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Data-Mining Location-Based Food and Drink Habits from Social Media to Identify Cultural Boundaries

4/2014

In April 2014 Thiago H Silva and colleagues, mainly from the Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil, reported results of data-mining food and drink habits from the location-based social media site, Foursquare.
Prior to the application of data-mining to the problem, the World Values Survey, a global network of social scientists studying values and their impact on social and political life, conducted over 250,000 interviews in 87 societies between 1981 and 2008. Between 2010 and 2014 the World Values survey conducted 80,000 interviews. However, that traditional approach was very time-consuming and and expensive. 

Thiago H Silva, Pedro O S Vaz de Melo, Jussara Almeida, Mirco Musolesi, Antonio Loureiro, "You are What you Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food & Drink Habits in Foursquare," http://arxiv.org/abs/1404.1009.
 
Abstract:
"Food and drink are two of the most basic needs of human beings. However, as society evolved, food and drink became also a strong cultural aspect, being able to describe strong differences among people. Traditional methods used to analyze cross-cultural differences are mainly based on surveys and, for this reason, they are very difficult to represent a significant statistical sample at a global scale. In this paper, we propose a new methodology to identify cultural boundaries and similarities across populations at different scales based on the analysis of Foursquare check-ins. This approach might be useful not only for economic purposes, but also to support existing and novel marketing and social applications. Our methodology consists of the following steps. First, we map food and drink related check-ins extracted from Foursquare into users' cultural preferences. Second, we identify particular individual preferences, such as the taste for a certain type of food or drink, e.g., pizza or sake, as well as temporal habits, such as the time and day of the week when an individual goes to a restaurant or a bar. Third, we show how to analyze this information to assess the cultural distance between two countries, cities or even areas of a city. Fourth, we apply a simple clustering technique, using this cultural distance measure, to draw cultural boundaries across countries, cities and regions."

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