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A platform for research: civil engineering, architecture and urbanism
Tweeting during the Pandemic in New York City: Unveiling the Evolving Sentiment Landscape of NYC through a Spatiotemporal Analysis of Geolocated Tweets
This article explores the relationship between spatial factors, socioeconomic conditions, and Twitter (now called X) sentiment in New York City (NYC) during the COVID-19 pandemic. Using Twitter data, the study investigates how sentiment varied across different geographies. It examines whether sentiment scores, unemployment rates, and COVID-19 hospitalization rates in NYC zip codes revealed spatial associations. The research employs sentiment analysis, a natural language processing technique used to algorithmically determine the emotional tone of a text, on a database of geo-located tweets spanning January to December 2020. The findings reveal a shift towards more negative sentiment during the initial year of the pandemic. Moreover, the study uncovers variations in sentiment trends across boroughs and zip codes. Additionally, a zip code-level fixed-effects model demonstrates a statistically significant relationship between sentiment scores and unemployment rates. In summary, this article makes a two-fold contribution: firstly, it adds a spatial lens to the scholarly debate regarding the use of Twitter data as an indicator of publicly expressed sentiment; secondly, it provides empirical evidence on the spatial interconnectedness of sentiment, health (hospitalization), and socioeconomic factors (unemployment). Overall, this research sheds light on the nuanced relationship between sentiment and space during the COVID-19 pandemic in NYC.
Tweeting during the Pandemic in New York City: Unveiling the Evolving Sentiment Landscape of NYC through a Spatiotemporal Analysis of Geolocated Tweets
This article explores the relationship between spatial factors, socioeconomic conditions, and Twitter (now called X) sentiment in New York City (NYC) during the COVID-19 pandemic. Using Twitter data, the study investigates how sentiment varied across different geographies. It examines whether sentiment scores, unemployment rates, and COVID-19 hospitalization rates in NYC zip codes revealed spatial associations. The research employs sentiment analysis, a natural language processing technique used to algorithmically determine the emotional tone of a text, on a database of geo-located tweets spanning January to December 2020. The findings reveal a shift towards more negative sentiment during the initial year of the pandemic. Moreover, the study uncovers variations in sentiment trends across boroughs and zip codes. Additionally, a zip code-level fixed-effects model demonstrates a statistically significant relationship between sentiment scores and unemployment rates. In summary, this article makes a two-fold contribution: firstly, it adds a spatial lens to the scholarly debate regarding the use of Twitter data as an indicator of publicly expressed sentiment; secondly, it provides empirical evidence on the spatial interconnectedness of sentiment, health (hospitalization), and socioeconomic factors (unemployment). Overall, this research sheds light on the nuanced relationship between sentiment and space during the COVID-19 pandemic in NYC.
Tweeting during the Pandemic in New York City: Unveiling the Evolving Sentiment Landscape of NYC through a Spatiotemporal Analysis of Geolocated Tweets
Ignaccolo, Carmelo (author) / Wibisono, Kevin (author) / Sutto, Maria Paola (author) / Plunz, Richard A. (author)
Journal of Urban Technology ; 31 ; 3-28
2024-05-26
26 pages
Article (Journal)
Electronic Resource
English
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