Y Ba: Researcher — Prolific Citations Library

Explore 2 peer-reviewed studies by Y Ba in Human-Computer Interaction (HCI) and Crowdsourcing (2025). Discover research powered by Prolific's participant panel.

This page lists 2 peer-reviewed papers authored or co-authored by Y Ba in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

Impact of Tone-Aware Explanations in Recommender Systems

Authors: A Okoso, K Otaki, S Koide, Y Baba

Year: 2025

Published in: ACM Transactions on Recommender Systems, 2025•dl.acm.org

Institution: Toyota Central R and D Labs, Toyota

Research Area: Human-Computer Interaction (HCI)

Discipline: Machine Learning, Artificial Intelligence

The study demonstrates that tailoring the tone of textual explanations in recommender systems to domains and user attributes, such as age and personality traits, can enhance users' perceptions and engagement.

Methods: Two online user studies: (1) 470 participants evaluated synthetic explanations with six tones across three domains (movies, hotels, and home products), (2) 103 participants engaged with a real-world dataset from the hotel domain using a personalized recommender system.

Key Findings: The perceived effects of different textual explanation tones on users, examined across domains (movies, hotels, home products) and user attributes (e.g., age, personality traits).

DOI: https://dl.acm.org/doi/10.1145/3718101

Citations: 13

Sample Size: 573
Data quality in crowdsourcing and spamming behavior detection

Authors: Y Ba, MV Mancenido, EK Chiou, R Pan

Year: 2025

Published in: Behavior Research Methods, 2025 - Springer

Institution: University of Delaware, National Taiwan University, University of British Columbia, Monash University

Research Area: Crowdsourcing, Data Quality, Spamming Behavior Detection, LLM Applications in Behavioral Research

Discipline: Computer Science, Artificial Intelligence, LLM

The paper introduces a systematic method to evaluate crowdsourced data quality and detect spam behaviors through variance decomposition, proposing a spammer index and credibility metrics to improve consistency and reliability in labeling tasks.

Methods: Variance decomposition, Markov chain models, and generalized random effects models were used to assess annotator consistency and credibility; metrics were applied to both simulated and real-world data from two crowdsourcing platforms.

Key Findings: Quality of crowdsourced data, spammer behaviors, annotators’ consistency, and credibility.

Citations: 2