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.
Golder and Macy @cite_2 studied collective mood in Twitter across countries from 509 million Twitter posts by 2.4 million users over a 2-year period. Despite having different cultures, geographies, and religions, all countries (USA, Canada, UK, Australia, India, and English-speaking Africa) in their study showed similar mood rhythms---people tended to be more positive on weekends and early in the morning. @cite_0 examined the variation of Twitter users' emoticon usage patterns in cross cultures. They used Hofstede's national culture scores of 78 countries and found that collectivist cultures favor vertical and eye-oriented emoticons, where people within individualistic cultures favor horizontal and mouth-oriented emoticons. Hofstede's cultural dimensions have also been used to study whether culture of a country is associated with the way people use Twitter @cite_21 . In another study on cross-country Twitter communication, showed that cultural variables such as Hofstede's indices, language and intolerance have an impact on Twitter communication volume @cite_32 .
@cite_0 @cite_21 @cite_32 @cite_2
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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. With the advent of Twitter and other lightweight social-networking services, one might think that it is easier than ever to maintain geographically dispersed, weaker social ties. By contrast, in this study we show that the international Twitter communication landscape is not only still largely predetermined by physical distance, but that it also depends on countries' social, economic, and cultural attributes. We describe a study of an international Twitter mention network of 13 million users across over 100 countries. We show that the Gravity Model, which hypothesizes that the flow between two areas is proportional to their masses (which we approximate using internet penetration) and inversely proportional to the distance between them, is correlated (r=0.68) with the international communication flow. Using this model, along with other social, economic, and cultural variables, we predict the communication volume at Adjusted R2 of @math , with trade, language and racial intolerance especially impacting communication. We discuss the implications of these barriers to communication in the contexts of collaborative work, software design, and recommendation systems. We identified individual-level diurnal and seasonal mood rhythms in cultures across the globe, using data from millions of public Twitter messages. We found that individuals awaken in a good mood that deteriorates as the day progresses—which is consistent with the effects of sleep and circadian rhythm—and that seasonal change in baseline positive affect varies with change in daylength. People are happier on weekends, but the morning peak in positive affect is delayed by 2 hours, which suggests that people awaken later on weekends. 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. 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. 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 The 5-year-old social media Web site Twitter now claims that more than 100 million users post 230 million "tweets" (text messages up to 140 characters long) every day. In that torrent of data, some social scientists see an unprecedented opportunity to study human communication and social networks. On page [1878][1] of this week's issue of Science , researchers report their effort to use Twitter to study the collective moods of millions of people in diverse cultures around the world in real time. Their findings paint a portrait of humanity's mood swings. @PARASPLIT [1]: http: www.sciencemag.org cgi content full 333 6051 1878 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. In this article, we analyze and compare user behavior on two different microblogging platforms: (1) Sina Weibo which is the most popular microblogging service in China and (2) Twitter. Such a comparison has not been done before at this scale and is therefore essential for understanding user behavior on microblogging services. In our study, we analyze more than 40 million microblogging activities and investigate microblogging behavior from different angles. We (i) analyze how people access microblogs and (ii) compare the writing style of Sina Weibo and Twitter users by analyzing textual features of microposts. Based on semantics and sentiments that our user modeling framework extracts from English and Chinese posts, we study and compare (iii) the topics and (iv) sentiment polarities of posts on Sina Weibo and Twitter. Furthermore, (v) we investigate the temporal dynamics of the microblogging behavior such as the drift of user interests over time. Our results reveal significant differences in the microblogging behavior on Sina Weibo and Twitter and deliver valuable insights for multilingual and culture-aware user modeling based on microblogging data. We also explore the correlation between some of these differences and cultural models from social science research. 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 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. Despite the widespread adoption of Twitter internationally, little research has investigated the differences among users of different languages. In prior research, the natural tendency has been to assume that the behaviors of English users generalize to other language users. We studied 62 million tweets collected over a four-week period and found that more than 100 languages were used. Only half of the tweets were in English (51 ). Other popular languages including Japanese, Portuguese, Indonesian, and Spanish together accounted for 39 of the tweets. Examining users of the top 10 languages, we discovered cross-language differences in adoption of features such as URLs, hashtags, mentions, replies, and retweets. We discuss our work’s implications for research on large-scale social systems and design of cross-cultural communication tools. 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 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. Social Web describes a new culture of participation on the Web where more and more people actively participate in publishing and organizing Web content. As part of this culture, people leave a variety of traces when interacting with (other people via) Social Web systems. In this paper, we investigate user modeling strategies for inferring personal interest profiles from Social Web interactions. In particular, we analyze individual micro-blogging activities on Twitter. We compare different strategies for creating user profiles based on the Twitter messages a user has published and study how these profiles change over time. Moreover, we evaluate the quality of the user modeling strategies in the context of personalized recommender systems and show that those strategies which consider the temporal dynamics of the individual profiles allow for the best performance. Microblogging is a form of online communication by which users broadcast brief text updates, also known as tweets, to the public or a selected circle of contacts. A variegated mosaic of microblogging uses has emerged since the launch of Twitter in 2006: daily chatter, conversation, information sharing, and news commentary, among others. Regardless of their content and intended use, tweets often convey pertinent information about their author's mood status. As such, tweets can be regarded as temporally-authentic microscopic instantiations of public mood state. In this article, we perform a sentiment analysis of all public tweets broadcasted by Twitter users between August 1 and December 20, 2008. For every day in the timeline, we extract six dimensions of mood (tension, depression, anger, vigor, fatigue, confusion) using an extended version of the Profile of Mood States (POMS), a well-established psychometric instrument. We compare our results to fluctuations recorded by stock market and crude oil price indices and major events in media and popular culture, such as the U.S. Presidential Election of November 4, 2008 and Thanksgiving Day. We find that events in the social, political, cultural and economic sphere do have a significant, immediate and highly specific effect on the various dimensions of public mood. We speculate that large scale analyses of mood can provide a solid platform to model collective emotive trends in terms of their predictive value with regards to existing social as well as economic indicators. 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. Mood is a popular access point in music digital libraries and online music repositories, and is often represented as numerical values in a small number of emotion-related dimensions (e.g., valence and arousal). As music mood is recognized as culturally dependent, this study investigates whether regression models built with music data in one culture can be applied to music in another culture. Results indicate that cross-cultural predictions of both valence and arousal values are feasible.