Star architecture in online public discourse: exploring Reddit user-generated content on the Vessel, New York, through a text analytics approach

Abstract

Purpose

User-generated content was explored to understand the public discourse surrounding the Vessel, a star architecture in New York. Through text analytics, the study aims to uncover topics, sentiments and themes in public opinion regarding this controversial building from social media data.

Design/methodology/approach

This study utilized a big data and text analytics approach, employing topic modeling with the BERTopic technique, sentiment analysis with roBERTa and thematic analysis on 10,259 Reddit comments pertaining to the Vessel.

Findings

The comments were grouped into 20 topics and seven themes, shedding light on discussions regarding the Vessel’s philosophy of existence, critiques of the architect’s approach, evaluations of project success or failure and considerations of the project’s future. Negative sentiments dominate the discourse, reflecting widespread criticism and skepticism towards the project.

Research limitations/implications

The manual data collection method, due to API restrictions, precluded tracking evolving trends over time. Nevertheless, the study provides insights for architects, urban planners, policymakers and stakeholders involved in public space design and management, highlighting the importance of considering user feedback from social media platforms.

Originality/value

This study enriches our comprehension of how users perceive star architecture in the age of social media, focusing on hidden layers of discourse surrounding a controversial iconic building. By combining topic modeling and sentiment analysis, the study offers a novel approach to analyzing architectural public debates on social media platforms like Reddit.

Citation

Pourahmad Ghalejough, A., Abbasi Avval, S., Haghparast, F. and Gharehbaglou, M. (2024), "Star architecture in online public discourse: exploring Reddit user-generated content on the Vessel, New York, through a text analytics approach", Archnet-IJAR, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ARCH-03-2024-0095