You are What you Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food and Drink Habits in Foursquare. Silva, H, T., de&nbsp;Melo, Vaz, P.&nbsp;O.<nbsp>S., Almeida, J., Musolesi, M., & Loureiro, A. 2014. cite arxiv:1404.1009Comment: 10 pages, 10 figures, 1 table. Proceedings of 8th AAAI Intl. Conf. on Weblogs and Social Media (ICWSM 2014)
You are What you Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food and Drink Habits in Foursquare [link]Paper  abstract   bibtex   
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.
@misc{ silva2014drink,
  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.},
  added-at = {2014-04-07T12:00:38.000+0200},
  author = {Silva, Thiago H and de Melo, Pedro O S Vaz and Almeida, Jussara and Musolesi, Mirco and Loureiro, Antonio},
  biburl = {http://www.bibsonomy.org/bibtex/27e637e54bd03290819653444828b4035/plaufer},
  interhash = {8f252e6236b777fe4c2077a3158b44e9},
  intrahash = {7e637e54bd03290819653444828b4035},
  keywords = {culture food foursquare},
  note = {cite arxiv:1404.1009Comment: 10 pages, 10 figures, 1 table. Proceedings of 8th AAAI Intl. Conf. on  Weblogs and Social Media (ICWSM 2014)},
  title = {You are What you Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food and Drink Habits in Foursquare},
  url = {http://arxiv.org/abs/1404.1009},
  year = {2014}
}

Downloads: 0