http://www.epjdatascience.com The latest research articles published by EPJ Data Science 2013-04-04T00:00:00Z AbstractModifiable health behaviors, a leading cause of illness and death in many countries, are often driven by individual beliefs and sentiments about health and disease. Individual behaviors affecting health outcomes are increasingly modulated by social networks, for example through the associations of like-minded individuals - homophily - or through peer influence effects. Using a statistical approach to measure the individual temporal effects of a large number of variables pertaining to social network statistics, we investigate the spread of a health sentiment towards a new vaccine on Twitter, a large online social network. We find that the effects of neighborhood size and exposure intensity are qualitatively very different depending on the type of sentiment. Generally, we find that larger numbers of opinionated neighbors inhibit the expression of sentiments. We also find that exposure to negative sentiment is contagious - by which we merely mean predictive of future negative sentiment expression - while exposure to positive sentiments is generally not. In fact, exposure to positive sentiments can even predict increased negative sentiment expression. Our results suggest that the effects of peer influence and social contagion on the dynamics of behavioral spread on social networks are strongly content-dependent. http://www.epjdatascience.com/content/2/1/4 Marcel Salathe Duy Vu Shashank Khandelwal David Hunter EPJ Data Science 2013, null:4 2013-04-04T00:00:00Z doi:10.1140/epjds16 /content/figures/epjds16-toc.gif EPJ Data Science 2193-1127 ${item.volume} 4 2013-04-04T00:00:00Z XML AbstractBackgroundLanguage has functions that transcend the transmission of information and varies with social context. To find out how language and social network structure interlink, we studied communication on Twitter, a broadly-used online messaging service.ResultsWe show that the network emerging from user communication can be structured into a hierarchy of communities, and that the frequencies of words used within those communities closely replicate this pattern. Consequently, communities can be characterised by their most significantly used words. The words used by an individual user, in turn, can be used to predict the community of which that user is a member.ConclusionsThis indicates a relationship between human language and social networks, and suggests that the study of online communication offers vast potential for understanding the fabric of human society. Our approach can be used for enriching community detection with word analysis, which provides the ability to automate the classification of communities in social networks and identify emerging social groups. http://www.epjdatascience.com/content/2/1/3 John Bryden Sebastian Funk Vincent Jansen EPJ Data Science 2013, null:3 2013-02-25T00:00:00Z doi:10.1140/epjds15 /content/figures/epjds15-toc.gif EPJ Data Science 2193-1127 ${item.volume} 3 2013-02-25T00:00:00Z XML The paper presents an analysis of the length of comments posted in Internet discussion fora, based on a collection of large datasets from several sources. We found that despite differences in the forum language, the discussed topics and user emotions, the comment length distributions are very regular and described by the lognormal form with a very high precision. We discuss possible origins of this regularity and the existence of a universal mechanism deciding the length of the user posts. We suggest that the observed lognormal dependence may be due to an entropy maximizing combination of two psychological factors which are perceived on a non-linear, logarithmic scale in accordance with the Weber-Fechner law, namely the time spent on post related considerations and the comment length itself. This hypothesis is supported by an experimental check of text length recognition capacity, confirming proportionality of the ‘just noticeable differences’ for text lengths - the basis of the Weber-Fechner law. http://www.epjdatascience.com/content/2/1/2 Pawel Sobkowicz Mike Thelwall Kevan Buckley Georgios Paltaglou Antoni Sobkowicz EPJ Data Science 2013, null:2 2013-02-18T00:00:00Z doi:10.1140/epjds14 /content/figures/epjds14-toc.gif EPJ Data Science 2193-1127 ${item.volume} 2 2013-02-18T00:00:00Z XML The importance of data sharing has become a mantra within the science research community. However, sharing has not been as easy (or as readily adopted) as advocates have suggested. Questions of privacy, individual scientist’s rights to their research, and industry-academia divides have been significant hurdles. This article looks at the history of the debates and problems associated with data access that occurred during the ‘human genome wars’ and their aftermath as a way to explore some of the challenges facing diverse research communities. http://www.epjdatascience.com/content/2/1/1 Barbara Jasny EPJ Data Science 2013, null:1 2013-02-12T00:00:00Z doi:10.1140/epjds13 /content/figures/epjds13-toc.gif EPJ Data Science 2193-1127 ${item.volume} 1 2013-02-12T00:00:00Z XML AbstractComplex dynamics of social media emerge from the interaction between the patterns of social connectivity of users and the information exchanged along such social ties. Unveiling the underlying mechanisms that drive the evolution of online social systems requires a deep understanding of the interplay between these two aspects. Based on the case of the aNobii social network, an online service for book readers, we investigate the dynamics of link creation and the social influence phenomenon that may trigger information diffusion in the social graph. By confirming that social partner selection is strongly driven by structural, geographical, and topical proximity, we develop a machine-learning social link recommender for individual users trained on a set of features selected as best predictive out of several and we test it on the still widely unexplored domain of a network of interest. We also analyze the influence process from the two distinct perspectives of users and items. We show that link creation plays an immediate effect on the alignment of user profiles and that the established social ties are a good substrate for social influence. We quantitatively measure influence by tracking the patterns of diffusion of specific pieces of information and comparing them with appropriate null models. We discover an appreciable signal of social influence even though item consumption is a very slow process in this context. All the detected patterns of social attachment and influence are observed to be stronger when considering the social subgraph on which communication effectively occurs. Based on our study of the dynamics of the aNobii social network, we investigate the possibility to predict the evolution of such a complex social system. http://www.epjdatascience.com/content/1/1/12 Luca Aiello Alain Barrat Ciro Cattuto Rossano Schifanella Giancarlo Ruffo EPJ Data Science 2012, null:12 2012-12-05T00:00:00Z doi:10.1140/epjds12 /content/figures/epjds12-toc.gif EPJ Data Science 2193-1127 ${item.volume} 12 2012-12-05T00:00:00Z XML AbstractWe analyse a large data set of genetic markers obtained from populations of Cymodocea nodosa, a marine plant occurring from the East Mediterranean to the Iberian-African coasts in the Atlantic Ocean. We fully develop and test a recently introduced methodology to infer the directionality of gene flow based on the concept of geographical segregation. Using the Jensen-Shannon divergence, we are able to extract a directed network of gene flow describing the evolutionary patterns of Cymodocea nodosa. In particular we recover the genetic segregation that the marine plant underwent during its evolution. The results are confirmed by natural evidence and are consistent with an independent cross analysis. http://www.epjdatascience.com/content/1/1/11 Paolo Masucci Sophie Arnaud-Haond Victor Eguiluz Emilio Hernandez-Garcia Ester Serrao EPJ Data Science 2012, null:11 2012-11-28T00:00:00Z doi:10.1140/epjds11 /content/figures/epjds11-toc.gif EPJ Data Science 2193-1127 ${item.volume} 11 2012-11-28T00:00:00Z XML We study spatiotemporal correlations and temporal diversities of handset-based service usages by analyzing a dataset that includes detailed information about locations and service usages of 124 users over 16 months. By constructing the spatiotemporal trajectories of the users we detect several meaningful places or contexts for each one of them and show how the context affects the service usage patterns. We find that temporal patterns of service usages are bound to the typical weekly cycles of humans, yet they show maximal activities at different times. We first discuss their temporal correlations and then investigate the time-ordering behavior of communication services like calls being followed by the non-communication services like applications. We also find that the behavioral overlap network based on the clustering of temporal patterns is comparable to the communication network of users. Our approach provides a useful framework for handset-based data analysis and helps us to understand the complexities of information and communications technology enabled human behavior. http://www.epjdatascience.com/content/1/1/10 Hang-Hyun Jo Márton Karsai Juuso Karikoski Kimmo Kaski EPJ Data Science 2012, null:10 2012-11-06T00:00:00Z doi:10.1140/epjds10 /content/figures/epjds10-toc.gif EPJ Data Science 2193-1127 ${item.volume} 10 2012-11-06T00:00:00Z XML Understanding social dynamics that govern human phenomena, such as communications and social relationships is a major problem in current computational social sciences. In particular, given the unprecedented success of online social networks (OSNs), in this paper we are concerned with the analysis of aggregation patterns and social dynamics occurring among users of the largest OSN as the date: Facebook. In detail, we discuss the mesoscopic features of the community structure of this network, considering the perspective of the communities, which has not yet been studied on such a large scale. To this purpose, we acquired a sample of this network containing millions of users and their social relationships; then, we unveiled the communities representing the aggregation units among which users gather and interact; finally, we analyzed the statistical features of such a network of communities, discovering and characterizing some specific organization patterns followed by individuals interacting in online social networks, that emerge considering different sampling techniques and clustering methodologies. This study provides some clues of the tendency of individuals to establish social interactions in online social networks that eventually contribute to building a well-connected social structure, and opens space for further social studies. http://www.epjdatascience.com/content/1/1/9 Emilio Ferrara EPJ Data Science 2012, null:9 2012-11-06T00:00:00Z doi:10.1140/epjds9 /content/figures/epjds9-toc.gif EPJ Data Science 2193-1127 ${item.volume} 9 2012-11-06T00:00:00Z XML We present a contribution to the debate on the predictability of social events using big data analytics. We focus on the elimination of contestants in the American Idol TV shows as an example of a well defined electoral phenomenon that each week draws millions of votes in the USA. This event can be considered as basic test in a simplified environment to assess the predictive power of Twitter signals. We provide evidence that Twitter activity during the time span defined by the TV show airing and the voting period following it correlates with the contestants ranking and allows the anticipation of the voting outcome. Twitter data from the show and the voting period of the season finale have been analyzed to attempt the winner prediction ahead of the airing of the official result. We also show that the fraction of tweets that contain geolocation information allows us to map the fanbase of each contestant, both within the US and abroad, showing that strong regional polarizations occur. The geolocalized data are crucial for the correct prediction of the final outcome of the show, pointing out the importance of considering information beyond the aggregated Twitter signal. Although American Idol voting is just a minimal and simplified version of complex societal phenomena such as political elections, this work shows that the volume of information available in online systems permits the real time gathering of quantitative indicators that may be able to anticipate the future unfolding of opinion formation events. http://www.epjdatascience.com/content/1/1/8 Fabio Ciulla Delia Mocanu Andrea Baronchelli Bruno Goncalves Nicola Perra Alessandro Vespignani EPJ Data Science 2012, null:8 2012-07-31T00:00:00Z doi:10.1140/epjds8 /content/figures/epjds8-toc.gif EPJ Data Science 2193-1127 ${item.volume} 8 2012-07-31T00:00:00Z XML AbstractEach year, crowd disasters happen in different areas of the world. How and why do such disasters happen? Are the fatalities caused by relentless behavior of people or a psychological state of panic that makes the crowd ‘go mad’? Or are they a tragic consequence of a breakdown of coordination? These and other questions are addressed, based on a qualitative analysis of publicly available videos and materials, which document the planning and organization of the Love Parade in Duisburg, Germany, and the crowd disaster on July 24, 2010. Our analysis reveals a number of misunderstandings that have widely spread. We also provide a new perspective on concepts such as ‘intentional pushing’, ‘mass panic’, ‘stampede’, and ‘crowd crushes’. The focus of our analysis is on the contributing causal factors and their mutual interdependencies, not on legal issues or the judgment of personal or institutional responsibilities. Video recordings show that people stumbled and piled up due to a ‘domino effect’, resulting from a phenomenon called ‘crowd turbulence’ or ‘crowd quake’. Crowd quakes are a typical reason for crowd disasters, to be distinguished from crowd disasters resulting from ‘mass panic’ or ‘crowd crushes’. In Duisburg, crowd turbulence was the consequence of amplifying feedback and cascading effects, which are typical for systemic instabilities. Accordingly, things can go terribly wrong in spite of no bad intentions from anyone. Comparing the incident in Duisburg with others, we give recommendations to help prevent future crowd disasters. In particular, we introduce a new scale to assess the criticality of conditions in the crowd. This may allow preventative measures to be taken earlier on. Furthermore, we discuss the merits and limitations of citizen science for public investigation, considering that today, almost every event is recorded and reflected in the World Wide Web. http://www.epjdatascience.com/content/1/1/7 Dirk Helbing Pratik Mukerji EPJ Data Science 2012, null:7 2012-06-25T00:00:00Z doi:10.1140/epjds7 /content/figures/epjds7-toc.gif EPJ Data Science 2193-1127 ${item.volume} 7 2012-06-25T00:00:00Z XML