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_27 used about 1.5 million Doodle polls from 211 countries and territories and studied the influence of national culture on people's scheduling behavior. Using Hofstede's cultural dimensions, they found that Doodle poll participants from collectivist countries find more consensus than those from predominantly individualist societies.
@cite_27
2009603815
Event scheduling is a group decision-making process in which social dynamics influence people's choices and the overall outcome. As a result, scheduling is not simply a matter of finding a mutually agreeable time, but a process that is shaped by social norms and values, which can highly vary between countries. To investigate the influence of national culture on people's scheduling behavior we analyzed more than 1.5 million Doodle date time polls from 211 countries. We found strong correlations between characteristics of national culture and several behavioral phenomena, such as that poll participants from collectivist countries respond earlier, agree to fewer options but find more consensus than predominantly individualist societies. Our study provides empirical evidence of behavioral differences in group decision-making and time perception with implications for cross-cultural collaborative work. Relying on Gudykunst's cultural variability in communication (CVC) framework and culture-specific facial expressions of emotion, we examined how people's use of emoticons varies cross-culturally. By merging emoticon usage patterns on Twitter with Hofstede's national culture scores and national indicators across 78 countries, this study found that people within individualistic cultures favor horizontal and mouth-oriented emoticons like :), while those within collectivistic cultures favor vertical and eye-oriented emoticons like ^_^. Our study serves to demonstrate how recent big data-driven approaches can be used to test research hypotheses in cross-cultural communication effectively from the methodological triangulation perspective. Implications and limitations regarding the findings of this study are also discussed. Previous studies have established the link between one's actions (e.g., engaging with others vs. minding one's own business) and one's national culture (e.g., collectivist vs. individualistic), and such actions have been shown to be important as they are collectively affiliated with a country's economic outcomes (e.g., Gross Domestic Product). Hitherto there has not been any systematic study of whether one's action on Twitter (e.g., deciding when to post messages) is linked to one's culture (e.g., country's Pace of Life). To fix that, we build different network snapshots starting from 55,000 seed users on Twitter, and we do so for 10 weeks across 30 countries (after filtering those with low penetration rates) for a total of 2.34 M profiles. Based on Hofstede's theory of cultural dimensions and Levine's Pace of Life theory, we consider three behavioral patterns on Twitter (i.e., temporal predictability of tweets, engaging with others, and supporting others who are less popular) and associate them with three different dimensions derived from the two theories: Pace of Life, Individualism and Power Distance. We find the following strong correlations: activity predictability negatively correlates with Pace of Life (r=-0.62), tweets with mentions negatively correlates with Individualism (r = -0.55), and power (e.g, Twitter popularity) imbalance in relationships (between, for example, two users mentioning each other) is correlated with Power Distance (r=0.62). These three cultural dimensions matter because they are associated with a country's socio-economic aspects - with GDP per capita, income inequality, and education expenditure. 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. This paper presents a methodology to characterize information about groups of people with the main goal of detecting cultural aspects. Based on tracked pedestrians, groups are detected and characterized. Group information is then used to find out Cultural aspects in videos, based on the Hofstede cultural dimensions theory. The presented work was tested in videos of pedestrian groups recorded in different countries and results seem promising in order to identify cultural aspects in the filmed sequences. This article describes briefly the Hofstede model of six dimensions of national cultures: Power Distance, Uncertainty Avoidance, Individualism Collectivism, Masculinity Femininity, Long Short Term Orientation, and Indulgence Restraint. It shows the conceptual and research efforts that preceded it and led up to it, and once it had become a paradigm for comparing cultures, research efforts that followed and built on it. The article stresses that dimensions depend on the level of aggregation; it describes the six entirely different dimensions found in the (2010) research into organizational cultures. It warns against confusion with value differences at the individual level. It concludes with a look ahead in what the study of dimensions of national cultures and the position of countries on them may still bring. Creative Commons License This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. This article is available in Online Readings in Psychology and Culture: http: scholarworks.gvsu.edu orpc vol2 iss1 8 Computer security tools usually provide universal solutions without taking user characteristics (origin, income level, ...) into account. In this paper, we test the validity of using such universal security defenses, with a particular focus on culture. We apply the previously proposed Security Behavior Intentions Scale (SeBIS) to 3,500 participants from seven countries. We first translate the scale into seven languages while preserving its reliability and structure validity. We then build a regression model to study which factors affect participants' security behavior. We find that participants from different countries exhibit different behavior. For instance, participants from Asian countries, and especially Japan, tend to exhibit less secure behavior. Surprisingly to us, we also find that actual knowledge influences user behavior much less than user self-confidence in their computer security knowledge. Stated differently, what people think they know affects their security behavior more than what they do know. The use of information technology in the study of human behavior is a subject of great scientific interest. Cultural and personality aspects are factors that influence how people interact with one another in a crowd. This paper presents a methodology to detect cultural characteristics of crowds in video sequences. Based on filmed sequences, pedestrians are detected, tracked and characterized. Such information is then used to find out cultural differences in those videos, based on the Big-five personality model. Regarding cultural differences of each country, results indicate that this model generates coherent information when compared to data provided in literature. We present the results of an online survey of smartphone unlocking (N=8,286) that we conducted in eight different countries. The goal was to investigate differences in attitudes towards smartphone unlocking between different national cultures. Our results show that there are indeed significant differences across a range of categories. For instance, participants in Japan considered the data on their smartphones to be much more sensitive than those in other countries, and respondents in Germany were 4.5 times more likely than others to say that protecting data on their smartphones was important. The results of this study shed light on how motivations to use various security mechanisms are likely to differ from country to country. Cultural behavior is an area of research that can allow us to further cross-cultural understanding, and is now starting to integrate itself within the field of information technology. One domain that expresses these behaviors is inside a crowd, however the analysis of micro-level crowd behavior is impractical in a real-world setting as passive observation has limitations on understanding true behavior. By using a virtual environment to simulate a crowd situation, measuring an individual's in-crowd behavior becomes feasible. This paper introduces the development of a virtual environment which enables the creation of different types of cultural crowds with which the user may interact. The parameterization of the crowds is based on the famous cultural dimensions put forward by Hofstede. One of the cultural dimensions, individualism collectivism, was mapped to agent characteristics during a series of simulations and it was found that two distinct types of crowd could be generated. For the dimensions have not yet been examined, the proposed environment provides an ideal opportunity to address this gap in the research as well as becoming a tool with which other types of experimentation can be performed. 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. This research examined the relation between self-relevance and word-of-mouth (WOM). The results of two studies suggest consumers are more likely to provide WOM for products that are relevant to self-concept than for more utilitarian products. There was also some indication that WOM was biased, in the sense that consumers exaggerated the benefits of self-relevant products compared to utilitarian products. Finally, self-relevance had a greater impact on WOM in individualist cultures than collectivist cultures, consistent with differences in the way self-concept is typically construed by these groups. Implications for marketing strategies concerning WOM are discussed. Previous studies have found that in American culture high-arousal positive states (HAP) such as excitement are valued more and low-arousal positive states (LAP) such as calm are valued less than they are in Chinese culture. What specific factors account for these differences? The authors predicted that when people and cultures aimed to influence others (i.e., assert personal needs and change others’ behaviors to meet those needs), they would value HAP more and LAP less than when they aimed to adjust to others (i.e., suppress personal needs and change their own behaviors to meet others’ needs). They test these predictions in 1 survey and 3 experimental studies. The findings suggest that within and across American and Chinese contexts, differences in ideal affect are due to specific interpersonal goals. We build dialogue system policies for negotiation, and in particular for argumentation. These dialogue policies are designed for negotiation against users of different cultural norms (individualists, collectivists, and altruists). In order to learn these policies we build simulated users (SUs), i.e. models that simulate the behavior of real users, and use Reinforcement Learning (RL). The SUs are trained on a spoken dialogue corpus in a negotiation domain, and then tweaked towards a particular cultural norm using hand-crafted rules. We evaluate the learned policies in a simulation setting. Our results are consistent with our SUs, in other words, the policies learn what they are designed to learn, which shows that RL is a promising technique for learning policies in domains, such as argumentation, that are more complex than standard slot-filling applications. Index Terms: spoken dialogue systems, reinforcement learning, simulated users, argumentation, negotiation, culture. We argue that traditional cultural factors models (from Hofstede, Nisbett, etc.) are too abstract to provide good predictions of important human performance behaviors such as directive compliance. Instead, we focus on culture-specific social interaction behaviors (i.e., "etiquette") as a bridge between abstract cultural factors and human performance. We describe a computational model of etiquette and politeness perception, called CECAEDA (Computational Effects of Cultural Attributes and Etiquette on Directive Adherence). CECAEDA consists of four parts: (1) a culturally universal model of politeness perceptions, their causes and effects, (2) a culturally universal cognitive model compliance decision making and behaviors, (3) a set of hypotheses about how politeness perceptions alter directive compliance, and (4) a set of hypotheses about how cultural factors (specifically, those proposed by Hofstede [1]) affect etiquette perceptions and, thus, directive compliance in culture-specific ways. Each component is discussed in detail, followed by a brief presentation of our research test bed and paradigm for evaluating CECAEDA. Online social networking tools are used around the world by people to ask questions of their friends, because friends provide direct, reliable, contextualized, and interactive responses. However, although the tools used in different cultures for question asking are often very similar, the way they are used can be very different, reflecting unique inherent cultural characteristics. We present the results of a survey designed to elicit cultural differences in people’s social question asking behaviors across the United States, the United Kingdom, China, and India. The survey received responses from 933 people distributed across the four countries who held similar job roles and were employed by a single organization. Responses included information about the questions they ask via social networking tools, and their motivations for asking and answering questions online. The results reveal culture as a consistently significant factor in predicting people’s social question and answer behavior. The prominent cultural differences we observe might be traced to people’s inherent cultural characteristics (e.g., their cognitive patterns and social orientation), and should be comprehensively considered in designing social search systems. This paper presents a methodology to detect personality and basic emotion characteristics of crowds in video sequences. Firstly, individuals are detected and tracked, and then groups are recognized and characterized. Such information is then mapped to OCEAN dimensions, used to find out personality and emotion in videos, based on OCC emotion models. Although it is a clear challenge to validate our results with real life experiments, we evaluate our method with the available literature information regarding OCEAN values of different Countries and also emergent Personal distance among people. Hence, such analysis refer to cultural differences of each country too. Our results indicate that this model generates coherent information when compared to data provided in available literature, as shown in qualitative and quantitative results. This paper presents a detailed analysis of the Google+ social network. We identify the key differences and similarities with other popular networks like Facebook and Twitter, in order to determine whether Google+ is a new paradigm or yet another social network. This work is based on large-scale crawls of over 27 million user profiles that represented nearly 50 of the entire network in 2011. We observe that the average path length between users is slightly higher than other networks, possibly because Google+ is a new system where relationships are still rapidly growing. Google+ shows a higher level of reciprocity than Twitter, which also has directed social links. The newly available "places lived" field could be used to study how users are distributed around the world and how aggressively the service has been adopted in different countries. We find that Google+ is popular in countries with relatively low Internet penetration rate. Based on the amount and types of information publicly shared in user profiles, we also find that the notion of privacy varies significantly across different cultures. Communication between humans and robots is a very critical step for the integration of social robots into society. Emotion expression through a robotic face is one of the key points of communication. Despite the most recent efforts, no matter how much expression capabilities improve, facial expression recognition is often hampered by a cultural divide between subjects that participate in surveys. The purpose of this work is to take advantage of the 24 degrees of freedom head of the humanoid social robot KOBIAN-R for making it capable of displaying different versions of the same expressions, using face and neck, in a way that they are easy to understand for Japanese and for Western subjects. We present a system based on relevant studies of human communication and facial anatomy, as well as on the work of illustrators and cartoonists. The expression generator we developed can be adapted to specific cultures. Results confirmed the in-group advantage, showing that the recognition rate of this system is higher when the nationality of the subjects and the cultural characterisation of the shown expressions are coincident. We conclude that this system could be used, in future, on robots that have to interact in a social environment, with people with different cultural background. This paper analyzes a corpus of Nigerian Tweets collected during the run-up to the 2011 Nigerian Presidential election and compares it with official election returns and polling data. We found that counts of the mentions on Twitter of the two major candidates correlated strongly with polling and election results when compared across the country's geopolitical regions, though the same data over represented two other candidates who did not fare as well in the polls or at the ballot box. Sentiment extracted from Twitter was less accurate in capturing mean levels of support for the two major candidates and, in particular, showed a strong negativity bias against the incumbent president. Twitter sentiment did mirror regional trends in the polling and election results though not as strongly as seen for mention counts. We demonstrate methodologically how to sample these data, extract sentiment, and compare this sentiment with ground-truth polling data and election results. Although social media clearly capture opinion about contentious issues, our results suggest that the opinion represented there may not always accurately reflect true public opinion.