Fast, global, and sensitively reacting to political, economic and social events of any kind, these are attributes that social media like Twitter share with foreign exchange markets. The leading assumption of this paper is that information which can be distilled from public debates on Twitter has predictive content for exchange rate movements. This assumption prompted a Twitter-based exchange rate model that harnesses regARIMA analyses for short-term out-of-sample ex post forecasts of the daily closing prices of EUR/USD spot exchange rates. The analyses used Tweet counts collected from January 1, 2012 – September 27, 2013. To identify concepts mentioned on Twitter with a predictive potential the analysis followed a 2-step selection. Firstly, a heuristic qualitative analysis assembled a long list of 594 concepts, e.g., Merkel, Greece, Cyprus, crisis, chaos, growth, unemployment expected to covary with the ups and downs of the EUR/USD exchange rate. Secondly, cross-validation using window averaging with a fixed-sized rolling origin was deployed to select concepts and corresponding univariate time series that had error scores below chance level as defined by the random walk model.

Home-work commuting is known to be one of the major components of human mobility and therefor always attracted much research attention. One of the well-known assumptions being the focus of many works in this area is the universal uniformity of commute times. However, quantifications of commute patterns have often been baffled by the intrinsic differences in the data collection methods, which make the observations from different countries incomparable. In the present work we use mobile phone data offering a common methodology for investigating into the mobility pattern in different parts of the world including entire countries as different as Portugal and Ivory Coast as well as cities (Boston) also comparing results with those obtained from vehicle GPS traces in Milan. We showed that despite substantial spatial and infrastructural differences, the commute time distributions and average values are indeed largely independent of commute distance or country.

Trending topics are the online conversations that grab collective attention on social media. They are continually changing and often reflect exogenous events that happen in the real world. Trends are localized in space and time as they are driven by activity in specific geographic areas that act as sources of traffic and information flow. Taken independently, trends and geography have been discussed in recent literature on online social media; although, so far, little has been done to characterize the relation between trends and geography. Here we investigate more than eleven thousand topics that trended on Twitter in 63 main US locations during a period of 50 days in 2013. This data allows us to study the origins and pathways of trends, how they compete for popularity at the local level to emerge as winners at the country level, and what dynamics underlie their production and consumption in different geographic areas.

Transmitting messages in the most efficient way as possible has always been one of politicians main concerns during electoral processes. Due to the rapidly growing number of users, online social networks have become ideal platforms for politicians to interact with their potential voters. Exploiting the available potential of these tools to maximize their influence over voters is one of politicians actual challenges. To step in this direction, we have analyzed the user activity in the online social network Twitter, during the 2011 Spanish Presidential electoral process, and found that such activity is correlated with the election results. We introduce a new measure to study political support in Twitter, which we call the Relative Support. We have also characterized user behavior by analyzing the structural and dynamical patterns of the complex networks emergent from the mention and retweet networks. Our results suggest that the collective attention is driven by a very small fraction of users. Furthermore we have analyzed the interactions taking place among politicians, observing a lack of debate. Finally we develop a network growth model to reproduce the interactions taking place among politicians.

Reliable statistical information is important to make political decisions on a sound basis and to help measure the impact of policies. Unfortunately, statistics offices in developing countries have scarce resources and statistical censuses are therefore conducted sporadically. Based on mobile phone communications and history of airtime credit purchases, we estimate the relative income of individuals, the diversity and inequality of income, and an indicator for socioeconomic segregation for fine-grained regions of an African country. Our study shows how to use mobile phone datasets as a starting point to understand the socio-economic state of a country, which can be especially useful in countries with few resources to conduct large surveys.

The FuturICT project is a response to the European Flagship Call in the Area of Future and Emerging Technologies, which is planning to spend 1 billion EUR on each of two flagship projects over a period of 10 years. FuturICT seeks to create an open, global but decentralized, democratically controlled information platform that will use online data and real-time measurements together with novel theoretical models and experimental methods to achieve a paradigm shift in our understanding of today’s strongly interdependent and complex world and make our techno-socio-economic systems more flexible, adaptive, resilient, sustainable, and livable through a participatory approach.