the limitations of standard collaborative filtering and motivate the need for more suitable regularization. This research lays the foundation for future work incorporating causal inference into recommendation systems, which has garnered increasing support in recent literature for its potential to improve model performance. Collapse

Topics: recommender system collaborative filtering

Methods: computational algorithm optimization model experiment PyTorch literature study