arXiv subjects

William Rand

Publications and source records attributed to William Rand.

8 recordsLinked to original sources

arXiv2026

Predicting Bot Vulnerability from Posting Trajectories: Censored Functional Regression under Informative Sampling

In this manuscript, we propose a novel framework for Scalar-on Censored Informative-design Functional Regression or SoCIFR. This setting is increasingly common in modern longitudinal and digital data applications but remains underdeveloped in functional data literature. We first discuss estimation and prediction in SoCIFR and extend the methodology to accommodate a matched case-control design. The proposed methodology is further generalized to handle multiple functional predictors, allowing for both censored and uncensored trajectories, observed under informative or non-informative sampling designs. Through simulation studies, we assess the performance of the proposed methods under various data-generating scenarios. We apply the methods to the motivating application for predicting user susceptibility to automated ("bot") interactions at increasing future time horizons based on social media behavioral trajectories observed over fixed time windows.

stat.ME↗

arXiv2020

Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks

This paper explains the design of a social network analysis framework, developed under DARPA's SocialSim program, with novel architecture that models human emotional, cognitive and social factors. Our framework is both theory and data-driven, and utilizes domain expertise. Our simulation effort helps in understanding how information flows and evolves in social media platforms. We focused on modeling three information domains: cryptocurrencies, cyber threats, and software vulnerabilities for the three interrelated social environments: GitHub, Reddit, and Twitter. We participated in the SocialSim DARPA Challenge in December 2018, in which our models were subjected to extensive performance evaluation for accuracy, generalizability, explainability, and experimental power. This paper reports the main concepts and models, utilized in our social media modeling effort in developing a multi-resolution simulation at the user, community, population, and content levels.

cs.SI↗

arXiv2019

The Effects of Information Overload on Online Conversation Dynamics

The inhibiting effects of information overload on the behavior of online social media users, can affect the population-level characteristics of information dissemination through online conversations. We introduce a mechanistic, agent-based model of information overload and investigate the effects of information overload threshold and rate of information loss on observed online phenomena. We find that conversation volume and participation are lowest under high information overload thresholds and mid-range rates of information loss. Calibrating the model to user responsiveness data on Twitter, we replicate and explain several observed phenomena: 1) Responsiveness is sensitive to information overload threshold at high rates of information loss; 2) Information overload threshold and rate of information loss are Pareto-optimal and users may experience overload at inflows exceeding 30 notifications per hour; 3) Local abundance of small cascades of modest global popularity and local scarcity of larger cascades of high global popularity explains why overloaded users receive, but do not respond to large, highly popular cascades; 4) Users typically work with 7 notifications per hour; 5) Over-exposure to information can suppress the likelihood of response by overloading users, contrary to analogies to biologically-inspired viral spread. Reconceptualizing information spread with the mechanisms of information overload creates a richer representation of online conversation dynamics, enabling a deeper understanding of how (dis)information is transmitted over social media.

cs.SI↗

arXiv2019

Computational landscape of user behavior on social media

With the increasing abundance of 'digital footprints' left by human interactions in online environments, e.g., social media and app use, the ability to model complex human behavior has become increasingly possible. Many approaches have been proposed, however, most previous model frameworks are fairly restrictive. We introduce a new social modeling approach that enables the creation of models directly from data with minimal a priori restrictions on the model class. In particular, we infer the minimally complex, maximally predictive representation of an individual's behavior when viewed in isolation and as driven by a social input. We then apply this framework to a heterogeneous catalog of human behavior collected from fifteen thousand users on the microblogging platform Twitter. The models allow us to describe how a user processes their past behavior and their social inputs. Despite the diversity of observed user behavior, most models inferred fall into a small subclass of all possible finite-state processes. Thus, our work demonstrates that user behavior, while quite complex, belies simple underlying computational structures.

cs.SI↗

arXiv2017

Evidence of Herding and Stubbornness in Jury Deliberations

We explore how the mechanics of collective decision-making, especially of jury deliberation, can be inferred from macroscopic statistics. We first hypothesize that the dynamics of competing opinions can leave a "fingerprint" in the joint distribution of final votes and time to reach a decision. We probe this hypothesis by modeling jury datasets from different states collected in different years and identifying which of the models best explains opinion dynamics in juries. In our best-fit model, individual jurors have a "herding" tendency to adopt the majority opinion of the jury, but as the amount of time they have held their current opinion increases, so too does their resistance to changing their opinion (what we call "increasing stubbornness"). By contrast, other models without increasing stubbornness, or without herding, create poorer fits to data. Our findings suggest that both stubbornness and herding play an important role in collective decision-making.

physics.soc-ph↗

arXiv2016

The Myopia of Crowds: A Study of Collective Evaluation on Stack Exchange

Crowds can often make better decisions than individuals or small groups of experts by leveraging their ability to aggregate diverse information. Question answering sites, such as Stack Exchange, rely on the "wisdom of crowds" effect to identify the best answers to questions asked by users. We analyze data from 250 communities on the Stack Exchange network to pinpoint factors affecting which answers are chosen as the best answers. Our results suggest that, rather than evaluate all available answers to a question, users rely on simple cognitive heuristics to choose an answer to vote for or accept. These cognitive heuristics are linked to an answer's salience, such as the order in which it is listed and how much screen space it occupies. While askers appear to depend more on heuristics, compared to voting users, when choosing an answer to accept as the most helpful one, voters use acceptance itself as a heuristic: they are more likely to choose the answer after it is accepted than before that very same answer was accepted. These heuristics become more important in explaining and predicting behavior as the number of available answers increases. Our findings suggest that crowd judgments may become less reliable as the number of answers grow.

cs.HC↗

arXiv2014

Competing opinions and stubbornness: connecting models to data

We introduce a general contagion-like model for competing opinions that includes dynamic resistance to alternative opinions. We show that this model can describe candidate vote distributions, spatial vote correlations, and a slow approach to opinion consensus with sensible parameter values. These empirical properties of large group dynamics, previously understood using distinct models, may be different aspects of human behavior that can be captured by a more unified model, such as the one introduced in this paper.

physics.soc-ph↗

arXiv2013

Understanding the Predictive Power of Computational Mechanics and Echo State Networks in Social Media

There is a large amount of interest in understanding users of social media in order to predict their behavior in this space. Despite this interest, user predictability in social media is not well-understood. To examine this question, we consider a network of fifteen thousand users on Twitter over a seven week period. We apply two contrasting modeling paradigms: computational mechanics and echo state networks. Both methods attempt to model the behavior of users on the basis of their past behavior. We demonstrate that the behavior of users on Twitter can be well-modeled as processes with self-feedback. We find that the two modeling approaches perform very similarly for most users, but that they differ in performance on a small subset of the users. By exploring the properties of these performance-differentiated users, we highlight the challenges faced in applying predictive models to dynamic social data.

cs.SI↗