CQA services are collaborative platforms where users ask and answer questions. We investigate the influence of national culture on people's online questioning and answering behavior. For this, we analyzed a sample of 200 thousand users in Yahoo Answers from 67 countries. We measure empirically a set of cultural metrics defined in Geert Hofstede's cultural dimensions and Robert Levine's Pace of Life and show that behavioral cultural differences exist in community question answering platforms. We find that national cultures differ in Yahoo Answers along a number of dimensions such as temporal predictability of activities, contribution-related behavioral patterns, privacy concerns, and power inequality.
@cite_26 used food and drink check-ins in Foursquare to identify cultural boundaries and similarities across populations. They showed that online footprints of foods and drinks are good indicators of cultural similarities between users, e.g., lunch time is the perfect time for Brazilians to go for slow food places more often, whereas Americans and English people go for slow foods more at dinner time. Extracted features like these allow them to apply simple clustering algorithms such as K-means to draw cultural boundaries across the countries.
@cite_26
1481628590
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. FOOD AND CULTURE is the market-leading text for the cultural foods courses, providing information on the health, culture, food, and nutrition habits of the most common ethnic and racial groups living in the United States. It is designed to help health professionals, chefs, and others in the food service industry learn to work effectively with members of different ethnic and religious groups in a culturally sensitive manner. Authors Pamela Goyan Kittler and Kathryn P. Sucher include comprehensive coverage of key ethnic, religious, and regional groups, including Native Americans, Europeans, Africans, Mexicans and Central Americans, Caribbean Islanders, South Americans, Chinese, Japanese, Koreans, Southeast Asians, Pacific Islanders, People of the Balkans, Middle Easterners, Asian Indians, and regional Americans. Food occupies a central position in every culture and it is therefore of great interest to understand the evolution of food culture. The advent of the World Wide Web and online recipe repositories have begun to provide unprecedented opportunities for data-driven, quantitative study of food culture. Here we harness an online database documenting recipes from various Chinese regional cuisines and investigate the similarity of regional cuisines in terms of geography and climate. We find that geographical proximity, rather than climate proximity, is a crucial factor that determines the similarity of regional cuisines. We develop a model of regional cuisine evolution that provides helpful clues for understanding the evolution of cuisines and cultures. In this paper, we propose a novel effective framework to expand an existing image dataset automatically leveraging existing categories and crowdsourcing. Especially, in this paper, we focus on expansion on food image data set. The number of food categories is uncountable, since foods are different from a place to a place. If we have a Japanese food dataset, it does not help build a French food recognition system directly. That is why food data sets for different food cultures have been built independently so far. Then, in this paper, we propose to leverage existing knowledge on food of other cultures by a generic “foodness” classifier and domain adaptation. This can enable us not only to built other-cultured food datasets based on an original food image dataset automatically, but also to save as much crowd-sourcing costs as possible. In the experiments, we show the effectiveness of the proposed method over the baselines.