Field Of Computational Social Science Research Articles

MEASURING CULTURAL DYNAMICS THROUGH THE EUROVISION SONG CONTEST

Measuring culture and its dynamics through surveys has important limitations, but the emerging field of computational social science allows us to overcome them by analyzing large-scale datasets. In this paper, we study cultural dynamics through the votes in the Eurovision song contest, which are decided by a crowd-based scheme in which viewers vote through mobile phone messages. Taking into account asymmetries and imperfect perception of culture, we measure cultural relations among European countries in terms of cultural affinity. We propose the Friend-or-Foe coefficient, a metric to measure voting biases among participants of a Eurovision contest. We validate how this metric represents cultural affinity through its relation with known cultural distances, and through numerical analysis of biased Eurovision contests. We apply this metric to the historical set of Eurovision contests from 1975 to 2012, finding new patterns of stronger modularity than using votes alone. Furthermore, we define a measure of polarization that, when applied to empirical data, shows a sharp increase within EU countries during 2010 and 2011. We empirically validate the relation between this polarization and economic indicators in the EU, showing how political decisions influence both the economy and the way citizens relate to the culture of other EU members.

Quantifying the Digital Traces of Hurricane Sandy on Flickr

Society’s increasing interactions with technology are creating extensive “digital traces” of our collective human behavior. These new data sources are fuelling the rapid development of the new field of computational social science. To investigate user attention to the Hurricane Sandy disaster in 2012, we analyze data from Flickr, a popular website for sharing personal photographs. In this case study, we find that the number of photos taken and subsequently uploaded to Flickr with titles, descriptions or tags related to Hurricane Sandy bears a striking correlation to the atmospheric pressure in the US state New Jersey during this period. Appropriate leverage of such information could be useful to policy makers and others charged with emergency crisis management.

Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data

Use of socially generated “big data” to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between “real time monitoring” and “early predicting” remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.

A Methodology for Complex Social Simulations

Social simulation — an emerging field of computational social science — has progressed from simple toy models to increasingly realistic models of complex social systems, such as agent-based models where heterogeneous agents interact with changing natural or artificial environments. These larger, multidisciplinary projects require a scientific research methodology distinct from, say, simpler social simulations with more limited scope, intentionally minimal complexity, and typically under a single investigator. This paper proposes a methodology for complex social simulations — particularly inter- and multi-disciplinary socio-natural systems with multi-level architecture — based on a succession of models akin to but distinct from the late Imre Lakatos' notion of a 'research programme'. The proposed methodology is illustrated through examples from the Mason-Smithsonian project on agent-based models of the rise and fall of polities in Inner Asia. While the proposed methodology requires further development, so far it has proven valuable for advancing the scientific objectives of the project and avoiding some pitfalls.

Living analytics methods for the social web

The collective effervescence of social media production has been enjoying a great deal of success in recent years. The hundred of millions of users who are actively participating in the Social Web are exposed to ever-growing amounts of sites, relationships, and information. This work contributes state-of-the-art methods and techniques to the emerging field of Living Analytics , whose main goal is to capture people interactions in real-time and to analyze these data in order to relieve information overload. We introduce intelligent filtering approaches that exploit social interactions, multidimensional relationships, metadata, and other data becoming ubiquitous in the social web, in order to discover and recommend the most relevant and attractive information that meets users' individual needs. In particular, the contributions of this work fall into mainly two categories. (i) Recommender Systems : We present novel algorithms that advance the state-of-the-art in Online Collaborative Filtering. Moreover, we propose an approach based on Swarm Intelligence to directly optimize ranking functions for item recommendations. New approaches to address the cold-start problem in social recommender systems are also part of our contributions. In addition, we also offer a personalized ranking algorithm for Epidemic Intelligence. (ii) Collective Intelligence : Our contributions in the field of computational social science are twofold. First, we explore how social media streams can be exploited for Epidemic Intelligence and show its potential for early warning detection and outbreak analysis and control. Second, we show how the real-time nature of social media streams can be leveraged to take the pulse of political emotions. In total, the methods and studies included in this work constitute an analytics toolbox to help understand and analyze the social web.

Geographic Information Systems and Spatial Agent-Based Model Simulations for Sustainable Development

In recent years the interdisciplinary field of Computational Social Science has developed theory and methodologies for building spatial Agent-Based Social Simulation (ABSS) models of human societies that are situated in ecosystems with land cover and climate. This article explains the needs and demand for Geographic Information Systems (GIS) in these types of agent-based models, with an emphasis on models applied to Eastern Africa and Inner Asia and relevance for understanding and analyzing development issues. The models are implemented with the MASON (Multi-Agent Simulator Of Networks and Neighborhoods) system, an open-source simulation environment in the Java language and suitable for developing ABSS models with GIS for representing spatial features.

A Methodology for Complex Social Simulations

Social simulation — an emerging field of computational social science — has progressed from simple toy models to increasingly realistic models of complex social systems, such as agent-based models where heterogeneous agents interact with changing natural or artificial environments. These larger, multidisciplinary projects require a scientific research methodology distinct from, say, simpler social simulations with more limited scope, intentionally minimal complexity, and typically under a single investigator. This paper proposes a methodology for complex social simulations—particularly inter- and multi-disciplinary socio-natural systems with multi-level architecture—based on a succession of models akin to but distinct from the late Imre Lakatos' notion of a 'research programme'. The proposed methodology is illustrated through examples from the Mason-Smithsonian project on agent-based models of the rise and fall of polities in Inner Asia. While the proposed methodology requires further development, so far it has proven valuable for advancing the scientific objectives of the project and avoiding some pitfalls.

A Comparison of Agent-Based Models of Income Tax Evasion

This article compares three multiagent-based simulation models of individual income tax evasion. The models'similarities and differences are highlighted, and their significance for the field of computational social science is discussed. Emphasis is placed on the importance of process validity for models intended to represent real-world phenomena of interest to policy makers.