Social media fingerprints of unemployment

Alejandro Llorente, Manuel García-Herránz, Manuel Cebrián and Esteban Moro (2014), arXiV1411.3140 [PDF]

SummaryspainflowPublicly available social media data can be used to quantify deviations from typical patterns of behavior and uncover how these deviations signal the socio-economical status of regions. Using data from geolocalized Twitter messages, we find that unemployment is correlated with technology adoption, daily activity, diversity in mobility patterns, and correctness in communication style. These behavioral metrics serve to build simple, interpretable, and cost-effective socio-economical predictors from these novel digital datasets. Our extensive investigation allows us not only to build accurate behavioral models of how unemployment impacts diverse geographical areas, but also to assessing the relevance and uniqueness of previously reported social media datasets to understand economical development.

Recent wide-spread adoption of electronic and pervasive technologies has enabled the study of human behavior at an unprecedented level, uncovering universal patterns underlying human activity, mobility, and inter-personal communication. In the present work, we investigate whether deviations from these universal patterns may reveal information about the socio-economical status of geographical regions. We quantify the extent to which deviations in diurnal rhythm, mobility patterns, and communication styles across regions relate to their unemployment incidence. For this we examine a country-scale publicly articulated social media dataset, where we quantify individual behavioral features from over 145 million geo-located messages distributed among more than 340 different Spanish economic regions, inferred by computing communities of cohesive mobility fluxes. We find that regions exhibiting more diverse mobility fluxes, earlier diurnal rhythms, and more correct grammatical styles display lower unemployment rates. As a result, we provide a simple model able to produce accurate, easily interpretable reconstruction of regional unemployment incidence from their social-media digital fingerprints alone. Our results show that cost-effective economical indicators can be built based on publicly-available social media datasets.


  • See the video of thousands of trips in Spain used to characterize the mobility between municipalities in Spain

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Macroscopic Response to Microscopic Intrinsic Noise in Three-Dimensional Fisher Fronts

Svetozar Nesic, Rodolfo Cuerno, and Esteban Moro
Physical Review Letters 113, 180602 (2014) [APS]

We study the dynamics of three-dimensional Fisher fronts in the presence of density fluctuations. To this end we simulate the Fisher equation subject to stochastic internal noise, and study how the front moves and roughens as a function of the number of particles in the system, N. Our results suggest that the macroscopic behaviour of the system is driven by the microscopic dynamics at its leading edge where number fluctuations are dominated by rare events. Contrary to naive expectations, the strength of front fluctuations decays extremely slowly as log N, inducing large-scale fluctuations which we find belong to the one-dimensional Kardar-Parisi-Zhang universality class of kinetically rough interfaces. Hence, we find that there is no weak-noise regime for Fisher fronts, even for realistic numbers of particles in macroscopic systems.

Comunidades de partidarios en redes sociales: estudio de las elecciones catalanas de 2010 y 2012

Esteban Moro en, Cotarelo, R. & Olmeda, J.A. (Comps.) (forthcoming). La democracia del siglo XXI. Política, medios de comunicación, internet y redes sociales. Actas de las II Jornadas españolas de ciberpolítica, 28 de mayo de 2013. Madrid: Centro de Estudios Políticos y Constitucionales. [pdf]

En los últimos años hemos asistido a un incremento notable de eventos de carácter político y/o social que han sido promovidos (cuando no originados) a través de medios de comunicación electrónicos. En particular, el uso cada vez más frecuente de plataformas de sociabilidad electrónicas como Facebook o Twitter para compartir opinión o información sobre temas de actualidad, política o sociales ha hecho que estás plataformas pasen a ser herramientas imprescindibles dentro de la comunicación política y también los nuevos canales en los que se comparten ideas, se busca información o se organizan campañas dentro del contexto político.

Uno de los ámbitos más estudiados ha sido el de las elecciones, en el que la discusión política está acotada tanto en ámbito como en tiempo y donde, por tanto, se produce un mayor posicionamiento político en las redes sociales de los usuarios, de los partidos, asociaciones, etc. El objetivo de esta contribución es estudiar este fenómeno de formación de grupos partidarios (partisanos) en el flujo de información en Twitter en un contexto político más variado en las que concurren una mayor diversidad opciones políticas (ver video). Asimismo pretendemos estudiar la caracterización, estabilidad de dichas comunidades y, en particular, la correlación entre sus propiedades y la estimación de voto. El hecho de disponer de datos con dos años de separación entre dos elecciones nos permite comprobar también la adecuación e invariabilidad de la metodología del análisis de redes sociales en dos situaciones diferentes.

Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks

Manuel Garcia-Herranz, Esteban Moro, Manuel Cebrian, Nicholas A. Christakis and James H. Fowler, PLoS ONE 9(4): e92413 (2014) [link]



Recent research has focused on the monitoring of global–scale online data for improved detection of epidemics, mood patterns, movements in the stock market political revolutions, box-office revenues, consumer behaviour and many other important phenomena. However, privacy considerations and the sheer scale of data available online are quickly making global monitoring infeasible, and existing methods do not take full advantage of local network structure to identify key nodes for monitoring. Here, we develop a model of the contagious spread of information in a global-scale, publicly- articulated social network and show that a simple method can yield not just early detection, but advance warning of contagious outbreaks. In this method, we randomly choose a small fraction of nodes in the network and then we randomly choose a friend of each node to include in a group for local monitoring. Using six months of data from most of the full Twittersphere, we show that this friend group is more central in the network and it helps us to detect viral outbreaks of the use of novel hashtags about 7 days earlier than we could with an equal-sized randomly chosen group. Moreover, the method actually works better than expected due to network structure alone because highly central actors are both more active and exhibit increased diversity in the information they transmit to others. These results suggest that local monitoring is not just more efficient, but also more effective, and it may be applied to monitor contagious processes in global–scale networks.

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Performance of Social Network Sensors During Hurricane Sandy

Yury Kryvasheyeu, Haohui Chen, Esteban Moro, Pascal Van Hentenryck, Manuel Cebrian (2013) [arxiv]

Isandynformation flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow, and a mean to derive early-warning sensors, improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioural properties derived from the “friendship paradox”, is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in user’s network centrality effectively translate into moderate awareness advantage (up to 26 hours); and that geo-location of users within or outside of the hurricane-affected area plays significant role in determining the scale of such advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility of implementing a simple “sentiment sensing” technique to detect and locate disasters.