Towards a thematic dimensional framework of online fraud: An exploration of fraudulent email attack tactics and intentions

2023 | Decision Support Systems | Citations: 1

Authors: Bera, Debalina; Ogbanufe, Obi; Kim, Dan J.

Abstract: Despite anti-phishing filters, social engineering-based cyber-attacks still resu ... Expand

Abstract: Despite anti-phishing filters, social engineering-based cyber-attacks still result in billions of dollars lost annually, significant personal identity theft, loss of corporate secrets, and espionage. We review the phishing literature on psychological attacks and design tactics employed for deception by attackers. The result reveals the need to continuously identify tactics embedded in fraudulent email content to understand why users still fall prey to phishing attacks. Using the literature as a backdrop, we identify and conceptualize a thematic dimensional framework of fraudulent phishing attacks. This study uses benchmark datasets of fraudulent email to empirically validate the proposed dimensional framework. Specifically, a combination of machine learning-based content analysis and topic modeling found a majority of the discovered topics were labeled against the proposed di­ mensions. Statistical analysis was employed to provide empirical evidence of the thematic dimensions of fraudulent email attacks. This study thus, not only extends the cybersecurity literature by identifying and vali­ dating eight dimensions that enrich our understanding of phishing and attack identification but stimulates cu­ mulative research endeavors to develop yet more comprehensive dimensional frameworks of phishing email attacks. Collapse

Topics: phishing email phishing electronic mail email spam personalization

Methods: term frequency–inverse document frequency natural language processing topic model discriminant analysis literature study

Coastal Resilience with Social Data Analytics: A Design Science Approach

2023 | International Conference on Information Systems | Citations: 0

Authors: Lee, Carol; Yoon, YoungHo; Bharati, Pratyush

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Topics: Twitter individual impact decision making privacy user interface

Methods: design science topic model design artifact design principle natural language processing

Elaboration likelihood model, endogenous quality indicators, and online review helpfulness

2022 | Decision Support Systems | Citations: 7

Authors: Chou, Yen-Chun; Chuang, Howard Hao-Chun; Liang, Ting-Peng

Abstract: Given strong influences of online customer reviews on consumer purchase decision ... Expand

Abstract: Given strong influences of online customer reviews on consumer purchase decisions, identifying helpful reviews has received broad attention from practitioners and researchers. The elaboration likelihood model (ELM) has been adopted to explain the review feature–helpfulness link. However, when analyzing reviews from websites, existing studies tend to ignore that quality indicators such as length and readability are merely cues and have not circumvented endogeneity induced by unseen argument quality. Hence, we propose an extended ELM applica­ tion to observational data on review helpfulness. We develop a research model that integrates relevant quality indicators and sentiment features based on a circumplex model of affect. To test our hypotheses, we use pub­ lically available review datasets from three platforms (Amazon.com, Drugs.com, and Yelp.com) and adopt an instrument-free method that allows for arbitrary correlations between unseen argument quality and multiple endogenous indicators. Our analysis shows that ignoring endogeneity would result in invalid effect size and hypothesis-testing. In addition to identifying effects of endogenous quality indicators on review helpfulness, we find asymmetric effects of positive and negative valence contingent on low or high arousal. By articulating conceptual pitfalls and illustrating empirical remedies, our study aims to be a prototypical example of performing ELM-grounded analyses of online customer reviews. Collapse

Topics: tablet online review user-generated content electronic commerce website

Methods: product review regression analysis method statistical hypothesis test topic model sentiment analysis

Theories: elaboration likelihood model

Coastal Resilience Decision Making with Machine Learning

2022 | Americas Conference on Information Systems | Citations: 2

Authors: Lee, Carol; Yoon, YoungHo; Bharati, Pratyush

Abstract: Our research aims to understand how social data can be integrated with climate ... Expand

Abstract: Our research aims to understand how social data can be integrated with climate data using machine learning for coastal resilience decisions. Although data analytics techniques have been adapted for decision models, incorporating unstructured data is a challenge. We adapt a design science research approach to develop a coastal resilience decision model that can accommodate various sets of climate criteria and social attributes to help us understand coastal risks in communities vulnerable to coastal hazards. We collected social data from environmental groups and individuals and conducted an exploratory social media data analysis on coastal resilience in the greater Boston, U.S., area. We employ non-negative matrix factorization (NMF), a topic modeling technique, to extract human-interpretable topics from a preliminary dataset of 131 documents from 50 different accounts. The outcomes of this research can help community members and policy makers understand and develop robust sustainability and climate focused decisions. Collapse

Topics: social media climate change decision making Twitter Facebook

Methods: non negative matrix factorization data transformation topic model design artifact design science

The Impact of Helping Others in Coopetitive Crowdsourcing Communities

2021 | Journal of the Association for Information Systems | Citations: 9

Authors: Dissanayake, Indika; Nerur, Sridhar; Wang, Jingguo; Yasar, Mahmut; Zhang, Jie

Abstract: Organizations are increasingly engaging the community through crowdsourcing plat ... Expand

Abstract: Organizations are increasingly engaging the community through crowdsourcing platforms to evolve innovative solutions to challenging business problems. Participants on such platforms often simultaneously cooperate and compete with one another to earn top honors. This paper addresses the imperative to understand the dynamics of knowledge sharing in such a coopetitive environment. Specifically, our study relies on the conceptual foundations of social exchange and social capital theories to investigate how help rendered (e.g., exchanging ideas or sharing knowledge) by participants in an online coopetitive crowdsourcing setting affects their performance. Furthermore, the study examines the moderating effects of the intensity of competition. Results of our econometrics analyses suggest that help given in a highly competitive contest, as opposed to a less competitive one, is more likely to be reciprocated, but less likely to improve the contributor’s contest performance. In addition, our study found that help received by participants positively impacts their contest performance, and partially mediates the relationship between help rendered and contest performance. This research also provides insight into what motivates participants to share knowledge under conditions of coopetition. The findings of our study have strong implications for both theory and practice. Collapse

Topics: crowdsourcing knowledge sharing social capital online community open source

Methods: instrumental variables estimation machine learning data transformation robustness check theoretical contribution

Theories: social exchange theory social capital theory

Measuring Sustainability through Textual Analysis

2021 | International Conference on Information Systems | Citations: 0

Authors: Maibaum, Frederik; Foege, Johann Nils

Abstract: The global climate crisis makes the transition towards a sustainable society th ... Expand

Abstract: The global climate crisis makes the transition towards a sustainable society the central challenge of our time. Such a transition is only possible if the global economy engages substantial efforts to reduce its ecological footprint. While IS scholars agree that it is important to identify organizational change activities that support such a sustainable development, it remains challenging to capture firms' sustainability in a quantitative, longitudinal way. In this paper, we describe the development of a text-based measure for firms' sustainability that enables us to assess how IS affect firms' sustainability in a longitudinal setting. Our approach uses machinelearning textual analysis to create a sustainability dictionary that we use to calculate a sustainability score based on 10-k filings and earnings calls for S&P 500 firms from 1994 to 2017. This score will allow us to better understand the sustainability implications of (green) IS. Collapse

Topics: database system green IS high-performance computing organization structure digital innovation

Methods: topic model longitudinal research qualitative content analysis survey computational algorithm

Predicting donation behavior: Acquisition modeling in the nonprofit sector using Facebook data

2021 | Decision Support Systems | Citations: 0

Authors: Schetgen, Lisa; Bogaert, Matthias; Van den Poel, Dirk

Abstract: The purpose of this study is to demonstrate the value of Facebook data in predic ... Expand

Abstract: The purpose of this study is to demonstrate the value of Facebook data in predicting first-time donation behavior. More specifically, we provide evidence that Facebook data can be used as a valuable data source for nonprofit organizations in acquiring new donors. To do so, we evaluate three different dimensionality reduction techniques (i.e., singular value decomposition, non-negative matrix factorization, and latent Dirichlet allocation) over seven classification techniques (i.e., logistic regression, k-nearest neighbors, bagged trees, random forest, adaboost, extreme gradient boosting, and artificial neural networks) using five times twofold cross-validation. Next, we assess what type of Facebook data and which predictors are most important. The results indicate that we can predict first-time donation behavior based on Facebook data with high predictive performance. Our benchmark indicates that the combination of singular value decomposition and logistic regression outperforms all other analytical methodologies with an area under the receiver operating characteristic of 0.72 and a top decile lift of 3.33. The results show that Facebook pages and categories of Facebook pages are the most important data types. The most important predictors are dimensions related to age, education, residence, materialism, responsible consumption, and interest in nonprofits. The presented acquisition models can be used by nonprofit organiza­ tions to implement a one-to-one targeted marketing campaign towards Facebook fans. To the best of our knowledge, our study is the first to determine the predictive value of Facebook data for nonprofits in a real-life acquisition context. Collapse

Topics: Facebook social media decision support system web page source code

Methods: machine learning data transformation computational algorithm dimensionality reduction F-test

Measuring Brand Favorability Using Large-Scale Social Media Data

2021 | Information Systems Research | Citations: 0

Authors: Zhang, Kunpeng; Moe, Wendy

Abstract: For decades, brand managers have monitored brand health with the use of consumer ... Expand

Abstract: For decades, brand managers have monitored brand health with the use of consumer surveys, which have been refined to address issues related to sampling bias, response bias, leading questions, etc. However, with the advance of Web 2.0 and the internet, consumers have turned to social media to express their opinions on a variety of topics and, subsequently, have generated an extremely large amount of interaction data with brands. Analyzing these publicly available data to measure brand health has attracted great research attention. In this study, we focus on developing a method to measure brand favorability while accounting for the measure biases exhibited by social media posters. Specifically, we propose a probabilistic graphical model–based collective inference framework and implement a block-based Markov chain Monte Carlo sampling technique to obtain an adjusted brand favorability measure that is correlated with traditional survey-based measures used by brands. For analysis, we collect and examine Facebook data for more than 3,300 brands and about 205 million unique users that interact with those brands via their Facebook brand pages. Our data set is large and contains 6.68 billion likes and full text for 1.01 billion user comments, creating challenges for any modeling efforts. We evaluate the effectiveness of our model via out-of-sample prediction, external ground truth testing, and simulation. All demonstrate that our model performs very well, providing brand managers with a new method to more accurately measure consumer opinions toward the brand using social media data. Collapse

Topics: social media Facebook financial performance marketing management Google+

Methods: computational algorithm simulation survey machine learning sentiment analysis

Algorithms and Applications to Weighted Rank-one Binary Matrix Factorization

2020 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Lu, Haibing; Chen, Xi; Shi, Junmin; Vaidya, Jaideep; Atluri, Vijayalakshmi; Hong, Yuan; Huang, Wei

Abstract: Many applications use data that are better represented in the binary matrix form ... Expand

Abstract: Many applications use data that are better represented in the binary matrix form, such as click-stream data, market basket data, document-term data, user-permission data in access control, and others. Matrix factorization methods have been widely used tools for the analysis of high-dimensional data, as they automatically extract sparse and meaningful features from data vectors. However, existing matrix factorization methods do not work well for the binary data. One crucial limitation is interpretability, as many matrix factorization methods decompose an input matrix into matrices with fractional or even negative components, which are hard to interpret in many real settings. Some matrix factorization methods, like binary matrix factorization, do limit decomposed matrices to binary values. However, these models are not flexible to accommodate some data analysis tasks, like trading off summary size with quality and discriminating different types of approximation errors. To address those issues, this article presents weighted rank-one binary matrix factorization, which is to approximate a binary matrix by the product of two binary vectors, with parameters controlling different types of approximation errors. By systematically running weighted rank-one binary matrix factorization, one can effectively perform various binary data analysis tasks, like compression, clustering, and pattern discovery. Theoretical properties on weighted rank-one binary matrix factorization are investigated and its connection to problems in other research domains are examined. As weighted rank-one binary matrix factorization in general is NP-hard, efficient and effective algorithms are presented. Extensive studies on applications of weighted rank-one binary matrix factorization are also conducted. Collapse

Topics: database system role-based access control access control information retrieval

Methods: computational algorithm experiment cluster analysis machine learning k-means clustering

Developing insights from social media using semantic lexical chains to mine short text structures

2019 | Decision Support Systems | Citations: 0

Authors: Chua, Cecil Eng Huang; Storey, Veda C.; Li, Xiaolin; Kaul, Mala

Abstract: Social media is increasingly being used for communication by individuals and org ... Expand

Abstract: Social media is increasingly being used for communication by individuals and organizations. Social media stores vast amounts of publicly available data that provides a rich source of information and insights. Often, social media users can easily infer meaning from short text such as microblogs and Facebook posts because they understand the context and terminology used. Although automated data-mining can be effective for gaining insights from text data, a significant challenge is to accurately infer meaning from social media text derived from a single social media account. This is difficult because social media communication uses very short, or sparse, text, which yields a relatively small sample of usable words for analysis. Furthermore, interpreting the contextual meaning from a relatively small set of words is challenging. This research proposes a methodology for extracting semantic lexical chains from frequently occurring words in a single social media account and using these chains to mine short text structures to infer the overall themes of the user. The methodology is based on a proposed clustering algorithm and illustrated with examples from Facebook posts. The algorithm is tested and illustrated by comparing it to existing work and further applying it to a variety of news posts. This methodology could be useful for gaining decision-making insights from social media, or other online forms with short or sparse text. Collapse

Topics: Facebook social media knowledge representation decision support system

Methods: computational algorithm cluster analysis WordNet word sense disambiguation machine learning