Language as a Measure of Welfare

Globalization is a process driven by international trade that leads to interactions and integration of people and government of different nations. Such process has been impacting us in many ways including our living standards or quality of life (QoL). At the same time, the integration between peoples of the world leads to a stronger diffusion of languages, ideas, and values; more recently the integration has received further boost by the emergence of online social networks. Online social networks give us a platform to connect without any restriction to geographic regions, language usage, costumes, etc. In fact, they are the quintessential example of globalization. Yet the links between language usage in society and QoL has received little attention. Since both language usage and the QoL are influenced by globalization it is just natural that one tries to study both subjects combined. This paper investigates if one can be used as a proxy for the other. Using approaches based on network science, our analysis of a large-scale Twitter dataset reveals that the patterns of user connectivities on online social networks (such as Twitter) as a function of languages usage is correlated to the QoL. Introduction and Motivation Evidence of globalization has been observed in many domains such as culture, economy, and international policies. The expansion of international trade and foreign investment is often seen as a sign of economic growth; higher economic growth rates and greater affluence are conducive to wellbeing (as in QoL). However, what is the wellbeing of a society? Many critics argue that GDP cannot capture the nation’s of wellbeing (Milenkovic et al. 2014). They claim that GDP is intended to measure the productivity of a nation and hence is an insufficient measure to quantify the QoL; wellbeing of a nation is a multi-dimensional concept and not the economic growth alone. There could be other social conditions that play a role in improving QoL. On the other hand, linguistic imperialism or the forcing of the dominant language in today’s world is also an outcome of (cultural) globalization. This paper looks at how these two concepts (language and QoL) are intertwined; we are interested in understanding the extent to which language can be seen as a proxy of wellbeing. Copyright c © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. The United Nations (UN) introduced a measure called Human Development Index (HDI) as a shift away from economic growth as the only measure of prosperity. The HDI is a composite metric that considers life expectancy, education and per-capita income in its equation, and is consequently a better indicator of QoL of people than GDP. The HDI allow countries to be ranked in four tiers: low, medium, high and very high. Although GDP is sometimes used as an indicator of prosperity, it does not necessarily correlate to HDI. An example is Cuba which according to the 2007 data, has low GDP of PPP US$6,876 and very high HDI of 0.863 (the max is 1) (Sandrin 2011). Authors have tried to look at relations between QoL and other factors. Ranis found that human development has important effects on economic growth (Ranis, Stewart, and Ramirez 2000). An increase in the capabilities available to individuals allow them to pursue occupations in which they are more productive. In this sense, human development is correlated to the human capital and human capital, in turn, correlates with the economic growth. Several studies used HDI in an attempt to understand specific human characteristics. One of the more interesting examples look at the relation between HDI and obesity levels (McLaren 2007). Mocanu et. al characterized the worldwide linguistic diversity in Twitter using geo-tagged data at different scales from country to neighborhood scales (Mocanu et al. 2013). They show that the usage of Twitter is not uniform and has a correlation with economical factors. In another study, Kulshrestha et al. demonstrated the influence of geography in the cultural and linguistic backgrounds in Twitter (Kulshrestha et al. 2012). Economic imbalances in society is correlated to the imbalance in the total number of tweets. For example, US accounts for 25% world GDP and 72% of all tweets produced in Twitter. Ronen et al. studied the interactions between languages using one billion tweets, book translations and editors of Wikipedia (Ronen et al. 2014). English was found as a hub in the network. This study revealed that there exists a strong correlation between the number of famous people native to a language and the position (eigenvector centrality) of the language in the network. Saha and Menezes also demonstrated that the positions of languages on Twitter indicate several interesting insights including the visibility of information generated in a particular language (Saha and Menezes 2016a; Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference