Identifying urban crowds using geo-located Social media data: a Twitter experiment in New York City

AbstractA key challenge in mining social media data streams is to identify events which are actively discussed by a group of people in a specific local or global area. Such events are useful for early warning for accident, protest, election or breaking news. However, neither the list of events nor the resolution of both event time and space is fixed or known beforehand. In this work, we propose an online spatio-temporal event detection system using social media that is able to detect events at different time and space resolutions. First, to address the challenge related to the unknown spatial resolution of events, a quad-tree method is exploited in order to split the geographical space into multiscale regions based on the density of social media data. Then, a statistical unsupervised approach is performed that involves Poisson distribution and a smoothing method for highlighting regions with unexpected density of social posts. Further, event duration is precisely estimated by merging events happening in the same region at consecutive time intervals. A post processing stage is introduced to filter out events that are spam, fake or wrong. Finally, we incorporate simple semantics by using social media entities to assess the integrity, and accuracy of detected events. The proposed method is evaluated using different social media datasets: Twitter and Flickr for different cities: Melbourne, London, Paris and New York. To verify the effectiveness of the proposed method, we compare our results with two baseline algorithms based on fixed split of geographical space and clustering method. For performance evaluation, we manually compute recall and precision. We also propose a new quality measure named strength index, which automatically measures how accurate the reported event is.

Download Full-text

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

SSRN Electronic Journal ◽

10.2139/ssrn.3706506 ◽

2020 ◽

Author(s):

Huijue Kelly Duan

Keyword(s):

New York ◽

Social Media ◽

New York City ◽

Text Mining ◽

York City ◽

City Street

Download Full-text

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.

Download Full-text

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.

Download Full-text

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.

Download Full-text

Notes from the Field: Use of Social Media as a Communication Tool During a Mumps Outbreak — New York City, 2015

MMWR Morbidity and Mortality Weekly Report ◽

10.15585/mmwr.mm6602a5 ◽

2017 ◽

Vol 66 (02) ◽

pp. 60-61 ◽

Cited By ~ 2

Author(s):

Beth M. Isaac ◽

Jane R. Zucker ◽

Jennifer MacGregor ◽

Mekete Asfaw ◽

Jennifer L. Rakeman ◽

...

Keyword(s):

New York ◽

Social Media ◽

New York City ◽

York City ◽

Communication Tool ◽

Use Of Social Media

Download Full-text

Fashion informatics of the Big 4 Fashion Weeks using topic modeling and sentiment analysis

Fashion and Textiles ◽

10.1186/s40691-021-00265-6 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Yeong-Hyeon Choi ◽

Seungjoo Yoon ◽

Bin Xuan ◽

Sang-Yong Tom Lee ◽

Kyu-Hye Lee

Keyword(s):

New York ◽

Social Media ◽

Network Analysis ◽

Sentiment Analysis ◽

Topic Modeling ◽

Semantic Network ◽

Semantic Network Analysis ◽

Social Media Data ◽

Big 4 ◽

Media Data

AbstractThis study used several informatics techniques to analyze consumer-driven social media data from four cities (Paris, Milan, New York, and London) during the 2019 Fall/Winter (F/W) Fashion Week. Analyzing keywords using a semantic network analysis method revealed the main characteristics of the collections, celebrities, influencers, fashion items, fashion brands, and designers connected with the four fashion weeks. Using topic modeling and a sentiment analysis, this study confirmed that brands that embodied similar themes in terms of topics and had positive sentimental reactions were also most frequently mentioned by the consumers. A semantic network analysis of the tweets showed that social media, influencers, fashion brands, designers, and words related to sustainability and ethics were mentioned in all four cities. In our topic modeling, the classification of the keywords into three topics based on the brand collection’s themes provided the most accurate model. To identify the sentimental evaluation of brands participating in the 2019 F/W Fashion Week, we analyzed the consumers’ sentiments through positive, neutral, and negative reactions. This quantitative analysis of consumer-generated social media data through this study provides insight into useful information enabling fashion brands to improve their marketing strategies.

Download Full-text