Digital experiments are routinely used to test the value of a treatment relative to a status quo control setting — for instance, a new search relevance algorithm for a website or a new results layout for a mobile app. As digital experiments have become increasingly pervasive in organizations and a wide variety of research areas, their growth has prompted a new set of challenges for experimentation platforms. One challenge is that experiments often focus on the average treatment effect (ATE) without explicitly considering differences across major sub-groups — heterogeneous treatment effect (HTE). This is especially problematic because ATEs have decreased in many organizations as the more obvious benefits have already been realized. However, questions abound regarding the pervasiveness of user HTEs and how best to detect them. We propose a framework for detecting and analyzing user HTEs in digital experiments. Our framework combines an array of user characteristics with double machine learning. Analysis of 27 real-world experiments spanning 1.76 billion sessions and simulated data demonstrates the effectiveness of our detection method relative to existing techniques. We also find that transaction, demographic, engagement, satisfaction, and lifecycle characteristics exhibit statistically significant HTEs in 10% to 20% of our real-world experiments, underscoring the importance of considering user heterogeneity when analyzing experiment results, otherwise personalized features and experiences cannot happen, thus reducing effectiveness. In terms of the number of experiments and user sessions, we are not aware of any study that has examined user HTEs at this scale. Our findings have important implications for information retrieval, user modeling, platforms, and digital experience contexts, in which online experiments are often used to evaluate the effectiveness of design artifacts.

Psychometric measures of ability, attitudes, perceptions, and beliefs are crucial for understanding user behavior in various contexts including health, security, e-commerce, and finance.Traditionally, psychometric dimensions have been measured and collected using survey-based methods.Inferring such constructs from user-generated text could allow timely, unobtrusive collection and analysis.In this work we construct a corpus for psychometric natural language processing (NLP) related to important dimensions such as trust, anxiety, numeracy, and literacy, in the health domain.We discuss our multi-step process to align user text with their survey-based response items and provide an overview of the resulting testbed, which encompasses surveybased psychometric measures and accompanying user-generated text from 8,502 respondents.Our testbed also encompasses selfreported demographic information, including race, sex, age, income, and education, allowing for measuring bias and benchmarking fairness of text classification methods.We report preliminary results on use of the text to predict/categorize users' survey response labels and on the fairness of these models.We also discuss the important implications of our work and resulting testbed for future NLP research on psychometrics and fairness.

Phishing is a significant, ongoing cybersecurity threat facing both individuals and organizations, and the consequences of falling victim to phishing can be dire, particularly in terms of financial loss. While much research has focused on understanding or predicting user susceptibility to phishing with the aim of preventing it, little of this research has focused on modeling the process of being phished holistically. Specifically, while the outcome of interacting with a phishing website may be thought of as binary (e.g., "Were you phished or not?"), the actual process typically involves a sequence of stages spanning from the choice to visit (or not visit) a link all the way to transacting with a website and giving away valuable personal or financial information. To better understand the variables that influence phishing website traversal, we conducted a controlled lab experiment with a large sample of 908 participants. In this experiment, each participant was given a task such as opening an online checking account and presented with a series of simulated search results that included both legitimate and phishing websites. By monitoring participants' interactions with these websites and collecting additional information via surveys, we evaluated which variables were the most likely to result in behavior that could lead to greater phishing exposure using a multi-model comparison approach. The results of our analyses shed light on the key variables that can lead to a greater propensity for being phished and may prove invaluable to researchers interested in designing new interventions.

Psychometric measures reflecting people’s knowledge, ability, attitudes, and personality traits are critical for many real-world applications, such as e-commerce, health care, and cybersecurity. However, traditional methods cannot collect and measure rich psychometric dimensions in a timely and unobtrusive manner. Consequently, despite their importance, psychometric dimensions have received limited attention from the natural language processing and information retrieval communities. In this article, we propose a deep learning architecture, PyNDA, to extract psychometric dimensions from user-generated texts. PyNDA contains a novel representation embedding, a demographic embedding, a structural equation model (SEM) encoder, and a multitask learning mechanism designed to work in unison to address the unique challenges associated with extracting rich, sophisticated, and user-centric psychometric dimensions. Our experiments on three real-world datasets encompassing 11 psychometric dimensions, including trust, anxiety, and literacy, show that PyNDA markedly outperforms traditional feature-based classifiers as well as the state-of-the-art deep learning architectures. Ablation analysis reveals that each component of PyNDA significantly contributes to its overall performance. Collectively, the results demonstrate the efficacy of the proposed architecture for facilitating rich psychometric analysis. Our results have important implications for user-centric information extraction and retrieval systems looking to measure and incorporate psychometric dimensions.

The number of active, online phishing websites continues to grow unabated in recent years. This has created an ever-increasing security risk for both individual and enterprise users in terms of identity theft, malware, financial loss, etc. Although resources exist for tracking, cataloguing, and blacklisting these types of sites (e.g., PhishTank.com), the ephemeral nature of phishing websites makes in-depth analysis exceptionally difficult. In order to better understand how these phishing sites exploit user and system weaknesses, we have crafted a platform named PhishMonger for capturing live phishing websites in real-time on an ever-present, rolling basis, which we outline in this paper. Moreover, we present details regarding our growing database of verified phishing websites, which currently encompasses over 88,754 sites, spanning 10,956,415 files and folders, utilizing 108GB of compressed storage. We offer recommendations on how this corpus can be leveraged by the cybersecurity and security informatics research communities to examine several important research problems.