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Ilias Pappas

Publications and source records attributed to Ilias Pappas.

2 recordsLinked to original sources

Building Metaverse Responsibly: Findings from Interviews with Experts

The metaverse promises unprecedented immersive digital experiences but also raises critical privacy concerns as vast amounts of personal and behavioral data are collected. As immersive technologies blur the boundaries between physical and virtual realms, established privacy standards are being challenged. However, little is known about how the experts of these technologies such as requirement analysts, designers, developers, and architects perceive and address privacy issues in the creation of metaverse platforms. This research aims to fill that gap by investigating privacy considerations in metaverse development from the experts perspective. We conducted in depth, semi structured interviews with metaverse platform and application experts to explore their views on privacy challenges and practices. The findings offer new empirical insights by extending information systems privacy research into the metaverse context, highlighting the interplay between technological design, user behavior, and regulatory structures. Practically, this work provides guidance for developers, and policymakers on implementing privacy by design principles, educating and empowering users, and proactively addressing novel privacy threats in metaverses.

cs.CY

A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions

Artificial Intelligence (AI) has been used for processing data to make decisions, interact with humans, and understand their feelings and emotions. With the advent of the internet, people share and express their thoughts on day-to-day activities and global and local events through text messaging applications. Hence, it is essential for machines to understand emotions in opinions, feedback, and textual dialogues to provide emotionally aware responses to users in today's online world. The field of text-based emotion detection (TBED) is advancing to provide automated solutions to various applications, such as businesses, and finances, to name a few. TBED has gained a lot of attention in recent times. The paper presents a systematic literature review of the existing literature published between 2005 to 2021 in TBED. This review has meticulously examined 63 research papers from IEEE, Science Direct, Scopus, and Web of Science databases to address four primary research questions. It also reviews the different applications of TBED across various research domains and highlights its use. An overview of various emotion models, techniques, feature extraction methods, datasets, and research challenges with future directions has also been represented.

cs.CL