Can Conversations on Reddit Forecast Future Economic Uncertainty? An Interpretable Machine Learning Approach

2023 | International Conference on Information Systems | Citations: 0

Authors: Jiang, Jinhang; Feng, Mei; Srinivasan, Karthik

Abstract: In recent years, social media has become an indispensable source of information ... Expand

Abstract: In recent years, social media has become an indispensable source of information through which public attitudes, opinions, and concerns can be studied and quantified. This paper proposes an interpretable machine learning framework for predicting the Equity Market-related Economic Uncertainty Index using features generated from a popular discussion forum on Reddit. Our framework consists of a series of custom preprocessing and analytics methods, including BERTopic for latent topic identification and regularized linear models. Using our framework, we evaluate explanatory models with different configurations over a large corpus of Reddit posts belonging to the personal finance category. Our analysis generates valuable insights about discussion topics on Reddit and their efficacy in accurately predicting future economic uncertainty. The study demonstrates the potential of using social media data and interpretable machine learning to inform economic forecasting research. Collapse

Topics: Reddit social media explainable artificial intelligence Python permutation feature importance

Methods: descriptive statistic time series analysis longitudinal research topic model experiment

Responsible Artificial Intelligence in Healthcare: Predicting and Preventing Insurance Claim Denials for Economic and Social Wellbeing

2023 | Information Systems Frontiers | Citations: 1

Authors: Johnson, Marina; Albizri, Abdullah; Harfouche, Antoine

Abstract: It is estimated that one out of seven health insurance claims is rejected in the ... Expand

Abstract: It is estimated that one out of seven health insurance claims is rejected in the US; hospitals across the country lose approximately $262 billion annually due to denied claims. This widespread problem causes huge cash-flow issues and overburdens patients. Thus, preventing claim denials before claims are submitted to insurers improves profitability, accelerates the revenue cycle, and supports patients’ wellbeing. This study utilizes Design Science Research (DSR) paradigm and develops a Responsible Artificial Intelligence (RAI) solution helping hospital administrators identify potentially denied claims. Guided by five principles, this framework utilizes six AI algorithms – classified as white-box and glass-box – and employs cross-validation to tune hyperparameters and determine the best model. The results show that a white-box algorithm (AdaBoost) model yields an AUC rate of 0.83, outperforming all other models. This research’s primary implications are to (1) help providers reduce operational costs and increase the efficiency of insurance claim processes (2) help patients focus on their recovery instead of dealing with appealing claims. Collapse

Topics: artificial intelligence ADA missing data partial dependence plot responsible artificial intelligence

Methods: machine learning computational algorithm experiment support vector machine design artifact

Profit vs. Equality? The Case of Financial Risk Assessment and A New Perspective on Alternative Data

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Lu, Tian; Zhang, Yingjie; Li, Beibei

Abstract: The importance of pursuing financial inclusion to accelerate economic growth an ... Expand

Abstract: The importance of pursuing financial inclusion to accelerate economic growth and enhance financial sustainability has been well noted. However, studies have provided few actionable insights into how financial institutions can balance the potential socioeconomic trade-off between profitability and equality. One major challenge arises from a lack of understanding of the impacts of various types of market information available on financial equality beyond economic profitability. Another challenge lies in how the socioeconomic trade-off under a large set of counterfactual policies in a real-world setting can be evaluated. Our motivation for the present study was the emerging sources of digitized user-behavior data (i.e., "alternative data") stemming from the high penetration of mobile devices and internet access. Accordingly, we investigated how alternative data from smartphones and social media can help mitigate potential financial inequality while preserving business profitability in the context of financial credit risk assessment. We partnered with a leading microloan website to design a novel "meta" experiment that allowed us to simulate various real-world field experiments under an exhaustive set of counterfactual policies. Interestingly, we found that profiling user financial risk using smartphone activities is 1.3 times more effective in improving financial inclusion than using online social media information (23.05% better vs. 18.11%), and nearly 1.3 times more effective in improving business profitability (42% better vs. 33%). Surprisingly, we found that using consumers' online shopping activities for credit risk profiling can hurt financial inclusion. Furthermore, we investigated potential explanations for financial inclusion improvements. Our findings suggest that alternative data, especially users' smartphone activities, not only demonstrate higher ubiquity but also appear to be more orthogonal to conventional sensitive demographic attributes. This, in turn, can help mitigate statistical bias driven by the unobserved factors or underrepresentative training samples in machine-based risk assessment processes. Collapse

Topics: website social media smartphone microblog electronic commerce

Methods: experiment XGBoost machine learning experimental design k-nearest neighbor

Introducing a methodological approach to determine value shares in Digital Ecosystems

2023 | International Conference on Business Informatics | Citations: 0

Authors: Mihale-Wilson, Cristina; Hagen, Simon; Kohl, Tobias; Kortum, Henrik; Illgen, Fabian; Rebstadt, Jonas; Thomas, Oliver; Hinz, Oliver

Abstract: Motivated by the critical yet unsolved task of fair value distribution in digit ... Expand

Abstract: Motivated by the critical yet unsolved task of fair value distribution in digital ecosystems (DEs), this study presents a methodological approach that allows us to determine ecosystem components' value share to the total co-created value. Our method takes a holistic perspective on DEs. It suggests that when viewing DEs as complex networks, the value share of a component to the total co-created value stems from the network size and the interaction between the network participants. We demonstrate the applicability of the proposed method in a simulation of a Smart Living service ecosystem. Our simulation shows that our method is suitable for unraveling hitherto hidden interconnectedness between value-co-creating ecosystem components. Components that offer a low structural contribution to the total value can still play a crucial role in the network and have the most significant value share to the whole network. Collapse

Topics: artificial intelligence digital ecosystem value creation shapley additive explanation Python

Methods: simulation design artifact theoretical contribution

Theories: social network theory

Learning Personalized Privacy Preference From Public Data

2023 | International Conference on Information Systems | Citations: 0

Authors: Wang, Wen; Li, Beibei

Abstract: Understanding consumers' privacy preferences is crucial for firms and policymak ... Expand

Abstract: Understanding consumers' privacy preferences is crucial for firms and policymakers to establish trust and encourage innovation and competition. With the widespread use of digital technologies, individuals generate and share vast amounts of data about them selves in the public domain. Even without knowing a person's private information, the psychosocial traits revealed in public data can provide valuable insights into their pri vacy preferences. In this study, we aim to predict personalized privacy preferences us ing social media posts. Our prediction model shows that psychosocial traits such as per sonality, lifestyles, riskpreference, economic thinking, emotions, etc extracted from posts provide significantly greater predictive power than demographic characteristics. Fur thermore, we demonstrate the practical value and impact of our model for business and society through a simulation analysis. Our tool can help platforms and policymakers es timate the impact of privacy policies and prevent potential harms such as discrimination. Collapse

Topics: privacy social media Twitter shapley additive explanation information privacy concern

Methods: BERT machine learning experiment computational algorithm topic model

Theories: game theory

A deep learning approach for detecting fake reviewers: Exploiting reviewing behavior and textual information

2023 | Decision Support Systems | Citations: 0

Authors: Zhang, Dong; Li, Wenwen; Niu, Baozhuang; Wu, Chong

Abstract: Ensuring the credibility of online consumer reviews (OCRs) is a growing societal ... Expand

Abstract: Ensuring the credibility of online consumer reviews (OCRs) is a growing societal concern. However, the problem of fake reviewers on online platforms significantly influences e-commerce authenticity and consumer trust. Existing studies for fake reviewer detection mainly focus on deriving novel behavioral and linguistic features. These features require extensive human labor and expertise, placing a heavy burden on platforms. Therefore, we propose a novel end-to-end framework to detect fake reviewers based on behavior and textual information. It has two key components: (1) a behavior-sensitive feature extractor that learns the underlying patterns of reviewing behavior; (2) a context-aware attention mechanism that extracts valuable features from online reviews. We rigorously evaluate each proposed module and the entire framework against state-of-the-art benchmarks on two real-world datasets from http://Yelp.com. Experimental results demonstrate that our method achieves state-ofthe-art results on fake reviewer detection. Our method can be considered a tentative step toward lowering human labor costs in realizing automated fake reviewer detection on e-commerce platforms. Collapse

Topics: fake review online review electronic commerce deception detection shapley additive explanation

Methods: machine learning digital trace data experiment parametric test deep learning

Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection

2021 | Decision Support Systems | Citations: 0

Authors: Kou, Gang; Xu, Yong; Peng, Yi; Shen, Feng; Chen, Yang; Chang, Kun; Kou, Shaomin

Abstract: Many bankruptcy prediction models for small and medium-sized enterprises (SMEs) ... Expand

Abstract: Many bankruptcy prediction models for small and medium-sized enterprises (SMEs) are built using accountingbased financial ratios. This study proposes a bankruptcy prediction model for SMEs that uses transactional data and payment network–based variables under a scenario where no financial (accounting) data are required. Offline and online test results both confirmed the predictive capability and economic benefit of transactional data–based variables. However, incorporating those features in predictive models produces high dimensional problems, which deteriorates model interpretability and increases feature acquisition costs. Thus, we propose a two-stage multiobjective feature-selection method that optimizes the number of features as well as model classification performance. The results showed that the proposed model achieved similar classification perfor­ mance while greatly reducing the cardinality of the feature subset. Finally, the feature importance evaluation for features in the optimal subset confirmed the importance of transactional data and payment network-based variables for bankruptcy prediction. Collapse

Topics: permutation feature importance accounting information privacy law legal context decision making

Methods: machine learning computational algorithm experiment support vector machine decision tree classification

Blowing the Whistle on Opioid Overprescription: Insights from Patient Feedback on Physician Rating Websites

2021 | HICSS | Citations: 0

Authors: Mamonov, Stanislav

Abstract: Overprescription of opioid pain relievers is a known contributor to the growing ... Expand

Abstract: Overprescription of opioid pain relievers is a known contributor to the growing opioid epidemic. Identification of medical providers that engage in overprescription has proven challenging. We examine the utility of physician rating websites (PRWs) as potential sources of data that may help identify overprescribing practices. We leverage text mining techniques to identify linguistic cues that are associated with known cases of overprescription. We find that patients flag potentially problematic medical providers in their reviews and suggest that intervention by authorities is warranted. Our study contributes to the growing body of literature on medical infoveillance by identifying patients’ appeals to regulatory authorities as an important type of social signal for regulatory monitoring. Collapse

Topics: website social media anonymity systems implementation permutation feature importance

Methods: natural language processing support vector machine computational algorithm machine learning naïve bayes

An Explainable Machine Learning Model for Chronic Wound Management Decisions

2021 | Americas Conference on Information Systems | Citations: 0

Authors: Mombini, Haadi; Tulu, Bengisu; Strong, Diane; Agu, Emmanuel; Lindsay, Clifford; Loretz, Lorraine; Pedersen, Peder; Dunn, Raymond

Abstract: Recent advances in machine learning (ML) algorithms have motivated their use fo ... Expand

Abstract: Recent advances in machine learning (ML) algorithms have motivated their use for automated Decision Support Systems (DSS). In healthcare domain, ML-based DSS enable providers to analyze large amounts of patient data and complex images quickly. However, providers find it difficult to interpret ML predictions due to their 'black box' reasonings. To facilitate meaningful interpretations, ML-based DSS should include explanation facilities as recommended in information systems (IS) research. For example, a wound care DSS should allow providers to understand the reasoning (e.g., amount and presence of unhealthy tissues) behind referral decisions. We present a ML-based DSS that provides global (reliance on domain knowledge) and local (reasoning for predicting an instance) explanations for wound care decisions. We use Shapley explanations for a logistic regression (trained on wound visual features) which outperformed other classifiers when predicting referral decisions (F-1 =0.938) and demonstrate its applicability in a wound care use-scenario. Findings suggest similar approach can be applied for other complex decision problems. Collapse

Topics: decision support system decision making permutation feature importance explainable artificial intelligence shapley additive explanation

Methods: machine learning support vector machine binary classification logistic regression gradient boosting

Forecasting demand profiles of new products

2020 | Decision Support Systems | Citations: 3

Authors: van Steenbergen, R.M.; Mes, M.R.K.

Abstract: Nowadays, many companies face shorter product life cycles, increasing the need t ... Expand

Abstract: Nowadays, many companies face shorter product life cycles, increasing the need to properly forecast demand for newly introduced products. These forecasts allow them to support operational decisions, such as procurement and inventory control. However, forecasting the demand of new products is challenging compared to existing products, since historical sales data is not available as an indicator of future sales. Moreover, little attention has been paid in literature to quantitative methods for new product forecasting, especially with respect to quanti­ fying the uncertainty in demand. In this paper, we present a novel demand forecasting method denoted by DemandForest, which combines K-means, Random Forest, and Quantile Regression Forest. This machine learning-based approach combines the historical sales data of previously introduced products and product characteristics of existing and new products to make prelaunch forecasts and support inventory management decisions for new products. DemandForest clusters and predicts demand patterns, and predicts the quantiles of the total demand during an introduction period. We validate and illustrate our approach for forecasting and inventory management using real-world data sets of several companies. Compared to several benchmark methods, DemandForest provides the most accurate predictions, resulting in potential inventory savings of around 15% depending on lead times and service levels. Collapse

Topics: supply chain management database system inventory management system business-to-consumer business-to-business

Methods: computational algorithm random forest classification experiment cluster analysis k-means clustering