This document discusses using social media data to analyze the multicultural diversity of cities. It analyzes data from Milan as a case study. The analysis uses 4 lenses: 1) which areas communicate with Milan based on phone call data, 2) identifying 3 "digital cities" within Milan based on Twitter language data, 3) determining popular attractions based on Foursquare check-in data with a focus on the impact of Expo, and 4) analyzing the most "cool" venues based on Foursquare check-in data and how hotels are impacted by events. The analysis found correlations between social media data and official census data regarding language exposure and identified Expo as a new major attraction during its run.

1. 1
Studying Multicultural Diversity of Cities
through Social Media Language Detection
Michela Arnaboldi, Marco Brambilla, Beatrice Cassottana, Paolo Ciuccarelli,
Davide Ripamonti, Simone Vantini, Riccardo Volonterio.

2. The digital reflection
of the city

3. The Urban Macroscope
Joël de Rosnay, The Macroscope, 1979

4. 4
1. City and the world
The first lens:
Which parts of the world do talk to Milan and which parts
does Milan talk to?
Mobile-phone calls from Milan and toward Milan.

5. 1. City and the world: the countries that talk to Milan (1)
5

6. 1. City and the world: the countries that talk to Milan (2)
6
EU + CH

7. 1. City and the world: the countries that talk with Milan (7)
7

8. 2. Cities into cities: a city made of different cities
8
The three digital cities in Milan, based on Twitter languages.
The three cities of Twitter are:
• A city that speaks in Italian with itself and with the rest of
Italy;
• An international city that speaks in English with the rest of
the world;
• A multi-ethnic city projected toward the new urban
communities and the communities of origin.

9. 9
2. Cities into cities
In red the most «Italian» NILs, in blue the most «global», in yellow the most «multiethnic»

10. 10
2. Cities into cities
In red the most «Italian» NILs, in blue the most «global», in yellow the most «multiethnic»
Among the NIL with a statistically
significant number of tweets in the
considered quarter, the coloured areas
(25% of total areas) represent the NIL
with the highest shares of messages.

11. 2. Cities into cities
Which languages other than Italian and English characterize Milan?
11

12. 2. Cities into cities
Spanish 25%
12
Heterogeneity in the distribution
languages different from Italian and
English (NIL characterized by a
high linguistic entropy).
Which languages other than Italian and English characterize Milan?

13. 2. Cities into cities
Spanish 57%
Indonesian 36%
13
NIL characterized by an
average linguistic entropy.
Which languages other than Italian and English characterize Milan?

14. 2. Cities into cities
Arabic 89%
14
There is a predominant language
among the ones different from Italian
and English (NIL characterized by a
low linguistic entropy).
Which languages other than Italian and English characterize Milan?

15. Some numbers from Twitter
NIL % of predominant
language
Trenno 72% Arabic
Loreto, Umbria – Molise, Ortomercato 64% Arabic
Parco Forlanini - Ortica 63% Arabic
Quintosole 100% Spanish
Parco dei Navigli 69% Spanish
Gallaratese 66% Spanish
Cascina Triulza- Expo 57% Spanish
Ex Om – Morivione 56% Spanish
Mecenate 51% Spanish
Padova 59% Tagalog
Giambellino 57% Tagalog
Villapizzone 56% Tagalog
Bruzzano 59% Portuguese
Parco Nord 55% Dutch
Chiaravalle 75% Norwegian

16. Some official numbers from Census Data

17. Correlation: Twitter vs. official residents
• Tagalog,
Ukranian and
Romanian
overexposed
• Portuguese,
Dutch,
Norwegian,
Albanian
underexposed
• Arabic, Spanish
largely present
and slightly
overexposed

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3. City Magnets
The third lens:
Which are the main attractions of the city?
• the most popular places and the most “checked-in” locations
by the users of Foursquare (Swarm).

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3. City Magnets and EXPO
• The Map during Expo. Expo as a new attraction, which does
not delete the “traditional” ones

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4. Top Venues
The fourth lens:
Which are the most «cool» places of Milan?
• The analysis of the checkins according to the type of
«venue» allows to see where the people want to be «seen».

21. 21
4. Top Venues: The appearance Expo (1)
• The columns represent the places with the highest numbers
of checkins in April, May and June.

22. 22
4. Top Venues: The Hotels (2)
• The places where the people in the city want to be seen.
(the dynamic of the hotels between Expo and the Furniture Fair)

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4. Top Venues: The Hotel (5)
• The places where the people in the city want to be seen
(the dynamic of the hotels between Expo and the Furniture Fair)

24. Further work (lenses)
• Photo analysis
– Flickr vs. Instagram
• Frequent paths
– Mobile phone data vs. sensors
• Events coverage

25. 25
Questions?
Contact: marco.brambilla@polimi.it
@marcobrambi