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Your search keyword '"Dumas, Catherine"' showing total 3 results
Start Over Author "Dumas, Catherine" Topic artificial intelligence
3 results on '"Dumas, Catherine"'

1. Online Incivility and Contextual Factors: Data‐Driven Detection and Analysis.

Author

Dumas, Catherine, Ghosh, Souvick, Hong, Lingzi, Karami, Amir, and Vaidya, Priya

Subjects
*SOCIAL media, *INFORMATION sharing, *INFORMATION science, *INFORMATION technology, *INFORMATION policy, *ARTIFICIAL intelligence, *DIGITAL technology
Abstract

Uncivil behaviors like rude or hate speech have been a persistent problem on social media, which could lead to negative user experience or even affect the psychological well‐being of users. Automatic detection and moderation of such behaviors are critical to creating a supportive online community for effective user communication and positive user experience. In this tutorial, we propose methods to study online incivility, which includes data collection from a social media platform, i.e., Reddit, automatic detection of incivility with pretrained deep learning classifiers, and statistical and visual analytical methods to investigate the combination of community characteristics and users' interactive patterns that relate to the occurrences of incivility. Similar methods can be applied to understand other information misbehaviors online, such as misinformation, dissemination of rumors, and cyberstalking. This panel is sponsored by SIG SM. [ABSTRACT FROM AUTHOR]

Published
2023
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2. Evaluating the Effectiveness of VR Training for Crisis Communication Skills Development Among LIS Graduate Students.

Author

Dumas, Catherine, Williams, Rachel D., Ogden, Lydia P., Flanagan, Joanna, Porwol, Luke, and Tillinghast, Julia

Subjects
*VIRTUAL reality, *CRISIS communication, *GRADUATE students, *TECHNOLOGICAL innovations, *ARTIFICIAL intelligence, *INFORMATION science, *INFORMATION technology, *INFORMATION policy
Abstract

This research analyzes the results of a study that is part of a larger, interdisciplinary, and multi‐institutional project that examines the usability and effectiveness of virtual reality (VR) training for library and information science (LIS) graduate students and professionals in gaining skills for interacting effectively with patrons in crisis. This paper reports on key findings related to the effectiveness of VR training for teaching empathy, confidence, and de‐escalation skills for LIS graduate students. The findings illustrate that VR has the potential to impact LIS graduate education by reaching a wider audience that introduces training in low‐stakes, immersive environments and that does not pose harm to patrons in crisis. This study also contributes innovative approaches that support training in skills including empathy, confidence, and de‐escalation. [ABSTRACT FROM AUTHOR]

Published
2023
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3. Transforming e-participation: VR-dialogue – building and evaluating an AI-supported framework for next-gen VR-enabled e-participation research.

Author

Porwol, Lukasz, Garcia Pereira, Agustin, and Dumas, Catherine

Abstract

Purpose: The purpose of this study is to explore whether immersive virtual reality (VR) can complement e-participation and help alleviate some major obstacles that hinder effective communication and collaboration. Immersive virtual reality (VR) can complement e-participation and help alleviate some major obstacles hindering effective communication and collaboration. VR technologies boost discussion participants' sense of presence and immersion; however, studying emerging VR technologies for their applicability to e-participation is challenging because of the lack of affordable and accessible infrastructures. In this paper, the authors present a novel framework for analyzing serious social VR engagements in the context of e-participation. Design/methodology/approach: The authors propose a novel approach for artificial intelligence (AI)-supported, data-driven analysis of group engagements in immersive VR environments as an enabler for next-gen e-participation research. The authors propose a machine-learning-based VR interactions log analytics infrastructure to identify behavioral patterns. This paper includes features engineering to classify VR collaboration scenarios in four simulated e-participation engagements and a quantitative evaluation of the proposed approach performance. Findings: The authors link theoretical dimensions of e-participation online interactions with specific user-behavioral patterns in VR engagements. The AI-powered immersive VR analytics infrastructure demonstrated good performance in automatically classifying behavioral scenarios in simulated e-participation engagements and the authors showed novel insights into the importance of specific features to perform this classification. The authors argue that our framework can be extended with more features and can cover additional patterns to enable future e-participation immersive VR research. Research limitations/implications: This research emphasizes technical means of supporting future e-participation research with a focus on immersive VR technologies as an enabler. This is the very first use-case for using this AI and data-driven infrastructure for real-time analytics in e-participation, and the authors plan to conduct more comprehensive studies using the same infrastructure. Practical implications: The authors' platform is ready to be used by researchers around the world. The authors have already received interest from researchers in the USA (Harvard University) and Israel and run collaborative online sessions. Social implications: The authors enable easy cloud access and simultaneous research session hosting 24/7 anywhere in the world at a very limited cost to e-participation researchers. Originality/value: To the best of the authors' knowledge, this is the very first attempt at building a dedicated AI-driven VR analytics infrastructure to study online e-participation engagements. [ABSTRACT FROM AUTHOR]

Published
2023
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