The Roles of Opinion Leaders in Elevating Online Health Discussion: Evidence from Reddit

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Alazazi, Massara; Wang, Bin

Abstract: Social networking sites and online communities are used by billions of people g ... Expand

Abstract: Social networking sites and online communities are used by billions of people globally. It is important to understand the participation in these collaborative platforms and the roles different participants play. In this study, we examine the roles both local and global opinion leaders play and their influence on the community using content related to Covid mental health from Reddit. We employed social network analysis to identify opinion leaders based on their network position and connections, and we used sentiment analysis and econometric models to examine their impact on online health community participation. Our study reveals that global opinion leaders have more influence on thread participation than local opinion leaders. In addition, negative emotions expressed encourage more participants' involvement in online health community discussion. Collapse

Topics: electronic health Reddit covid 19 social network website

Methods: sentiment analysis social network analysis regression analysis method econometric modeling statistical hypothesis test

A temporal graph framework for intelligence extraction in social media networks

2023 | Information & Management | Citations: 0

Authors: Chung, Wingyan; Lai, Vincent S.

Abstract: Social media networks (SMNs) are increasingly used in professional management of ... Expand

Abstract: Social media networks (SMNs) are increasingly used in professional management of knowledge workers and related assets. However, the factors affecting behavioral trends and activity levels in these networks are not well understood. Although social and cognitive theories can help to explain human behavior in traditional social networks, their application to SMNs has not been validated. Traditional social network modeling techniques may not accurately predict real-world SMN activities. This research developed a temporal graph framework for in­ telligence extraction in SMNs. Theory-based, data-driven models (Conformity Model (COM), Recency-Primacy Model (REM), Trend Interaction Model (TIM), Periodic Interaction Model (PIM)) were developed based on the framework to capture various aspects of user behavior: conformity effect, recency, primacy, periodicity, and dynamic trend. The models capture the activity history and dynamically combine pricing information to enhance predictive accuracy. Using data of 83,536 GitHub software repositories on cryptocurrency, this article reports the results of experiments that compare the models’ performance in predicting SMN activities over time. Experi­ mental results show that the model (REM) that captures recency/primacy effects of human cognitive processing outperformed other models in 9 (out of 18) measures pertaining to engagement, contribution, influence, and popularity. Primacy plays a dominant role in predicting engagement, contribution, and popularity, whereas recency plays a key role in predicting influence. Short-term trend (modeled with TIM) was found to yield significantly better performance on predicting user contribution. The models also outperformed an integrated machine learning (IML) model by most measures. Overall, the effects modeled by REM and TIM were found to be more significant than the effects modeled by COM, PIM, and IML. The research contributes to enhancing un­ derstanding of SMN behavior, developing new models to simulate and predict SMN activities, and designing new artifacts for information systems practitioners to manage knowledge assets and to extract SMN intelligence. Collapse

Topics: cryptocurrency social network systems development situation awareness user behavior

Methods: experiment machine learning longitudinal research computational algorithm time series analysis

Theories: psychological theory social contagion theory social interactionist theory social impact theory social sciences theory

Consensus-trust Driven Bidirectional Feedback Mechanism for Improving Consensus in Social Network Large-group Decision Making

2023 | Group Decision and Negotiation | Citations: 0

Authors: Gai, Tiantian; Cao, Mingshuo; Chiclana, Francisco; Zhang, Zhen; Dong, Yucheng; Herrera-Viedma, Enrique; Wu, Jian

Abstract: This paper proposes a consensus-trust driven framework of bidirectional interact ... Expand

Abstract: This paper proposes a consensus-trust driven framework of bidirectional interaction for social network large-group decision making. Firstly, the concepts of interaction consensus threshold and interaction trust threshold are defined, which are used to discriminate the interaction modes between subgroups into four categories. Corresponding hybrid feedback strategies are designed in which the consensus level and trust level of subgroups are regarded as reliable resources to facilitate the achievement of group consensus. Secondly, a minimum adjustment bidirectional feedback model considering cohesion is developed to help the interacting subgroups reach mutual consensus with minimum opinion modification. Finally, the proposed consensus framework is applied to a blockchain platform selection problem in supply chain to demonstrate the effectiveness and applicability of the model. Collapse

Topics: blockchain supply chain management decision making social network

Methods: computational algorithm simulation social network analysis trade-off analysis method multi objective optimization

Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework

2023 | Information Systems Frontiers | Citations: 0

Authors: Hajek, Petr; Abedin, Mohammad Zoynul; Sivarajah, Uthayasankar

Abstract: Mobile payment systems are becoming more popular due to the increase in the numb ... Expand

Abstract: Mobile payment systems are becoming more popular due to the increase in the number of smartphones, which, in turn, attracts the interest of fraudsters. Extant research has therefore developed various fraud detection methods using supervised machine learning. However, sufficient labeled data are rarely available and their detection performance is negatively affected by the extreme class imbalance in financial fraud data. The purpose of this study is to propose an XGBoost-based fraud detection framework while considering the financial consequences of fraud detection systems. The framework was empirically validated on a large dataset of more than 6 million mobile transactions. To demonstrate the effectiveness of the proposed framework, we conducted a comparative evaluation of existing machine learning methods designed for modeling imbalanced data and outlier detection. The results suggest that in terms of standard classification measures, the proposed semi-supervised ensemble model integrating multiple unsupervised outlier detection algorithms and an XGBoost classifier achieves the best results, while the highest cost savings can be achieved by combining random under-sampling and XGBoost methods. This study has therefore financial implications for organizations to make appropriate decisions regarding the implementation of effective fraud detection systems. Collapse

Topics: fraud detection mobile system mobile payment electronic payment Python

Methods: XGBoost machine learning anomaly detection supervised learning experiment

An empirical study of content-based recommendation systems in mobile app markets

2023 | Decision Support Systems | Citations: 0

Authors: Jozani, Mohsen; Liu, Charles Zhechao; Choo, Kim-Kwang Raymond

Abstract: Recommendation systems are widely used to promote product visibility and sales. ... Expand

Abstract: Recommendation systems are widely used to promote product visibility and sales. However, past research suggests that they primarily benefit market superstars and therefore, can be detrimental to niche products. This is especially critical in superstar dominated markets such as the market of mobile apps. This study incorporates social network analysis and econometric models to empirically examine the impact of content-based filtering (CBF) recommendation systems on the distribution of demand in mobile app markets. The analysis results of two comprehensive panel datasets from App Store and Google Play suggest that CBF recommendations favor niche items and effectively boost the long tail of the market. Moreover, consistent with signaling theory, relative quality signals provided by recommendation systems can impact consumer decision making process, leading to a spillover of demand. Our findings provide important implications for developers and market operators to better promote their products in the highly competitive market of mobile apps. Collapse

Topics: mobile application mobile application market digital distribution recommender system Google+

Methods: computational algorithm robustness check instrumental variables estimation longitudinal research statistical hypothesis test

Theories: signaling theory

A SYSTEMATIC REVIEW OF COMPUTATIONAL METHODS IN AND RESEARCH TAXONOMY OF HOMOPHILY IN INFORMATION SYSTEMS

2023 | European Conference On Information Systems | Citations: 0

Authors: LI, Yisheng; Vatrapu, Ravi; Zihayat, Morteza

Abstract: Homophily is both a principle for social group formation with like-minded peopl ... Expand

Abstract: Homophily is both a principle for social group formation with like-minded people as well as a mechanism for social interactions. Recent years have seen a growing body of management research on homophily particularly on large-scale social media and digital platforms. However, the predominant traditional qualitative and quantitative methods employed face validity issues and/or are not well-suited for big social data. There are scant guidelines for applying computational methods to specific research domains concerning descriptive patterns, explanatory mechanisms, or predictive indicators of homophily. To fill this research gap, this paper offers a structured review of the emerging literature on computational social science approaches to homophily with a particular emphasis on their relevance, appropriateness, and importance to information systems research. We derive a research taxonomy for homophily and offer methodological reflections and recommendations to help inform future research.Although computational methods are increasingly being adopted for and adapted to information systems (IS) research in general and homophily research in particular, there is a dearth of comprehensive reviews and guidelines of research methodologies appropriate to homophily in digital systems. Most extant literature either qualitatively measures perceived homophily with pre-validated surveys (e.g., "My new Facebook friend: "is similar to/like me") or quantitatively tests hypotheses about homophily with regression models (Dvir-Gvirsman, 2017;Liu and Brown, 2014;Shalizi and Thomas, 2011;Song et al., 2019). Nevertheless, traditional survey-based methods may face threats to construct validity while many regression models like logistic regression may not be a fit for some social network relationships in which observations are not independent (Chipidza and Tripp, 2018;Lazer et al., 2020). To fill the research lacuna, our paper is motivated to offer a structured review of the methodologies being leveraged to study homophily with special emphasis on state-of-the-art computational methods in IS application domains. Our review outlines a roadmap to big data-driven studies in IS by focusing on patterns, explanations, and predictions of homophily in business and social commerce settings and highlighting methodological novelty such as blending traditional research methods with machine learning (ML) that informs future research design. We propose a taxonomy that classifies research themes, data collection techniques, and computational methods in data-driven management research on the mass digitization of homophily. The rest of this paper is organized as follows. Section 2 describes the methodological foundations drawn from the reference discipline of computational social science (CSS). Section 3 presents our literature review procedure. Section 4 reviews two CSS approaches particularly suitable for homophily-social network analysis (SNA) and text analytics. Section 5 illustrates their utility in understanding different dimensions of homophily, especially in the application domains of IS. Section 6 outlines a taxonomy and offers several methodological guidelines for future research. Collapse

Topics: recommender system explainable artificial intelligence privacy database system Facebook

Methods: natural language processing literature study social network analysis survey regression analysis method

Security Networks as an Effective Means to Reduce Spear Phishing Susceptibility

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Lamprecht, Robert; Haid, Franziska; Eckhardt, Andreas

Abstract: Over the past few years, cybersecurity has gained importance for organizations ... Expand

Abstract: Over the past few years, cybersecurity has gained importance for organizations as social engineering attacks in general, and in particular phishing attacks evolved. Previous phishing research focused on improving the susceptibility of individuals towards phishing attacks, while collective security behavior has been neglected. In this emergent research forum paper, we aim to contribute to the existing phishing literature by extending the scope from the individual towards the collective and the effect of security networks on phishing susceptibility. The work plans to evaluate the effect of trust, interdependence, connectivity, relational strength, position within a network and the impact within a network on spear phishing susceptibility. Thus, our work intends to show whether security networks can improve the collective behavior when being targeted on online social networks. Collapse

Topics: phishing data security IT security information security effectiveness social network

Methods: randomized field experiment field experiment social network analysis theoretical contribution conceptual modelling

A Linguistic Cloud-Based Consensus Framework with Three Behavior Classifications Under Trust-Interest Relations

2023 | Group Decision and Negotiation | Citations: 0

Authors: Liu, Weiqiao; Zhu, Jianjun; Liu, Peide; Wang, Peng; Song, Wen

Abstract: In the consensus reaching process (CRP), experts are not infinitely compromising ... Expand

Abstract: In the consensus reaching process (CRP), experts are not infinitely compromising when adjusting opinions, and the “compromise limit” is used to reflect the limited compromise behaviors in the process of expert opinion adjustment. In complex and multiple social relations, experts tend to exhibit different behaviors according to their compromise limits. This paper aims to develop a novel CRP framework to promote a consensus that categorizes and manages experts based on their compromise limits. Firstly, the trust-interest network is defined to represent the multiple relations among experts, and the expert weights are calculated by considering the impact of interest manipulation on trust relations. Secondly, a novel cloud model-based minimum cost consensus model is established, which considers the mutual acceptance between the experts and the group, as well as the changes in the ranges of experts’ hesitation and the collective acceptance. Thirdly, three behavior classifications are defined based on individual compromise limits and group acceptance ranges: cooperative behavior, hesitating non-cooperative behavior, and strong non-cooperative behavior, and a CRP optimization model is constructed to manage the three behaviors. Finally, a numerical example is given to illustrate the validity and superiority of the proposed model. Collapse

Topics: cloud computing social network decision making

Methods: optimization model cross sectional research group interview computational algorithm social network analysis

Theories: optimization theory

A Look Through a Broken Window: The Relationship Between Disorder and Toxicity on Social Networking Sites

2023 | European Conference On Information Systems | Citations: 0

Authors: Messerschmidt, Nils; Gundlach, Jana; Baumann, Annika

Abstract: Toxicity has increased on social networking sites (SNSs), sparking a debate on ... Expand

Abstract: Toxicity has increased on social networking sites (SNSs), sparking a debate on its underlying causes. While research readily explored eligible social factors, disorder induced by the very nature of SNSs has been neglected so far. The relationship between disorder and deviant behaviors could be revealed within the offline sphere. Incorporating the theoretical lens of the Broken Windows Theory, we propose that a similar mechanism is prevalent in the online context. To test the hypothesis that perceived disorder increases toxicity on SNSs, the study compares two subcommunities on Reddit dedicated to the same topic that differ in their perceived disorder. Sampling the toxicity scores via data collection and natural language processing yields the first evidence for our hypothesis. We further outline subsequent studies that aim to investigate further the phenomenon of how disorder-related factors contribute to toxic online environments. Collapse

Topics: Reddit criminality application programming interface cloud computing

Methods: natural language processing experimental design qualitative coding survey design survey

Differential Impacts of Technology-Network Structures on Cost Efficiency: Knowledge Spillovers in Healthcare

2023 | Journal of Management Information Systems | Citations: 0

Authors: Nwafor, Onyi; Ma, Xiao; Johnson, Norman A.; Singh, Rahul; Aron, Ravi

Abstract: We examine how hospital cost efficiency can improve because of knowledge spillov ... Expand

Abstract: We examine how hospital cost efficiency can improve because of knowledge spillover effects, arising from the experiences of members of a healthcare system. We identify two types of functional technology networks—that we term repository-type and workflow-type networks—which are defined by the adoption patterns of different types of electronic health record (EHR) systems application functions by hospitals in a health system. Additionally, we examine how structural features of these two network types—namely, network centrality and interconnectedness—impact such knowledge spillover effects. By analyzing seven years of panel data for 1,420 U.S. hospitals across 216 health systems, and data obtained from surveys of healthcare experts, we found that greater centrality of nodes in repository-type networks enhances the influence of partners' experiences on cost efficiency but has the opposite effect in workflow-type networks, while greater interconnectedness of workflow-type networks enhances the influence of partners' experiences on cost efficiency. Interestingly, we found the opposite effect in repository-type networks, where greater interconnectedness diminishes the influence of partners' experiences on cost efficiency. We discuss reasons for this surprising and counterintuitive finding. Overall, our results demonstrate that at least in healthcare settings where information technologies enable knowledge-sharing networks to be formed, benefits can accrue through knowledge spillover effects; moreover, variations across functional categories of technologies adopted in a network context can result in different technology-network structures with differential impacts on hospital cost efficiency because of their influence on knowledge spillover at both the hospital and system levels. Not only can these results explain mixed findings in the literature that has been dominated by a focus on cost efficiency outcomes dependent solely on the EHR adoption choices of individual hospitals, but they also provide the basis for guidelines for a network-based perspective of technology adoption. We discuss other possible settings to which our empirical findings can be reasonably generalized, including supply chain technologies and the Internet of Things. Collapse

Topics: health information system knowledge sharing knowledge spillover technology adoption IT manager

Methods: longitudinal research robustness check descriptive statistic parametric test ANOVA

Theories: social network theory