in income, wealth, education, social well-being, and access to services. Social media sharing adds new inequalities. In some parts of the city people share many images that show their experiences and places they visit. In other areas, they share much less. In this way, some parts of a city become “social media rich” while others remain “social media poor.”
Social media allows us to share with others our urban experiences and self-representations. These collective posts create an “image of a city” for its residents and the outside world. In this image, some areas are represented well, while others may not be visible at all. This directly affects city economy and its social life.
We introduce the new concept of “social media inequality.” This concept allows us to quantitatively compare pattern in social media activities between parts of a city, a number of cities, or any other spatial areas. To test our ideas, we use a dataset of 7,442,454 public geo-coded Instagram images shared in Manhattan during five months in 2014 (March-July). This website presents our core findings; for details about our methods, additional findings, and discussion see
Publications
“Social media inequality in Manhattan is larger than inequalities in levels of income, rent, and unemployment.”
Number of accounts
Shared images
All assigned tags
% of images with hashtags
Number of accounts
Shared images
All assigned tags
% of images with hashtags
Note that according to U.S. Census Bureau (2015), there are 1,636,268 people residing in Manhattan. In the same year, 56.4 million people visited New York City (source: NYC statistics, 2015).
Economic inequality indicates how some economic characteristics or material resources, such as income, wealth or consumption are distributed in a city, country or between countries. Accordingly, we can define social media inequality as the measures of distribution of characteristics of social media content shared in a particular geographic area or between areas. The examples of such characteristics is the number of photos shared by all users of a social network such as Instagram in a given city area, the content of these photos, the numbers of tags, and content of these tags.
To measure social media inequality, we use standard measure of inequality adopted in many fields: Gini index. For example, it is used in economics to measure income or wealth inequalities. The index can vary between 0 (complete equality) and 1 (absolute inequality). What are Gini indexes for numbers of images shared on Instagram in Manhattan?
Locals
Visitors
These numbers indicate a very large difference between areas with lots of images and areas with few images. The ratio between sq. km areas with most (552,787) and least images (2,157) is 256,275. (To calculate income inequality for Manhattan, we use Census tracts data. To calculate social media inequality for Manhattan, we count numbers of images shared in each tract and normalize them by tract size.)
To put these measures of social media inequality in perspective, we can compare them with measures for income inequality in a number of countries and Manhattan.
Finland (one of the lowest in income inequality)
Spain
USA
Mexico
Manhattan
Seychelles (one of the highest in income inequality)
Rank
Location
Tract
Rank
Location
Tract
Rank
Location
Tract
Rank
Location
Tract
For locals, we see a clear separation between work areas (Financial District),shopping areas (SoHo) and nightlife areas and nightlife areas (East Village, Lower East Side, Meatpacking District). For visitors, these patterns are similar but even more clearly pronounced (for example, number of images in East Village keeps increasing after 9 pm). One exception is Midtown: here visitors’ activity keeps increasing late in the evening. (This area biggest concentration of hotels in the city and also key attractions such as Times Square.)
The patterns in the neighborhoods located above 59th street are different. Overall, the number of shared photos keep increasing and peaks latter in the evening. and in the areas with lower average income (East Harlem, Morningside Heights, and Washington Heights), this number keeps increasing until midnight. (Read our paper for an explanation we offer for this pattern.)
For visitors, overall patterns are similar, but there are also interesting differences. Numbers of images shared in Financial area and Soho peak and then go down quickly. Looking at main nightlife areas, we see that image sharing in East Village and Lower East Side continues late in the evening, when locals are already sharing less. (Because the number of images shared by visitors in the neighborhoods above 110 str. is relatively low, these patterns are not as reliable,so this graph is not included here).
Computer Science, The Graduate Center, City University of New York (CUNY).
Economics, The Graduate Center, City University of New York (CUNY)
Website design
This study uses 7,442,454 public geo-coded Instagram images shared in Manhattan during five months (March-July) in 2014. This dataset was originally created for On Broadway project commissioned by New York Public Library. Jay Chow downloaded all Instagram data and images. Mehrdad Yazdani separated locals and visitors, analyzed hashtags, and separated Instagram data according to Census boundaries working together with Ran Goldblatt.
On Broadway used only a part of the Manhattan dataset: 661,809 Instagram images shared along Broadway. However, It allowed us to develop the conceptual framework for Social Media Inequality paper and website: analysis of social media patterns across small city areas, and comparison with Census indicators for these areas. The original idea to compare images shared along Broadway comes from Daniel Goddemeyer. Daniel and Moritz Stefaner designed a number of visual interfaces for interacting with these images and other data sources. The case study in our paper builds on this work by expanding analysis to all of Manhattan.
On Broadway project:
The Graduate Center, City University of New York
California Institute for Telecommunications and Information at University of California, San Diego
The Andrew W. Mellon Foundation
Lev Manovich
manovich.lev@gmail.com