Studying Multicultural Diversity of Cities and Neighborhoods through Social Media Language Detection...
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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.
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Transcript of Studying Multicultural Diversity of Cities and Neighborhoods through Social Media Language Detection...
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Studying Multicultural Diversity of Cities through Social Media Language Detection
Michela Arnaboldi, Marco Brambilla, Beatrice Cassottana, Paolo Ciuccarelli, Davide Ripamonti, Simone Vantini, Riccardo Volonterio.
The digital reflection of the
city
The Urban Macroscope
Joël de Rosnay, The Macroscope, 1979
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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.
1. City and the world: the countries that talk to Milan (1)
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1. City and the world: the countries that talk to Milan (2)
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EU + CH
1. City and the world: the countries that talk with Milan (7)
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2. Cities into cities: a city made of different cities
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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.
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2. Cities into citiesIn red the most «Italian» NILs, in blue the most «global», in yellow the most «multiethnic»
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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.
2. Cities into cities
Which languages other than Italian and English characterize Milan?
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2. Cities into cities
Spanish 25%
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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?
2. Cities into cities
Spanish 57%
Indonesian 36%
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NIL characterized by an average linguistic entropy.
Which languages other than Italian and English characterize Milan?
2. Cities into cities
Arabic 89%
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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?
Some numbers from Twitter
NIL % of predominant language
Trenno 72% ArabicLoreto, Umbria – Molise, Ortomercato
64% Arabic
Parco Forlanini - Ortica 63% ArabicQuintosole 100% SpanishParco dei Navigli 69% SpanishGallaratese 66% SpanishCascina Triulza- Expo 57% SpanishEx Om – Morivione 56% SpanishMecenate 51% SpanishPadova 59% TagalogGiambellino 57% TagalogVillapizzone 56% TagalogBruzzano 59% PortugueseParco Nord 55% DutchChiaravalle 75% Norwegian
Some official numbers from Census Data
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».
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4. Top Venues: The appearance Expo (1)
• The columns represent the places with the highest numbers of checkins in April, May and June.
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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)
Further work (lenses)
• Photo analysis– Flickr vs. Instagram
• Frequent paths – Mobile phone data vs. sensors
• Events coverage
