arXiv subjects

Miu Kojima

Publications and source records attributed to Miu Kojima.

4 recordsLinked to original sources

arXiv2026

Access Over Deception: Fighting Deceptive Patterns through Accessibility

Deceptive patterns, dark patterns, and manipulative user interfaces (UI) are a widely used design strategy that manipulates users to act against their own interests in pursuit of shareholder aims. These patterns may particularly affect people with less education, visual impairments, and older adults. Yet, access is a critical feature of the user experience (UX), development standards, and law. We considered whether and how the Web Content Accessibility Guidelines (WCAG) and related legislation, like the European Accessibility Act (EAA), could act as a tool against deceptive patterns. We used heuristic evaluation to analyze whether and how deceptive patterns violate or conform to these guidelines and legal statutes. Although statistical analysis revealed no significant differences by pattern type, we identified three patterns implicated by the WCAG guidelines: Countdown Timer, Auto-Play, and Hidden Information. We offer this approach as one tool in the fight against UI-based deception and in support of inclusive design.

cs.HC↗

arXiv2024

AWARE Narrator and the Utilization of Large Language Models to Extract Behavioral Insights from Smartphone Sensing Data

Smartphones, equipped with an array of sensors, have become valuable tools for personal sensing. Particularly in digital health, smartphones facilitate the tracking of health-related behaviors and contexts, contributing significantly to digital phenotyping, a process where data from digital interactions is analyzed to infer behaviors and assess mental health. Traditional methods process raw sensor data into information features for statistical and machine learning analyses. In this paper, we introduce a novel approach that systematically converts smartphone-collected data into structured, chronological narratives. The AWARE Narrator translates quantitative smartphone sensing data into English language descriptions, forming comprehensive narratives of an individual's activities. We apply the framework to the data collected from university students over a week, demonstrating the potential of utilizing the narratives to summarize individual behavior, and analyzing psychological states by leveraging large language models.

cs.HC↗

arXiv2024

Deceptive, Disruptive, No Big Deal: Japanese People React to Simulated Dark Commercial Patterns

Dark patterns and deceptive designs (DPs) are user interface elements that trick people into taking actions that benefit the purveyor. Such designs are widely deployed, with special varieties found in certain nations like Japan that can be traced to global power hierarchies and the local socio-linguistic context of use. In this breaking work, we report on the first user study involving Japanese people (n=30) experiencing a mock shopping website injected with simulated DPs. We found that Alphabet Soup and Misleading Reference Pricing were the most deceptive and least noticeable. Social Proofs, Sneaking in Items, and Untranslation were the least deceptive but Untranslation prevented most from cancelling their account. Mood significantly worsened after experiencing the website. We contribute the first empirical findings on a Japanese consumer base alongside a scalable approach to evaluating user attitudes, perceptions, and behaviours towards DPs in an interactive context. We urge for more human participant research and ideally collaborations with industry to assess real designs in the wild.

cs.HC↗

arXiv2023

Kawaii Game Vocalics: A Preliminary Model

Kawaii is the Japanese concept of cute++, a global export with local characteristics. Recent work has explored kawaii as a feature of user experience (UX) with social robots, virtual characters, and voice assistants, i.e., kawaii vocalics. Games have a long history of incorporating characters that use voice as a means of expressing kawaii. Nevertheless, no work to date has evaluated kawaii game voices or mapped out a model of kawaii game vocalics. In this work, we explored whether and how a model of kawaii vocalics maps onto game character voices. We conducted an online perceptions study (N=157) using 18 voices from kawaii characters in Japanese games. We replicated the results for computer voice and discovered nuanced relationships between gender and age, especially youthfulness, agelessness, gender ambiguity, and gender neutrality. We provide our initial model and advocate for future work on character visuals and within play contexts.

cs.HC↗