New York City Street Cleanliness: Applying Text Mining Techniques to Social Media Information

The sidewalks outside New York Fashion Week are lined with makeshift plywood walls. They are designed to keep pedestrians out of construction zones, but they have become the backdrops of innumerable “street style” photographs, portraits taken on city streets of self-appointed fashion “influencers” and other stylish “regular” people. Photographers, working to build a reputation within the fashion industry, take photos of editors, bloggers, club kids, and models, looking to do the same thing. The makeshift walls have become a site for the staging and performance of urban style. This photo essay documents the production of style in urban space, a transient process made semi-permanent through photography.

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Social dynamics of Twitter usage in London, Paris, and New York City

First Monday ◽

10.5210/fm.v19i5.4820 ◽

2014 ◽

Cited By ~ 4

Author(s):

Muhammad Adnan ◽

Paul A. Longley ◽

Shariq M. Khan

Keyword(s):

New York ◽

Social Media ◽

New York City ◽

Ethnic Identity ◽

York City ◽

Social Dynamics ◽

Activity Patterns ◽

Twitter Users ◽

And Gender ◽

Use Of Social Media

The penetration and use of social media services differs from city to city. This paper investigates the social dynamics of Twitter social media usage in three ethnically diverse cities — London, Paris, and New York City. We present a spatial analysis of Tweeting activity in the three cities, broken down by ethnicity and gender. We model the ethnic identity of Twitter users using their paired forenames and surnames. The geo–tagged Tweets provide an insight into the geography of their activity patterns across the three cities. The gender of each Twitter user is identified through classification of forenames, suggesting that, irrespective of the ethnic identity, the majority of Twitter users are male. Taken together, the results present a window on the activity patterns of different ethnic groups.

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Spatio-Temporal Distribution of Negative Emotions in New York City After a Natural Disaster as Seen in Social Media

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15102275 ◽

2018 ◽

Vol 15 (10) ◽

pp. 2275 ◽

Cited By ~ 10

Author(s):

Oliver Gruebner ◽

Sarah Lowe ◽

Martin Sykora ◽

Ketan Shankardass ◽

SV Subramanian ◽

...

Keyword(s):

New York ◽

Social Media ◽

New York City ◽

Natural Disaster ◽

York City ◽

Negative Emotions ◽

Temporal Distribution ◽

Census Tracts ◽

Post Disaster ◽

Over Time

Disasters have substantial consequences for population mental health. We used Twitter to (1) extract negative emotions indicating discomfort in New York City (NYC) before, during, and after Superstorm Sandy in 2012. We further aimed to (2) identify whether pre- or peri-disaster discomfort were associated with peri- or post-disaster discomfort, respectively, and to (3) assess geographic variation in discomfort across NYC census tracts over time. Our sample consisted of 1,018,140 geo-located tweets that were analyzed with an advanced sentiment analysis called ”Extracting the Meaning Of Terse Information in a Visualization of Emotion” (EMOTIVE). We calculated discomfort rates for 2137 NYC census tracts, applied spatial regimes regression to find associations of discomfort, and used Moran’s I for spatial cluster detection across NYC boroughs over time. We found increased discomfort, that is, bundled negative emotions after the storm as compared to during the storm. Furthermore, pre- and peri-disaster discomfort was positively associated with post-disaster discomfort; however, this association was different across boroughs, with significant associations only in Manhattan, the Bronx, and Queens. In addition, rates were most prominently spatially clustered in Staten Island lasting pre- to post-disaster. This is the first study that determined significant associations of negative emotional responses found in social media posts over space and time in the context of a natural disaster, which may guide us in identifying those areas and populations mostly in need for care.

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Spatiotemporal Patterns of Human Mobility and Its Association with Land Use Types during COVID-19 in New York City

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10050344 ◽

2021 ◽

Vol 10 (5) ◽

pp. 344

Author(s):

Yuqin Jiang ◽

Xiao Huang ◽

Zhenlong Li

Keyword(s):

New York ◽

Social Media ◽

Land Use ◽

New York City ◽

York City ◽

Local Governments ◽

Human Mobility ◽

Federal State ◽

Mobility Patterns ◽

Land Use Types

The novel coronavirus disease (COVID-19) pandemic has impacted every facet of society. One of the non-pharmacological measures to contain the COVID-19 infection is social distancing. Federal, state, and local governments have placed multiple executive orders for human mobility reduction to slow down the spread of COVID-19. This paper uses geotagged tweets data to reveal the spatiotemporal human mobility patterns during this COVID-19 pandemic in New York City. With New York City open data, human mobility pattern changes were detected by different categories of land use, including residential, parks, transportation facilities, and workplaces. This study further compares human mobility patterns by land use types based on an open social media platform (Twitter) and the human mobility patterns revealed by Google Community Mobility Report cell phone location, indicating that in some applications, open-access social media data can generate similar results to private data. The results of this study can be further used for human mobility analysis and the battle against COVID-19.

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