Rumor Classification through a Multimodal Fusion Framework and Ensemble Learning

2023 | Information Systems Frontiers | Citations: 0

Authors: Azri, Abderrazek; Favre, Cécile; Harbi, Nouria; Darmont, Jérôme; Noûs, Camille

Abstract: The proliferation of rumors on social media has become a major concern due to it ... Expand

Abstract: The proliferation of rumors on social media has become a major concern due to its ability to create a devastating impact. Manually assessing the veracity of social media messages is a very time-consuming task that can be much helped by machine learning. Most message veracity verification methods only exploit textual contents and metadata. Very few take both textual and visual contents, and more particularly images, into account. Moreover, prior works have used many classical machine learning models to detect rumors. However, although recent studies have proven the effectiveness of ensemble machine learning approaches, such models have seldom been applied. Thus, in this paper, we propose a set of advanced image features that are inspired from the field of image quality assessment, and introduce the Multimodal fusiON framework to assess message veracIty in social neTwORks (MONITOR), which exploits all message features by exploring various machine learning models. Moreover, we demonstrate the effectiveness of ensemble learning algorithms for rumor detection by using five metalearning models. Eventually, we conduct extensive experiments on two real-world datasets. Results show that MONITOR outperforms state-of-the-art machine learning baselines and that ensemble models significantly increase MONITOR’s performance. Collapse

Topics: Twitter social network Python microblog logistics management

Methods: computational algorithm machine learning experiment meta learning ensemble learning

Trust in Online Ride-Sharing Transactions: Impacts of Heterogeneous Order Features

2023 | Journal of Management Information Systems | Citations: 0

Authors: Cheng, Xusen; Fu, Shixuan; Sun, Jianshan; Zuo, Meiyun; Meng, Xiangsong

Abstract: With the development of the sharing economy, online ride-sharing has become a pr ... Expand

Abstract: With the development of the sharing economy, online ride-sharing has become a primary form of commuting. Using secondary transaction data, this study investigates the associations between the heterogeneous features and mutual trust in sharing economy-driven online ride-sharing transactions. Based on an examination of 12,404 ride-sharing orders in Beijing, we propose a set of trust distribution maps using order location data to reveal heterogeneous spatial patterns of the relationship between online ride-sharing transactions and mutual trust. The results show that the historical order completion rate and order distance are positively associated with mutual trust in ride-sharing transactions, whereas order time and departure density negatively and significantly influence mutual trust. Furthermore, we use machine learning algorithms to predict trust. The implications for theory and practice and future research directions are discussed. Collapse

Topics: ride sharing platform sharing platform logistics management geographic information system online review

Methods: machine learning computational algorithm regression analysis method experiment exploratory data analysis

Theories: signaling theory

Clustering and Topological Data Analysis: Comparison and Application

2023 | HICSS | Citations: 0

Authors: Combs, K.; Bihl, T.

Abstract: Clustering is common technique used to demonstrate relationships between data a ... Expand

Abstract: Clustering is common technique used to demonstrate relationships between data and information. Of recent interest is topological data analysis (TDA), which can represent and cluster data through persistent homology. The TDA algorithms used include the Topological Mode Analysis Tool (ToMATo) algorithm, Garin and Tauzin's TDA Pipeline, and the Mapper algorithm. First, TDA is compared to ten other clustering algorithms on artificial 2D data where it ranked third overall. TDA had the second-highest performance in terms of average accuracy (97.9%); however, its computation-time performance ranked in the middle of the algorithms. TDA ranked fourth on the qualitative "visual trustworthiness" metric. On realworld data, TDA showed promising classification results (accuracy between 80-95%). Overall, this paper shows TDA is a competitive algorithm performancewise, though computationally expensive. When TDA is used for visualization, the Mapper algorithm allows for unique alternative views especially effective for visualizing highly dimensional data. Collapse

Topics: Python cloud computing missing data

Methods: computational algorithm cluster analysis DBSCAN agglomerative hierarchical clustering BIRCH

Prediction of hotel booking cancellations: Integration of machine learning and probability model based on interpretable feature interaction

2023 | Decision Support Systems | Citations: 0

Authors: Chen, Shuixia; Ngai, Eric W.T.; Ku, Yaoyao; Xu, Zeshui; Gou, Xunjie; Zhang, Chenxi

Abstract: Reliable hotel cancellation prediction can help establish appropriate operationa ... Expand

Abstract: Reliable hotel cancellation prediction can help establish appropriate operational strategies for hotel manage­ ment. In this sector, personal name records (PNR) data may be the most representative information source for prediction tasks. Despite the popularity of PNR, its inherent lack of availability has been commonly disregarded in the literature. Existing studies have directly input PNR into high-dimensional machine learning (ML) models to achieve cancellation predictions. Another type of model generates cancellation prediction based on the proba­ bility modeling of samples. In this study, we propose an interpretable feature interaction method to enrich the existing PNR information. Thereafter, we empirically assess the prediction performance of the two model classes. This study specifically determines whether or not the two methods can cross-fertilize each other to improve cancellation prediction. To do so, we propose a model integrating Bayesian networks (BNs) and Lasso regression for this prediction task. This study utilizes BNs for the probability model consistent with our correlated variables and dichotomous prediction setting. Moreover, we use a linear ML model (i.e., Lasso regression), given its ad­ vantages in reducing ineffective predictors and transparency for ranking feature importance. Empirical results show that the proposed integration model has better prediction performance, and the obtained BN estimators and interactive features are the most important predictors. This study contributes to the booking cancellation liter­ ature by proposing an interpretable feature interaction and a prediction method integrating two types of effective models. The obtained accurate and interpretable cancellation prediction further contributes to offering practical implications to hoteliers in managerial decision-making. Collapse

Topics: missing data decision making competitive advantage price management marketing management

Methods: lasso regression experiment computational algorithm robustness check parametric test

Near-Infrared Spectroscopy for Bladder Monitoring: A Machine Learning Approach

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

Authors: Fechner, Pascal; König, Fabian; Kratsch, Wolfgang; Lockl, Jannik; Röglinger, Maximilian

Abstract: Patients living with neurogenic bladder dysfunction can lose the sensation of th ... Expand

Abstract: Patients living with neurogenic bladder dysfunction can lose the sensation of their bladder filling. To avoid over-distension of the urinary bladder and prevent long-term damage to the urinary tract, the gold standard treatment is clean intermittent catheterization at predefined time intervals. However, the emptying schedule does not consider actual bladder volume, meaning that catheterization is performed more often than necessary, which can lead to complications such as urinary tract infections. Time-consuming catheterization also interferes with patients' daily routines and, in the case of an empty bladder, uses human and material resources unnecessarily. To enable individually tailored and volume-responsive bladder management, we design a model for the continuous monitoring of bladder volume. During our design science research process, we evaluate the model's applicability and usefulness through interviews with affected patients, prototyping, and application to a real-world in vivo dataset. The developed prototype predicts bladder volume based on relevant sensor data (i.e., near-infrared spectroscopy and acceleration) and the time elapsed since the previous micturition. Our comparison of several supervised state-of-the-art machine and deep learning models reveals that a long short-term memory network architecture achieves a mean absolute error of 116.7 ml that can improve bladder management for patients. Collapse

Topics: computer hardware open source mobile application user interface programming language

Methods: design artifact qualitative interview design process support vector machine computational algorithm

Causal Matching for Startups: Methods for Controlling Confounding

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Fernandes Marinho Ferreira, Vitor; Kuruzovich, Jason

Abstract: Causal inference is a critical methodology that allows researchers to empirical ... Expand

Abstract: Causal inference is a critical methodology that allows researchers to empirically test hypotheses and build theories. Ideally, the design method for establishing this causality would be through randomized controlled trials. However, studying the impact of different practices (hereinafter treatments) on initial outcomes when treatments are not applied exogenously is a challenge. Matching methods are an appropriate way to provide these study groups. The objective of this work is to provide an algorithmbased solution to match startups in a way that can mimic a human match. To do this, we use the human matching effort by Yu (2020) as a ground truth to evaluate various natural language processes and compare the performance of each with human raters. By comparing automated approaches with matching, we provide guidance for researchers interested in the causal analysis of startups using textual data and other covariates. Collapse

Topics: entrepreneurship business model strategic management

Methods: causality analysis natural language processing computational algorithm term frequency–inverse document frequency GloVe

Graph Learning of Multifaceted Motivations for Online Engagement Prediction in Counter-party Social Networks

2023 | International Conference on Information Systems | Citations: 0

Authors: Hu, Manting; LIN, Qingyuan; Zhang, Denghui; Lu, Angela; Junming, Liu; Xiong, Hui

Abstract: Social media has emerged as an essential venue to invigorate online political e ... Expand

Abstract: Social media has emerged as an essential venue to invigorate online political engagement. However, political engagement is multifaceted and impacted by both individuals' selfmotivation and social influence from peers and remains challenging to model in a counterparty network. Therefore, we propose a counter-party graph representation learning model to study individuals' intrinsic and extrinsic motivations for online political engagement. Firstly, we capture users' intrinsic political interests providing self-motivation from a user-topic network. Then, we encode how users cast influence on others from the inner-/counter-party through a user-user network. With the learned embedding of intrinsic and extrinsic motivations, we model the interactions between these two facets and utilize the dependency by deep sequential model decoding. Finally, extensive experiments using Twitter data related to the 2020 U.S. presidential election and the 2019 HK protests validate the model's predictive power. This study has implications for online political engagement, political participation, and political polarization. Collapse

Topics: social network Twitter social media user-generated content intrinsic motivation

Methods: machine learning experiment machine learning method computational algorithm ablation analysis

Theories: motivation theory

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

All eyes on me: Predicting consumer intentions on social commerce platforms using eye-tracking data and ensemble learning

2023 | Decision Support Systems | Citations: 0

Authors: Mikalef, Patrick; Sharma, Kshitij; Chatterjee, Sheshadri; Chaudhuri, Ranjan; Parida, Vinit; Gupta, Shivam

Abstract: Understanding what information is important for consumers when making a purchase ... Expand

Abstract: Understanding what information is important for consumers when making a purchase-related decision has been a key question for researchers and practitioners ever since the advent of empirical research in commerce. Nevertheless, our knowledge of what information is important has been formed primarily through post-purchase conscious capturing approaches, such as surveys and questionnaires. To overcome these limitations, we ground this research on an exploratory study that captures eye-tracking data during a decision-making task of product selection. Grounded on the dynamic attention theory, we utilize different information types and formats present on a popular social commerce platform, to identify elements which are important when deciding about online product purchase decision. Specifically, we employ a series of prediction algorithms and use an ensemble learning setup to predict the aspects that contribute to product selection by consumers. Our analysis highlights the most important informational cues to accurately predict product selection among alternatives. In addition, the results showcase how such elements shift in importance during the temporal sequence of comparing different product alternatives. Our results provide insight into how we can understand the journey of decision-making for social commerce customers when navigating through information to select a product. In addition, it opens the discussion about the shifts that eye-tracking in combination with machine learning can create for researchers and marketers. Collapse

Topics: decision making social commerce website user requirement web page

Methods: oculometry computational algorithm ensemble learning machine learning eye tracking tool

Theories: cognitive attention theory

Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning

2023 | Decision Support Systems | Citations: 0

Authors: Wang, Bing; Li, Weizi; Bradlow, Anthony; Bazuaye, Eghosa; Chan, Antoni T.Y.

Abstract: Effective and rapid triaging from primary care into secondary care plays a pivot ... Expand

Abstract: Effective and rapid triaging from primary care into secondary care plays a pivotal role in providing patients with timely treatment and managing increasing demands for healthcare resources. Existing triaging methods from primary care to secondary care are labor-intensive processes that involve manually reviewing referral data from multiple sources and can cause long referral to treatment time. There has been no research using machine learning methods that automatically analyzes heterogeneous data including referral letters to recognize regu­ larities to support the primary to secondary care triage. In this paper, we propose a heterogeneous data-driven hybrid machine learning model including Natural Language Processing (NLP) to improve hospital triage effi­ ciency at the point of triage. The proposed model achieved a precision of 0.83, recall of 0.82, F1-Score of 0.83, accuracy of 0.82, AUC of 0.90 in identifying patients with non-inflammatory conditions (NIC) and inflammatory arthritis (IA) at the point of triage with explainable risk stratifications. Our model is piloted in a real-world trial in a large secondary care hospital in the UK to compare referral accuracy and time saved between our model and clinicians, and evaluate its acceptability by users. Our model achieved precision and recall of 0.83 and 0.81, compared with the precision and recall of 0.80 and 0.78 by clinicians. The research also shows that our model enabled decision support can save clinicians 8 h per week in assessing the referral assessment. This paper is the first study to streamline hospital triage from primary care to secondary care using machine learning. Collapse

Topics: local interpretable model agnostic explanation decision support decision support system data governance data protection

Methods: machine learning BERT data imputation natural language processing ensemble learning