E-loyalty networks in online auctions

Creating a loyal customer base is one of the most important, and at the same time, most difficult tasks a company faces. Creating loyalty online (e-loyalty) is especially difficult since customers can ``switch'' to a competitor with the click of a mouse. In this paper we investigate e-loyalty in online auctions. Using a unique data set of over 30,000 auctions from one of the main consumer-to-consumer online auction houses, we propose a novel measure of e-loyalty via the associated network of transactions between bidders and sellers. Using a bipartite network of bidder and seller nodes, two nodes are linked when a bidder purchases from a seller and the number of repeat-purchases determines the strength of that link. We employ ideas from functional principal component analysis to derive, from this network, the loyalty distribution which measures the perceived loyalty of every individual seller, and associated loyalty scores which summarize this distribution in a parsimonious way. We then investigate the effect of loyalty on the outcome of an auction. In doing so, we are confronted with several statistical challenges in that standard statistical models lead to a misrepresentation of the data and a violation of the model assumptions. The reason is that loyalty networks result in an extreme clustering of the data, with few high-volume sellers accounting for most of the individual transactions. We investigate several remedies to the clustering problem and conclude that loyalty networks consist of very distinct segments that can best be understood individually.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS310 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A DATA ANALYTICAL FRAMEWORK FOR IMPROVING REAL-TIME, DECISION SUPPORT SYSTEMS IN HEALTHCARE

In this dissertation we develop a framework that combines data mining, statistics and operations research methods for improving real-time decision support systems in healthcare. Our approach consists of three main concepts: data gathering and preprocessing, modeling, and deployment. We introduce the notion of offline and semi-offline modeling to differentiate between models that are based on known baseline behavior and those based on a baseline with missing information. We apply and illustrate the framework in the context of two important healthcare contexts: biosurveillance and kidney allocation. In the biosurveillance context, we address the problem of early detection of disease outbreaks. We discuss integer programming-based univariate monitoring and statistical and operations research-based multivariate monitoring approaches. We assess method performance on authentic biosurveillance data. In the kidney allocation context, we present a two-phase model that combines an integer programming-based learning phase and a data-analytical based real-time phase. We examine and evaluate our method on the current Organ Procurement and Transplantation Network (OPTN) waiting list. In both contexts, we show that our framework produces significant improvements over existing methods

HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition

This paper introduces HeBERT and HebEMO. HeBERT is a Transformer-based model for modern Hebrew text, which relies on a BERT (Bidirectional Encoder Representations for Transformers) architecture. BERT has been shown to outperform alternative architectures in sentiment analysis, and is suggested to be particularly appropriate for MRLs. Analyzing multiple BERT specifications, we find that while model complexity correlates with high performance on language tasks that aim to understand terms in a sentence, a more-parsimonious model better captures the sentiment of entire sentence. Either way, out BERT-based language model outperforms all existing Hebrew alternatives on all common language tasks. HebEMO is a tool that uses HeBERT to detect polarity and extract emotions from Hebrew UGC. HebEMO is trained on a unique Covid-19-related UGC dataset that we collected and annotated for this study. Data collection and annotation followed an active learning procedure that aimed to maximize predictability. We show that HebEMO yields a high F1-score of 0.96 for polarity classification. Emotion detection reaches F1-scores of 0.78-0.97 for various target emotions, with the exception of surprise, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even among English-language models of emotion detection

The insider on the outside: a novel system for the detection of information leakers in social networks

Confidential information is all too easily leaked by naive users posting comments. In this paper we introduce DUIL, a system for Detecting Unintentional Information Leakers. The value of DUIL is in its ability to detect those responsible for information leakage that occurs through comments posted on news articles in a public environment, when those articles have withheld material non-public information. DUIL is comprised of several artefacts, each designed to analyse a different aspect of this challenge: the information, the user(s) who posted the information, and the user(s) who may be involved in the dissemination of information. We present a design science analysis of DUIL as an information system artefact comprised of social, information, and technology artefacts. We demonstrate the performance of DUIL on real data crawled from several Facebook news pages spanning two years of news articles

Israeli pediatricians’ confidence level in diagnosing and treating children with skin disorders: a cross-sectional questionnaire pilot study

BackgroundPediatricians daily see large numbers of patients with skin disorders. However, they encounter limited guidance as a result of a marked deficiency in pediatric dermatologists. Hence, reevaluation of training opportunities during pediatric residency has become essential. Our aim was to evaluate the confidence level of pediatric residents and specialists in diagnosing and treating skin disorders in children and to determine career and training-related characteristics that influence it.MethodsConducted as a cross-sectional study, we administered a questionnaire to 171 pediatricians across Israel. We assessed respondents’ self-efficacy about their ability to diagnose and treat skin disorders and collected data regarding their previous dermatology training and preferred training methods.Results77.8% of respondents reported below or average self-efficacy scores in diagnosing and managing children with skin disorders. Older age (>40 years old; OR = 5.51, p = 0.019), treating a higher number of patients with skin disorders (OR = 2.96, p = 0.032), and having any training in dermatology, either during medical school or residency (OR = 7.16, p = 0.031, OR = 11.14, p = 0.003 respectively), were all significant parameters involved in pediatricians reporting high self-efficacy in skin disorder management.ConclusionMost pediatric residents and pediatricians have average or below-average confidence in managing pediatric skin disorders. We suggest incorporating dermatology rotations during pediatric residency to improve young pediatricians’ self-efficacy in managing skin disorders and ultimately help pediatricians provide better care for patients presenting with dermatological conditions. These findings can ultimately help refine a pilot program in dermatology that might be implemented during pediatric residency

Wearable biosensors have the potential to monitor physiological changes associated with opioid overdose among people who use drugs: A proof-of-concept study in a real-world setting

INTRODUCTION: Wearable biosensors have the potential to monitor physiological change associated with opioid overdose among people who use drugs. METHODS: We enrolled 16 individuals who reported ≥ 4 daily opioid use events within the previous 30 day. Each was assigned a wearable biosensor that measured respiratory rate (RR) and actigraphy every 15 s for 5 days and also completed a daily interview assessing drug use. We describe the volume of RR data collected, how it varied by participant characteristics and drug use over time using repeated measures one-way ANOVA, episodes of acute respiratory depression (≤5 breaths/minute), and self-reported overdose experiences. RESULTS: We captured 1626.4 h of RR data, an average of 21.7 daily hours/participant over follow-up. Individuals with longer injection careers and those engaging in polydrug use captured significantly fewer total hours of respiratory data over follow-up compared to those with shorter injections careers (94.7 vs. 119.9 h, p = 0.04) and injecting fentanyl exclusively (98.7 vs. 119.5 h, p = 0.008), respectively. There were 385 drug use events reported over follow-up. There were no episodes of acute respiratory depression which corresponded with participant reports of overdose experiences. DISCUSSION: Our preliminary findings suggest that using a wearable biosensor to monitor physiological changes associated with opioid use was feasible. However, more sensitive biosensors that facilitate triangulation of multiple physiological data points and larger studies of longer duration are needed

TACKLING SIMPSON\u27S PARADOX IN BIG DATA USING CLASSIFICATION & REGRESSION TREES

This work is aimed at finding potential Simpson´s paradoxes in Big Data. Simpson´s paradox (SP) arises when choosing the level of data aggregation for causal inference. It describes the phenomenon where the direction of a cause on an effect is reversed when examining the aggregate vs. disaggregates of a sample or population. The practical decision making dilemma that SP raises is which level of data aggregation presents the right answer. \ \ We propose a tree-based approach for detecting SP in data. Classification and regression trees are popular predictive algorithms that capture relationships between an outcome and set of inputs. They are used for record-level predictions and for variable selection. We introduce a novel usage for a cause-and-effect scenario with potential confounding variables. A tree is used to capture the relationship between the effect and the set of cause and potential confounders. We show that the tree structure determines whether a paradox is possible. The resulting tree graphically displays potential confounders and the confounding direction, allowing researchers or decision makers identify potential SPs to be further investigated with a causal toolkit.. We illustrate our SP detection approach using real data for both a single confounder and for multiple confounder in a large dataset on Kidney transplant waiting time

Location, location, location: Close ties among older continuing care retirement community residents.

This study examines two theoretical explanations for the existence of close ties among continuing care retirement community residents: the attractiveness theory, which suggests that residents who possess certain attributes are more likely to be perceived as appealing to others; and the homophily theory, which argues that individuals are more likely to have close ties with people who share similar attributes. As a variant of the homophily theory, we also examined whether sharing a physical location makes the existence of certain connections more likely. Data from four continuing care retirement communities were used. To test the attractiveness theory, correlations between the number of individuals who named a person as a significant contact (ego's in-degree) and ego attributes were examined. To test the homophily theory, the median value of existing ties was compared against all possible social ties as though they were randomly formed. Finally, to further test the role of the institutional culture against various motivations that drive social ties-attractiveness and homophily-we used link prediction models with random forests. In support of the homophily theory, beyond the institutional culture, the only consistent predictor of the existence of close ties among residents was sharing a wing in the retirement community (geographic proximity). Therefore, we discuss the role of the physical location in the lives of older adults