What Do They Meme? Exploring the Role of Memes as Cultural Symbols of Online Communities

2023 | International Conference on Information Systems | Citations: 0

Authors: Henn, Theresa; Posegga, Oliver

Abstract: Analyzing symbols shared within online communities (OCs) is essential to better ... Expand

Abstract: Analyzing symbols shared within online communities (OCs) is essential to better understand communities' expressed cultures. To evaluate how OCs differ in their expressed culture and analyze the effects of community rules (CR) and moderation policies (MP), we examined meme sharing of subreddit and interaction communities on Reddit. To detect memes shared within subreddits automatically, we trained a convolutional neural network and applied a feature-matching algorithm to create meme networks with components consisting of visually similar memes. Based on each community's component composition, we created community-specific meme languages that we compared across subreddit and interaction communities. Our results show that memes can be aggregated to characteristic meme languages linked to individual OCs; yet MP and CR do not impact the homogeneity of shared memes. Based on these findings, we plan to analyze dynamically the relationship between memes and OCs, examining memes' textual content and diving deeper into users' individual meme languages. Collapse

Topics: Reddit internet technology social media

Methods: computational algorithm nonparametric test Mann Whitney U test convolutional neural network Keras

A novel approach for fraudulent reviewer detection based on weighted topic modelling and nearest neighbors with asymmetric Kullback–Leibler divergence

2022 | Decision Support Systems | Citations: 0

Authors: Zhang, Wen; Xie, Rui; Wang, Qiang; Yang, Ye; Li, Jian

Abstract: The task of detecting fraudulent reviewers is of great importance to E-commerce ... Expand

Abstract: The task of detecting fraudulent reviewers is of great importance to E-commerce platforms. Existing research has invested much effort into developing comprehensive features and advanced techniques to detect fraudulent reviewers. However, most of these studies have ignored the data imbalance problem inherent in fraudulent reviewer detection: non-fraudulent reviewers are the majority, while fraudulent reviewers are the minority in real practice. To fill this gap, we propose a novel approach called ImDetector to detect fraudulent reviewers while handling data imbalance based on weighted latent Dirichlet allocation (LDA) and Kullback–Leibler (KL) divergence. Specifically, we develop a weighted LDA model to extract the latent topics of reviewers distributed on the review features. Asymmetric KL divergence is adopted to make the similarity measure between reviewers biased toward the fraudulent minority when using the K-nearest-neighbor for classification. By mapping the reviewers to the latent topics of features derived from the weighted LDA model and measuring the similarities between reviewers using asymmetric KL divergence, the data imbalance problem in fraudulent reviewer detection is alleviated. Extensive experiments on the Yelp.com dataset demonstrate that the proposed ImDetector approach is superior to the state-of-the-art techniques used for fraudulent reviewer detection. We also explain the experimental results and present the managerial implications of this paper. Collapse

Topics: online review fraud detection knowledge representation network effect website

Methods: machine learning experiment nonparametric test Mann Whitney U test supervised learning

Learning from Crowdsourced Multi-labeling: A Variational Bayesian Approach

2021 | Information Systems Research | Citations: 0

Authors: Yin, Junming; Luo, Jerry; Brown, Susan A.

Abstract: Microtask crowdsourcing has emerged as a cost-effective approach for obtaining l ... Expand

Abstract: Microtask crowdsourcing has emerged as a cost-effective approach for obtaining large-scale labeled data. Crowdsourcing platforms, such as MTurk, provide an online marketplace where task requesters can submit a batch of microtasks for a crowd of workers to complete for a small monetary compensation. As the information collected from a crowd can be prone to errors, additional algorithmic techniques are needed to infer the ground truth labels from noisy annotations by workers with heterogeneous quality. Moreover, it would be very beneficial to identify and possibly filter out low-quality workers to foster the creation of a healthy and sustainable crowdsourcing ecosystem. Much of the existing literature on crowd labeling has focused on the single-label setting. However, in many application domains, it is common that each item to be annotated can be assigned to multiple categories simultaneously. In this paper, we present a variety of new approaches for modeling label dependency and worker quality in the context of multi-label crowdsourcing. To capture label dependency, we introduce three methods based on a Bayesian mixture of Bernoulli distributions, its Dirichlet process extension, and a multivariate logit-normal distribution. We also propose two distinct generative models for characterizing shared and hierarchical structures of worker quality. Efficient collapsed and Laplace variational inference algorithms are then developed to jointly infer ground truth labels and worker quality. Extensive simulation and MTurk experiments show that the models based on integrating Bernoulli mixtures and shared structure of worker quality achieve a significant improvement over other state-of-the-art methods. Our study clearly highlights that joint and effective modeling of label dependency and worker quality is crucial to the design of a multi-label crowdsourcing system. The proposed framework also has great potential to be extended to a broader range of applications, in which different opinions need to be combined to measure multiple perspectives of an object. Collapse

Topics: crowdsourcing cloud computing Twitter brainstorming online review

Methods: computational algorithm experiment simulation Bayesian analysis machine learning

Affordable Uplift: Supervised Randomization in Controlled Experiments

2019 | International Conference on Information Systems | Citations: 2

Authors: Haupt, Johannes; Jacob, Daniel; Gubela, Robin; Lessmann, Stefan

Abstract: Customer scoring models are the core of scalable direct marketing. Uplift models ... Expand

Abstract: Customer scoring models are the core of scalable direct marketing. Uplift models provide an estimate of the incremental benefit from a treatment that is used for operational decision-making. Training and monitoring of uplift models require experimental data. However, the collection of data under randomized treatment assignment is costly, since random targeting deviates from an established targeting policy. To increase the costefficiency of experimentation and facilitate frequent data collection and model training, we introduce supervised randomization. It is a novel approach that integrates existing scoring models into randomized trials to target relevant customers, while ensuring consistent estimates of treatment effects through correction for active sample selection. An empirical Monte Carlo study shows that data collection under supervised randomization is cost-efficient, while downstream uplift models perform competitively. Collapse

Topics: marketing management telephone information privacy concern computer based testing data quality

Methods: experiment experimental group Monte Carlo simulation simulation logistic regression

Theories: marketing theory

The attitude cube—A three-dimensional model of situational factors in IS adoption and their impact on the attitude–behavior relationship

2015 | Information & Management | Citations: 0

Authors: Kroenung, Julia; Eckhardt, Andreas

Abstract: Since the introduction of the TAM by Davis et al. (Manage. Sci. 35 (1989) 982–10 ... Expand

Abstract: Since the introduction of the TAM by Davis et al. (Manage. Sci. 35 (1989) 982–1003), user attitudes have been considered an important determinant of IS adoption. Their impact on behavior is rooted in behavioral frameworks such as the theory of reasoned action (Fishbein and Ajzen, Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Addison-Wesley, 1975) and the theory of planned behavior (Ajzen, Organ. Behav. Hum. Decis. Processes. 50 (1991) 179–211). However, in IS adoption models, the predictive power of attitudes for behavior as an endogenous variable has proven strong in some circumstances and weak in others (Bagozzi and Yi, Soc. Psychol. Q. 52 (1989) 266–279; Taylor and Todd, MIS Q. 19 (1995) 561–570). These inconsistencies resulted in an exclusion of the construct attitude from further modified versions of the TAM and other models explaining individual IS adoption and use. To better understand the prevalence of inconsistent attitude–behavior relationships in IS adoption models, this research follows social psychological research practices focusing on situational factors prone to result in insignificant attitude–behavior relationships. To test for the impact of these factors on the attitude–behavior relationship, the article raises the level of analysis to a higher level of abstraction. A scientometric literature review and a meta-analysis of 14 top IS journals from 1989 to 2014 finds that three out of four situational factors positively influence the attitude–behavior relationship in the IS fields: voluntariness, technology type, and adoption context. These factors constitute “the attitude cube”, which provides conceptual guidance for researchers in assessing the conditions under which the attitude–behavior relationship is likely to be weak or strong. Collapse

Topics: technology adoption missing data user attitude IS discipline information system use

Methods: bibliometric analysis literature sample Kruskal Wallis test nonparametric test statistical hypothesis test

Theories: technology acceptance model

The MOOCs are Coming! Revolution or Fad in the Business School?

2014 | Communications of the Association for Information Systems | Citations: 0

Authors: Ayala, Cesar; Dick, Geoffrey; Treadway, Jayson

Abstract: The massive open online course (MOOC) phenomenon has been the subject of extraor ... Expand

Abstract: The massive open online course (MOOC) phenomenon has been the subject of extraordinary hype over the last 12 months. This paper represents the first academic study of student satisfaction with a MOOC—this one experienced as part of an information systems class in an MBA program. We developed a causal model based on the literature was and measured 1) student satisfaction with the learning experience and 2) students’ desire to take more courses this way. Using structural equation modeling, the analysis indicated that content in terms of quality and course materials, along with the opportunity for college credit, were the dominant factors in satisfaction, which, in turn, influenced the desire for more courses. The paper ends with a call for action by university administrators to proactively manage this new technology rather than adopt a “wait and see” attitude. Collapse

Topics: online learning system internet technology IS management

Methods: survey partial least squares regression survey design nonparametric test ethnography

Flaming in the Electronic Classroom

1997 | Journal of Management Information Systems | Citations: 3

Authors: Reinig, Bruce A.; Briggs, Robert O.; Nunmaker Jr., Jay F.

Abstract: Research has shown that group support systems (GSS) can be used to substantially ... Expand

Abstract: Research has shown that group support systems (GSS) can be used to substantially improve learning at all levels of formal education. However, some instructors are reluctant to adopt GSS because they are concerned that learners will engage in flaming, or verbal attacks intended to offend either persons or organizations and often characterized by profanity, obscenity, and insults. This paper posits that flaming is caused by hostility engendered by a perception that vested interests have been or are about to be violated. It further posits that the relationship between hostility and flaming is moderated by personal values and risk of reprisal. A longitudinal experiment is presented that provides empirical support for the model. The paper then reports five field cases highlighting various strategies used by instructors for reducing or eliminating flaming. Collapse

Topics: group support system anonymity

Methods: experiment experimental group Wilcoxon signed-rank test nonparametric test cross sectional research

Testing the Determinants of Microcomputer Usage via a Structural Equation Model

1995 | Journal of Management Information Systems | Citations: 137

Authors: Igbaria, Magid; Guimaraes, Tor; Davis, Gordon B.

Abstract: The objective of this study was to develop and test an integrated conceptual mod ... Expand

Abstract: The objective of this study was to develop and test an integrated conceptual model of microcomputer usage. This study used the technology acceptance model and sought to extend it by investigating the impact of the external factors (i.e., individual, organizational, and system characteristics) on the user acceptance of microcomputer technology. The paper reports the results of a field study investigating the determinants of microcomputer usage. The analyses of the measurement model confirm the existence of two distinct constructs of (1) beliefs-perceived usefulness and perceived ease of use; (2) organizational support-management support and end-user computing (EUC) support; and (3) microcomputer usage-perceived usage and variety of use. The tested conceptual model confirms the effects of individual, organizational, and system characteristics on perceived ease of use and perceived usefulness. The model also confirms the influence of perceived ease of use on perceived usefulness, and the effects of perceived usefulness on perceived usage and variety of use. Results confirm several previously proposed notions, including the effects of individual, organizational, and system characteristics on ease of use and usefulness; the influence of ease of use on usefulness, and the effects of perceived usefulness on usage and variety of use. The results demonstrate the utility of investigating factors contributing to microcomputer usage and the external factors affecting endogenous variables such as system usefulness. The importance of EUC support and management support is corroborated, as well as the need for designing mechanisms such as training programs and newsletters to improve user perceptions of microcomputers. Collapse

Topics: personal computer perceived usefulness perceived ease of use user training user acceptance

Methods: partial least squares regression conceptual modelling survey path analysis factor analysis

Theories: technology acceptance model theory of planned behavior

Examining the Feasibility of a Case-Based Reasoning Model for Software Effort Estimation

1992 | Management Information Systems Quarterly | Citations: 0

Authors: Mukhopadhyay, Tridas; Vicinanza, Steven; Prietula, Michael

Abstract: Existing algorithmic models fail to produce accurate software development effor ... Expand

Abstract: Existing algorithmic models fail to produce accurate software development effort estimates. To address this problem, a case.based reasoning model, called Estor, was developed based on the verbal protocols of a human expert solving a set of estimation problems. Estor was then presented with 15 software effort estimation tasks. The estimates of Estor were compared to those of the expert as well as those of the function point and COCOMO estimations of the projects.The estimates generated by the human expert and Estor were more accurate and consistent than those of the function point and COCOMO methods. In fact, Estor was nearly as accurate and consistent as the expert. These results sug.' An earlier version of this paper was presented at the International Conference on Information Systems, Copenhagen, Denmark, December 1990.gest that a case-based reasoning approach for software effort estimation holds promise and merits additional research. Collapse

Topics: problem solving knowledge base IT investment systems development computer software

Methods: statistical hypothesis test correlation analysis Wilcoxon signed-rank test nonparametric test sensitivity analysis

Theories: theory of case-based reasoning theory of problem solving