DOCSLIB.ORG

Supporting Information For: the Limits of Social Mobilization Alex Rutherford, Manuel Cebrian, Sohan Dsouza, Esteban Moro, Alex Pentland, Iyad Rahwan

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Supporting Information For: the Limits of Social Mobilization Alex Rutherford, Manuel Cebrian, Sohan Dsouza, Esteban Moro, Alex Pentland, Iyad Rahwan Supporting Information for: The limits of social mobilization Alex Rutherford, Manuel Cebrian, Sohan Dsouza, Esteban Moro, Alex Pentland, Iyad Rahwan Contents A Population Density Distribution 1 B Simulation Details 5 B.1 Simulation Method . 5 B.2 Branching Factor Distribution . 7 C Further Results 8 C.1 Parameter Exploration . 8 C.2 DARPA Network Challenge Balloon Locations . 8 C.3 Search Completion Times . 11 C.4 Effect of Search Origin . 13 C.5 Searchability . 14 C.6 Search Efficiency . 14 C.7 Logarithmic Blendability Function . 14 D Density Dependent Mobility & Distribution of Passive Recruits 16 D.1 Density Dependent Mobility Radius . 16 D.2 Completion Time & Balloon Location Probability . 16 D.3 Super-linear Blendability Function . 20 D.4 Logarithmic Blendability Function . 20 E Analysis of Findability Function 22 A Population Density Distribution As input to the simulations, we use gridded population density [1] based on census data [2] for the mainland USA. This comprises 7,820,528 cells each with an area of 1km2, of which 5,060,288 are populated (i.e. 2,760,240 are empty). The distribution of population amongst the cells displays a familiar fat-tailed behaviour (Fig.(2)), as a result 90% of the cells contain a population <10 allowing for very precise simulation of the recruitment dynamics. The fat-tailed behaviour of cell populations is a result of the highly hetrogeneous distribution of population typical of a country with large urban centres. This can be seen more clearly still in Fig. (3). The spatial autocorrelation is seen to decay very slowly over distance with a chacteristic lengthscale of around 14km. Thus on average, any location with high population density has a surrounding area of π(142) ∼ 616km2 with comparable density, and likewise a cell of low density will be surrounded by an area of low density. This small-scale homegenity is observed despite large-scale heterogeneity. A further pathological demonstration of the population heterogeneity is the difference between Liben-Nowell et al neighborhoods in a dense urban environment (New York City) and a relatively less populated urban environment (Yuma, AZ). Correlations in network topology such as transitivity and clustering as well as bursty dynamics have been found to slow diffusive behaviour on networks [3], and we conjecture an analagous effect due to spatial clustering of population. In NY we see a very small Liben-Nowell et al neighborhood, whereas in AZ the lower population density gives rise to a larger neighborhood. We see that the closer cells have a much stronger weighting i.e. probability of social tie, compared to more distant cells. 1 Figure 1: Map of Population Density (logarithmic scale, per km2) Across Mainland USA and Locations of Balloons in Red Balloon Challenge. (Lambert Azimuthal Equal Area Projection) Figure 2: Log-log Plot of Population Distribution of Cells 2 Figure 3: Spatial Autocorrelation of Population Density 3 Figure 4: Heatmaps of Liben-Nowell et al neighborhoods in New York City (top) and Yuma, AZ (bottom) relative to the central black cell. Each square is a 1km2 area, the shading reflects the weight of each cell (high to low, red to blue) i.e. The probability of a friendship between a person in that cell and a person in the central cell. The irregular shape of the lower image is due to unpopulated regions. 4 B Simulation Details B.1 Simulation Method The simulation begins by seeding the cell corresponding to MIT in Cambridge, MA with 164 seeds representing the first round of recruitment from the MIT team. Each seed recruits a given number of new active nodes taken from the empirical branching distribution in [4]. Each new, active recruit is assigned an action time given as the current time plus a waiting time taken from a log-normal distribution as observed in [5] and placed into a priority queue sorted by future action time. The waiting time represents the time between the parent node sending the message and the child node sending the message to others and joining the search. Recruits are of 2 geographical types; background, which are chosen uniformly at random from the entire population and rank based, which are selected in inverse proportion to their rank according to (1) as in [6]. 1 P / (1) ij P p k:rik<rij k Where Pij is the probability of friendship between agents i and j and pk is the population at k. Each successful recruitment is determined to be a background recruit with probability n p = background (2) nbackground + nrank and rank based with probability 1 − p. nrank and nbackground are given as 5.5 and 2.5 respectively from [6]. The probability of geographical recruits is truncated at 105 and distances of 104km. As well as active recruits which join the branching process, each parent node also gives rise to npass passive recruits regardless of the number of active recruits. These behave in exactly the same way as the active recruits described above, except upon activation, they search for balloons but do not perform further recruitment. The simulation proceeds by stepping forward in time until the activation time of the recruit at the top of the pri- ority queue. This recruit is removed from the queue, performs any recruitment of its own, adds any such new recruits to the queue and locates any balloons in its vicinity. The number of people recruited from each cell is counted as the simulation progresses, and may not exceed the population of the cell. Any further recruitment from a cell beyond its population is ignored and assumed to represent a loop in the recruitment network. Passive recruits may later be recruited into an active role, however agents selected as active recruits and later selected as passive recruits have no effect on the search process. The calculation of the weights and ranks for each Liben-Nowell et al neighborhood of every cell at this level of pre- cision is extremely computationaly demmanding. Regions of low population density give rise to larger neighborhoods containing up to 20,000 other cells. Therefore the full set of cells and weights for each neighborhood was calculated in advance and retreived from a database as required during the course of each simulation. If a balloon is located within the search neighborhood of a recruit, that balloon is `found' immediately which is reasonable since the agent in question is able to report any sightings made before her recruitment. Once a balloon in a cell is found, any further recruitment from the population within that cell will have no effect.The balloon is found with probability 1 within its neighborhood of size rmob. Figure (5) shows the results of a typical (unsuccessful) search simulation. Initially the number of recruits grows steadily but eventually saturates around 5 × 105, likewise the rate at which cells are searched decreases. The difference between the number of activated recruits (blue) and recruited but not activated individuals (red) represents the size of the action queue, when they converge there are no further agents waiting to act and the branching process terminates. Only the initial dynamics are displayed here, due to the skew in waiting time, the final 20 recruits take 3 years to join the search. 5 Figure 5: Plot of the Number of Activated Recruits (blue), Number of Agents Recruited But Not Yet Activated (red) and Number of Cells Searched (green). Parameters are npass = 400, rmob = 1km 6 B.2 Branching Factor Distribution We determine the distribution of the branching factor to be sampled in our simulations by fitting to a subset of the 4495 individuals which signed up to the balloon challenge. We consider the initial round of recruitment by the MIT seed node to be atypical since it targeted a number of individuals far greater than the average number of friends of an individual, and it is likely that a larger proportion of those targeted will be recruited due to the affinity of the team with the challenge. Thus our simulations begin with 164 seed recruits, and further recruitment proceeds in accordance with the typical branching behaviour. Since extra effort is likely to be excerted by seed nodes to recruit individuals in the initial stages of any social mobilization task, our setup maintains generality. Due to the small sample set, we exclude several single large outliers in the fitting procedure which were considered atypical i.e. media outlets or other individuals with a strong affinity to the task. It must be emphasised that the exact distribution of the branching factor is difficult to determine due to the sparsity and uniqueness of the data. In any case it is not impor- tant, since the mean is well below the tipping point, other processes dominate the search as discussed in the main paper. The branching behaviour of the remaining 4483 nodes was fit to a power law distribution with exponent α = 2:0786 and mean < Ro >= 0:8906. In order to appropriately sample the power law distribution, we construct a Harris discrete distribution function for branching factor k. H P (k) = αβ (3) β + kα Where Hαβ is chosen to ensure normalisation and β allows fitting to a given empirical mean value. 7 C Further Results C.1 Parameter Exploration Figure 6: Heat map of the average number of balloons located (top) and probability of success (bottom) in 100 distinct search simulations for different values of passive recruits and mobility radius. Figure (6) shows the average number of balloons located (top), and the probability of locating all 10 balloons (bottom) in 500 simulations for each of a range of values of passive recruits and mobility radii.
Load more

Recommended publications
  • Recursive Incentives and Innovation in Social Networks
    Recruiting Hay to Find Needles: Recursive Incentives and Innovation in Social Networks Erik P. Duhaime1, Brittany M. Bond1, Qi Yang1, Patrick de Boer2, & Thomas W. Malone1 1Massachusetts Institute of Technology 2University of Zurich Finding innovative solutions to complex problems is often about finding people who have access to novel information and alternative viewpoints. Research has found that most people are connected to each other through just a few degrees of separation, but successful social search is often difficult because it depends on people using their weak ties to make connections to distant social networks. Recursive incentive schemes have shown promise for social search by motivating people to use their weak ties to find distant targets, such as specific people or even weather balloons placed at undisclosed locations. Here, we report on a case study of a similar recursive incentive scheme for finding innovative ideas. Specifically, we implemented a competition to reward individual(s) who helped refer Grand Prize winner(s) in MIT’s Climate CoLab, an open innovation platform for addressing global climate change. Using data on over 78,000 CoLab members and over 36,000 people from over 100 countries who engaged with the referral contest, we find that people who are referred using this method are more likely than others both to submit proposals and to submit high quality proposals. Furthermore, we find suggestive evidence that among the contributors referred via the contest, those who had more than one degree of separation from a pre-existing CoLab member were more likely to submit high quality proposals. Thus, the results from this case study are consistent with the theory that people from distant networks are more likely to provide innovative solutions to complex problems.
    [Show full text]
  • From Conventional to Electric Cars
    Individual mobility: From conventional to electric cars Alberto V. Donati Panagiota Dilara* Christian Thiel Alessio Spadaro Dimitrios Gkatzoflias Yannis Drossinos European Commission, Joint Research Centre, I-21027 Ispra (VA), Italy *Current address: European Commission, DG GROW, B-1049 Brussels, Belgium 2015 Forename(s) Surname(s) Report EUR 27468 EN European Commission Joint Research Centre Institute for Energy and Transport Contact information Yannis Drossinos Address: Joint Research Centre, Via Enrico Fermi 2749, TP 441, I-21027 Ispra (VA), Italy E-mail: [email protected] Tel.: +39 0332 78 5387 Fax: +39 0332 78 5236 JRC Science Hub http://ses.jrc.ec.europa.eu Legal Notice This publication is a Science and Policy Report by the Joint Research Centre, the European Commission’s in-house science service. It aims to provide evidence-based scientific support to the European policy-making process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. All images © European Union 2015 JRC97690 EUR 27468 EN ISBN 978-92-79-51894-2 (PDF) ISBN 978-92-79-51895-9 (print) ISSN 1831-9424 (online) ISSN 1018-5593 (print) doi:10.2790/405373 (online) Luxembourg: Publications Office of the European Union, 2015 © European Union, 2015 Reproduction is authorised provided the source is acknowledged. Printed in Italy Abstract The aim of this report is twofold. First, to analyse individual (driver) mobility data to obtain fundamental statistical parameters of driving patterns for both conventional and electric vehicles.
    [Show full text]
  • Impact of Human Mobility on Social Networks
    100 JOURNAL OF COMMUNICATIONS AND NETWORKS, VOL. 17, NO. 2, APRIL 2015 Impact of Human Mobility on Social Networks Dashun Wang and Chaoming Song Abstract: Mobile phone carriers face challenges from three syner- ical space—social connections between individuals and their gistic dimensions: Wireless, social, and mobile. Despite significant mobility—no longer exist in isolation. Rather they increasingly advances that have been made about social networks and human interact with and depend on each other. To truly harness and un- mobility, respectively, our knowledge about the interplay between leash the potential of social and mobile technologies, we need two layers remains largely limited, partly due to the difficulty in to develop a quantitative framework of the interplay between so- obtaining large-scale datasets that could offer at the same time so- cial networks and human mobility patterns. cial and mobile information across a substantial population over Our knowledge about the interplay between social networks an extended period of time. In this paper, we take advantage of a massive, longitudinal mobile phone dataset that consists of hu- and human mobility patterns is limited, partly due to the diffi- man mobility and social network information simultaneously, al- culty in obtaining large-scale dataset that could offer at the same lowing us to explore the impact of human mobility patterns on the time social and mobile information across a substantial popula- underlying social network. We find that human mobility plays an tion over an extended period of time. This situation is changing important role in shaping both local and global structural prop- drastically, however, thanks to the ever-increasing availability erties of social network.
    [Show full text]
  • Flow Descriptors of Human Mobility Networks
    FLOW DESCRIPTORS OF HUMAN MOBILITY NETWORKS David Pastor-Escuredo1,2,*, Enrique Frias-Martinez3 1LifeD Lab, Madrid, Spain 2Center Innovation and Technology for Development, Technical University Madrid, Spain. 3Telefónica Research, Madrid, Spain *email: [email protected] ABSTRACT The study of human mobility is key for a variety of problems like traffic forecasting, migration flows of virus spreading. The recent explosion of geolocated datasets has contributed to better model those problems. In this context, mobile phone datasets enable the timely and fine-grained study human mobility, allowing the description of mobility at different resolutions and with different spatial, temporal and social granularity. In this paper we propose a systematic analysis to characterize mobility network flows and topology and assess their impact into individual traces. Discrete flow-based descriptors are used to classify and understand human mobility patterns at multiple scales. This framework is suitable to assess urban planning, optimize transportation, measure the impact of external events and conditions, monitor internal dynamics and profile users according to their movement patterns. INTRODUCTION Since over more than a decade, mobile phone data has enabled for the analysis of individual and collective human mobility (Gonzalez et al., 2008) (Candia et al., 2008). Several studies have focused on the models to predict mobility from big data sources and theoretical and practical limits of predictability (Song et al., 2010) (Simini et al., 2012) (Lu et al., 2013). The analysis of mobility has permitted novel studies and applications in the intersection with social science (Blondel et al., 2015) (Naboulsi et al., 2015). A relevant focus of research and application has been the humanitarian sector.
    [Show full text]
  • Friendship and Mobility: User Movement in Location-Based Social Networks
    Friendship and Mobility: User Movement In Location-Based Social Networks Eunjoon Cho ∗ Seth A. Myers ∗ Jure Leskovec Stanford University Stanford University Stanford University [email protected] [email protected] [email protected] ABSTRACT Even though the above are some of the most fundamental ques- Even though human movement and mobility patterns have a high tions and hypotheses about the dynamics of human mobility, an- degree of freedom and variation, they also exhibit structural pat- swers to them remain largely unknown mostly due to the fact that terns due to geographic and social constraints. Using cell phone reliable large scale human mobility data has been hard to obtain. location data, as well as data from two online location-based social Recently, however, location-based online social networking appli- networks, we aim to understand what basic laws govern human mo- cations have emerged, where users share their current location by tion and dynamics. We find that humans experience a combination checking-in on websites such as Foursquare, Facebook, Gowalla, of periodic movement that is geographically limited and seemingly etc. While traditionally records of calls made by cell phones have random jumps correlated with their social networks. Short-ranged been used to track the location of the cell phone towers associ- travel is periodic both spatially and temporally and not effected by ated with the calls [12, 17, 30], location-based social networks [15, the social network structure, while long-distance travel is more in- 29, 28] provide an important new dimension in understanding hu- fluenced by social network ties. We show that social relationships man mobility.
    [Show full text]
  • Arxiv:1907.07062V6 [Physics.Soc-Ph] 4 Jun 2021
    SCIKIT-MOBILITY: A PYTHON LIBRARY FOR THE ANALYSIS, GENERATION AND RISK ASSESSMENT OF MOBILITY DATA Luca Pappalardo Filippo Simini Gianni Barlacchi * Roberto Pellungrini ISTI-CNR, Italy University of Bristol, UK FBK, Italy University of Pisa, Italy [email protected] Argonne National Lab, US Amazon Alexa, Germany [email protected] [email protected] [email protected] ABSTRACT The last decade has witnessed the emergence of massive mobility data sets, such as tracks generated by GPS devices, call detail records, and geo-tagged posts from social media platforms. These data sets have fostered a vast scientific production on various applications of mobility analysis, ranging from computational epidemiology to urban planning and transportation engineering. A strand of literature addresses data cleaning issues related to raw spatiotemporal trajectories, while the second line of research focuses on discovering the statistical “laws” that govern human movements. A significant effort has also been put on designing algorithms to generate synthetic trajectories able to reproduce, realistically, the laws of human mobility. Last but not least, a line of research addresses the crucial problem of privacy, proposing techniques to perform the re-identification of individuals in a database. A view on state of the art cannot avoid noticing that there is no statistical software that can support scientists and practitioners with all the aspects mentioned above of mobility data analysis. In this paper, we propose scikit-mobility, a Python library that has the ambition of providing an environment to reproduce existing research, analyze mobility data, and simulate human mobility habits. scikit-mobility is efficient and easy to use as it extends pandas, a popular Python library for data analysis.
    [Show full text]
  • Natural Human Mobility Patterns and Spatial Spread of Infectious Diseases
    Natural Human Mobility Patterns and Spatial Spread of Infectious Diseases The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Belik, Vitaly, Theo Geisel, and Dirk Brockmann. “Natural Human Mobility Patterns and Spatial Spread of Infectious Diseases.” Physical Review X 1, no. 1 (August 2011). As Published http://dx.doi.org/10.1103/PhysRevX.1.011001 Publisher American Physical Society Version Final published version Citable link http://hdl.handle.net/1721.1/89013 Terms of Use Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. PHYSICAL REVIEW X 1, 011001 (2011) Natural Human Mobility Patterns and Spatial Spread of Infectious Diseases Vitaly Belik,1,* Theo Geisel,1,2 and Dirk Brockmann3,4 1Max Planck Institute for Dynamics and Self-Organization, Go¨ttingen, Germany 2Faculty of Physics, University of Go¨ttingen, Go¨ttingen, Germany 3Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois, USA 4Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois, USA (Received 13 October 2010; revised manuscript received 23 May 2011; published 8 August 2011) We investigate a model for spatial epidemics explicitly taking into account bidirectional movements between base and destination locations on individual mobility networks. We provide a systematic analysis of generic dynamical features of the model on regular and complex metapopulation network topologies and show that significant dynamical differences exist to ordinary reaction-diffusion and effective force of infection models. On a lattice we calculate an expression for the velocity of the propagating epidemic front and find that, in contrast to the diffusive systems, our model predicts a saturation of the velocity with an increasing traveling rate.
    [Show full text]
  • A Survey of Systemic Risk Analytics
    OFFICE OF FINANCIAL RESEARCH U.S. DEPARTMENT OF THE TREASURY Office of Financial Research Working Paper #0001 January 5, 2012 A Survey of Systemic Risk Analytics 1 Dimitrios Bisias 2 Mark Flood 3 Andrew W. Lo 4 Stavros Valavanis 1 MIT Operations Research Center 2 Senior Policy Advisor, OFR, [email protected] 3 MIT Sloan School of Management, [email protected] 4 MIT Laboratory for Financial Engineering The Office of Financial Research (OFR) Working Paper Series allows staff and their co-authors to disseminate preliminary research findings in a format intended to generate discussion and critical comments. Papers in the OFR Working Paper Series are works in progress and subject to revision. Views and opinions expressed are those of the authors and do not necessarily represent official OFR or Treasury positions or policy. Comments are welcome as are suggestions for improvements, and should be directed to the authors. OFR Working Papers may be quoted without additional permission. www.treasury.gov/ofr A Survey of Systemic Risk Analytics∗ Dimitrios Bisias†, Mark Flood‡, Andrew W. Lo§, Stavros Valavanis¶ This Draft: January 5, 2012 We provide a survey of 31 quantitative measures of systemic risk in the economics and finance literature, chosen to span key themes and issues in systemic risk measurement and manage- ment. We motivate these measures from the supervisory, research, and data perspectives in the main text, and present concise definitions of each risk measure—including required inputs, expected outputs, and data requirements—in an extensive appendix. To encourage experimentation and innovation among as broad an audience as possible, we have developed open-source Matlab code for most of the analytics surveyed, which can be accessed through the Office of Financial Research (OFR) at http://www.treasury.gov/ofr.
    [Show full text]
  • Differentially-Private Next-Location Prediction with Neural Networks
    Differentially-Private Next-Location Prediction with Neural Networks Ritesh Ahuja Gabriel Ghinita Cyrus Shahabi University of Southern California University of Massachusetts Boston University of Southern California [email protected] [email protected] [email protected] ABSTRACT in a Machine-Learning-as-a-Service (MLaaS) infrastructure to The emergence of mobile apps (e.g., location-based services, produce business-critical outcomes and actionable insights (e.g., geo-social networks, ride-sharing) led to the collection of vast traffic optimization). Figure 1 illustrates these cases. Given his- amounts of trajectory data that greatly benefit the understand- torical trajectories, several approaches exploit recent results in ing of individual mobility. One problem of particular interest is neural networks to produce state-of-the-art POI recommender next-location prediction, which facilitates location-based adver- systems [10, 35, 58]. Even though individual trajectory data are tising, point-of-interest recommendation, traffic optimization, not disclosed directly, the model itself retains significant amounts etc. However, using individual trajectories to build prediction of specific movement details, which in turn may leak sensitive models introduces serious privacy concerns, since exact where- information about an individual’s health status, political orienta- abouts of users can disclose sensitive information such as their tion, entertainment preferences, etc. The problem is exacerbated health status or lifestyle choices. Several research efforts focused by the use of neural networks, which have the tendency to overfit on privacy-preserving next-location prediction, but they have the data, leading to unintended memorization of rare sequences serious limitations: some use outdated privacy models (e.g., k- which act as quasi-identifiers of their owners [9, 13].
    [Show full text]
  • A Comparison of Spatial-Based Targeted Disease Mitigation Strategies Using Mobile Phone Data Stefania Rubrichi1* ,Zbigniewsmoreda1 and Mirco Musolesi2
    Rubrichi et al. EPJ Data Science (2018)7:17 https://doi.org/10.1140/epjds/s13688-018-0145-9 REGULAR ARTICLE OpenAccess A comparison of spatial-based targeted disease mitigation strategies using mobile phone data Stefania Rubrichi1* ,ZbigniewSmoreda1 and Mirco Musolesi2 *Correspondence: [email protected] Abstract 1SENSE, Orange Labs, Chatillon, France Epidemic outbreaks are an important healthcare challenge, especially in developing Full list of author information is countries where they represent one of the major causes of mortality. Approaches that available at the end of the article can rapidly target subpopulations for surveillance and control are critical for enhancing containment and mitigation processes during epidemics. Using a real-world dataset from Ivory Coast, this work presents an attempt to unveil the socio-geographical heterogeneity of disease transmission dynamics. By employing a spatially explicit meta-population epidemic model derived from mobile phone Call Detail Records (CDRs), we investigate how the differences in mobility patterns may affect the course of a hypothetical infectious disease outbreak. We consider different existing measures of the spatial dimension of human mobility and interactions, and we analyse their relevance in identifying the highest risk sub-population of individuals, as the best candidates for isolation countermeasures. The approaches presented in this paper provide further evidence that mobile phone data can be effectively exploited to facilitate our understanding of individuals’ spatial behaviour and its relationship with the risk of infectious diseases’ contagion. In particular, we show that CDRs-based indicators of individuals’ spatial activities and interactions hold promise for gaining insight of contagion heterogeneity and thus for developing mitigation strategies to support decision-making during country-level epidemics.
    [Show full text]
  • Mobile Homophily and Social Location Prediction
    Mobile Homophily and Social Location Prediction Halgurt Bapierre Chakajkla Jesdabodi Georg Groh TU München TU München TU München Faculty for Informatics Faculty for Informatics Faculty for Informatics [email protected] [email protected] [email protected] ABSTRACT 2. SOCIAL RELATIONS AND MOBILE HO- The mobility behavior of human beings is predictable to a MOPHILY: RELATED WORK varying degree e.g. depending on the traits of their personal- Social relations and geographic distance d exhibit many ity such as the trait extraversion - introversion: the mobility interesting interrelations. Propinquity has been studied in of introvert users may be more dominated by routines and form of the probability of friendship relations as a function habitual movement patterns, resulting in a more predictable of d:[48, 25, 59, 44,8, 68, 69, 77]. Most studies find a mobility behavior on the basis of their own location history power law relation p(d) / d−α with slightly different expo- while, in contrast, extrovert users get about a lot and are nents. [77] find an inverse correlation between distance of explorative by nature, which may hamper the prediction of centers of life of two users and the relative size of the over- their mobility. However, socially more active and extrovert lap of their immediate social relations. In contrast to that, users meet more people and share information, experiences, [39] found that purely online (virtual) interaction between believes, thoughts etc. with others. which in turn leads users may not be not strongly influenced by distance. The to a high interdependency between their mobility and social mutual influence of mobility and social tie strength has also lives.
    [Show full text]
  • Reflecting on the DARPA Red Balloon Challenge
    contributed articles Doi:10.1145/1924421.1924441 how more recent developments (such Finding 10 balloons across the U.S. illustrates as social media and crowdsourcing) could be used to solve challenging how the Internet has changed the way problems involving distributed geo- we solve highly distributed problems. locations. Since the Challenge was an- nounced only about one month before By John C. tanG, manueL Cebrian, nicklaus a. GiaCoBe, the balloons were deployed, it was not hyun-Woo Kim, taemie Kim, anD Douglas “BeaKeR” WickeRt only a timed contest to find the bal- loons but also a time-limited challenge to prepare for the contest. Both the dif- fusion of how teams heard about the Challenge and the solution itself dem- Reflecting onstrated the relative effectiveness of mass media and social media. The surprising efficiency of apply- ing social networks of acquaintances on the DaRPa to solve widely distributed tasks was demonstrated in Stanley Milgram’s celebrated work9 popularizing the no- tion of “six degrees of separation”; that is, it typically takes no more than six in- Red Balloon termediaries to connect any arbitrary pair of people. Meanwhile, the Internet and other communication technolo- gies have emerged that increase the Challenge ease and opportunity for connections. These developments have enabled crowdsourcing—aggregating bits of information across a large number of users to create productive value—as a popular mechanism for creating en- cyclopedias of information (such as ThE 2009 dARPA Red Balloon Challenge (also known Wikipedia) and solving other highly distributed problems.1 as the DARPA Network Challenge) explored how the The Challenge was announced at the Internet and social networking can be used to solve “40th Anniversary of the Internet” event a distributed, time-critical, geo-location problem.
    [Show full text]