arXiv subjects

Kevin Dunnell

Publications and source records attributed to Kevin Dunnell.

5 recordsLinked to original sources

arXiv2026

The CAST-framework: Measure and model social media use as a multi-level phenomenon through real-world applications

Designing social media experiences that support well-being requires understanding when, how, and for whom use matters. Screen-time totals omit content and context, and connecting these with behavior and experience requires coordinating measurements across timescales. We introduce the CAST framework to connect measurement choices with person-specific models of exposure, behavior, physiology, and experience. Its dimensions specify where observations occur, how they are obtained, what they measure, and at what temporal resolution. Responses to interventions, such as whether to proceed after an app-opening pause, enter as behavioral measurements. We propose four synchronized measurement modules linking mobile and wearable data with self-reports and intervention responses. A synthetic demonstration with 120 simulated participants over 28 days illustrates how daily aggregation can obscure opposing effects of different activities under specified generating assumptions. The framework guides selection of measures and outcomes for evaluating social media interfaces and interventions.

cs.HC↗

arXiv2026

Interactive visualizations for adolescents to understand and challenge algorithmic profiling in online platforms

Social media platforms regularly track, aggregate, and monetize adolescents' data, yet provide them with little visibility or agency over how algorithms construct their digital identities and make inferences about them. We introduce Algorithmic Mirror, an interactive visualization tool that transforms opaque profiling practices into explorable landscapes of personal data. It uniquely leverages adolescents' real digital footprints across YouTube, TikTok, and Netflix, to provide situated, personalized insights into datafication over time. In our study with 27 participants (ages 12--16), we show how engaging with their own data enabled adolescents to uncover the scale and persistence of data collection, recognize cross-platform profiling, and critically reflect algorithmic categorizations of their interests. These findings highlight how identity is a powerful motivator for adolescents' desire for greater digital agency, underscoring the need for platforms and policymakers to move toward structural reforms that guarantee children better transparency and the agency to influence their online experiences.

cs.HC↗

arXiv2025

Algorithmic Mirror: Designing an Interactive Tool to Promote Self-Reflection for YouTube Recommendations

Big Data analytics and Artificial Intelligence systems derive non-intuitive and often unverifiable inferences about individuals' behaviors, preferences, and private lives. Drawing on diverse, feature-rich datasets of unpredictable value, these systems erode the intuitive connection between our actions and how we are perceived, diminishing control over our digital identities. While Explainable Artificial Intelligence scholars have attempted to explain the inner workings of algorithms, their visualizations frequently overwhelm end-users with complexity. This research introduces 'hypothetical inference', a novel approach that uses language models to simulate how algorithms might interpret users' digital footprints and infer personal characteristics without requiring access to proprietary platform algorithms. Through empirical studies with fourteen adult participants, we identified three key design opportunities to foster critical algorithmic literacy: (1) reassembling scattered digital footprints into a unified map, (2) simulating algorithmic inference through LLM-generated interpretations, and (3) incorporating temporal dimensions to visualize evolving patterns. This research lays the groundwork for tools that can help users recognize the influence of data on platforms and develop greater autonomy in increasingly algorithm-mediated digital environments.

cs.HC↗

arXiv2024

Form Forge: Latent Space Exploration of Architectural Forms via Explicit Latent Variable Manipulation

This paper presents 'Form Forge,' a prototype of a creative system for interactively exploring the latent space of architectural forms, inspired by Franois Blanciak's SITELESS: 1001 Building Forms via direct manipulation of latent variables. Utilizing a fine-tuned StyleGAN2-ADA model, the system allows users to navigate an array of possible building forms derived from Blanciak's sketches. Distinct from common latent space exploration tools that often rely on projected navigation landmarks, Form Forge provides direct access to manipulate each latent variable, aiming to offer a more granular exploration of the model's capabilities. Form Forge's design is intended to simplify the interaction with a complex, high-dimensional space and to serve as a preliminary investigation into how such tools might support creative processes in architectural design.

cs.HC↗

arXiv2023

Latent Lab: Large Language Models for Knowledge Exploration

This paper investigates the potential of AI models, particularly large language models (LLMs), to support knowledge exploration and augment human creativity during ideation. We present "Latent Lab" an interactive tool for discovering connections among MIT Media Lab research projects, emphasizing "exploration" over search. The work offers insights into collaborative AI systems by addressing the challenges of organizing, searching, and synthesizing content. In a user study, the tool's success was evaluated based on its ability to introduce users to an unfamiliar knowledge base, ultimately setting the groundwork for the ongoing advancement of human-AI knowledge exploration systems.

cs.AI↗