Measuring culture refers to understanding the behaviors and habits of people residing in different parts of the world. It is a complex task that comes with a lot of challenges. However, the surprising fact is that data mining methods can be utilized to measure culture from location-based social networks.
Traditional methods for understanding the cultural differences include evaluating the findings of the World Values Survey, a global research project that discover people’s values and its impact on social and political life. During the period 1981-2008, the survey conducted over 250,000 interviews in 87 societies.
Those days have gone and now everyone is relying on data generated from location-based social networks such as Foursquare for data capture. The method allows researchers to collect huge amounts of data in a short span of time. Estimates show that data collected during a week’s time is in the same order of magnitude of that generated by the World Values Survey in three decades time.
How Data Mining Helps in Identifying Cultural Differences
Evaluation for culture starts by analyzing the eating and drinking habits of a particular region. The food and drink preferences of individuals change according to the time of the day and geographical location. A large number of individual preferences from different parts of the world are compared to determine how closely they match or how they differ.
The data collected from Foursquare includes check-ins related to drinks, fast food, and ordinary restaurant food. This is again divided into sub categories according to dishes, drinks, type of restaurant from where it is served, and so on. Each check-in gives the time and geographical location, which makes the system very suitable for global level comparison.
The results of the study provide fascinating insights into human behavior. Similar drinking habits are shown by some countries (such as Brazil and France) and some cities of the same country. Evaluating the bulk data generated from Foursquare is complex and time consuming. The complexity can be simplified by assigning the work to a professional firm offering data mining services.
