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Limits of Social Mobilization

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Limits of Social Mobilization Limits of social mobilization Alex Rutherforda, Manuel Cebrianb,c, Sohan Dsouzaa, Esteban Morod,e, Alex Pentlandf, and Iyad Rahwana,g,1 aComputing and Information Science, Masdar Institute of Science and Technology, Abu Dhabi 54224, United Arab Emirates; bDepartment of Computer Science and Engineering, University of California at San Diego, La Jolla, CA 92093; cNational Information and Communications Technology Australia, Melbourne, VIC 3010, Australia; dDepartamento de Matemáticas and Grupo Interdisciplinar de Sistemas Complejos, Universidad Carlos III de Madrid, 28911 Madrid, Spain; eInstituto de Ingeniería del Conocimiento, Universidad Autónoma de Madrid, 28049 Madrid, Spain; fMedia Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139; and gSchool of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom Edited* by Jon Kleinberg, Cornell University, Ithaca, NY, and approved March 1, 2013 (received for review September 19, 2012) The Internet and social media have enabled the mobilization of large targets were visible for only 12 h and followed normal itineraries crowds to achieve time-critical feats, ranging from mapping crises around the cities of Stockholm, London, Bratislava, New York, in real time, to organizing mass rallies, to conducting search- and Washington DC. Our winning team located three of the five and-rescue operations over large geographies. Despite significant suspects using social media, without any of the team members success, selection bias may lead to inflated expectations of the being based in any of the target cities (10), demonstrating yet an- efficacy of social mobilization for these tasks. What are the limits other example of time-critical social mobilization in tasks that re- of social mobilization, and how reliable is it in operating at these quire coverage of large geographies. limits? We build on recent results on the spatiotemporal structure Despite these numerous successes, we still have limited un- of social and information networks to elucidate the constraints derstanding of the limits of technology-mediated mobilization. If they pose on social mobilization. We use the DARPA Network we are to rely on social media to react to time-critical emergen- Challenge as our working scenario, in which social media were cies, it is important to understand the conditions under which they used to locate 10 balloons across the United States. We conduct can be successful and the risks of failure associated with them. A high-resolution simulations for referral-based crowdsourcing and particular case, of highly practical importance, is to understand obtain a statistical characterization of the population recruited, the extent to which we can expect to cover a certain geographical geography covered, and time to completion. Our results demon- area in a given amount of time. For this, we must understand the strate that the outcome is plausible without the presence of mass complete statistical characterization of the population recruited, media but lies at the limit of what time-critical social mobilization geographical area covered, and completion time it takes for social can achieve. Success relies critically on highly connected individ- mobilization to succeed in a particular task, as well as to quantify uals willing to mobilize people in distant locations, overcoming the likelihood of failure. the local trapping of diffusion in highly dense areas. However, This lack of understanding is especially prone to selection bias even under these highly favorable conditions, the risk of unsuc- over few successful social mobilization strategies and may lead to cessful search remains significant. These findings have implica- inflated expectations of the reliability and efficacy of these tech- tions for the design of better incentive schemes for social niques (11, 12). However, it is beyond experimental capabilities to mobilization. They also call for caution in estimating the reliability perform randomized experimentation with large crowdsourcing of this capability. challenges [with notable exceptions emerging recently (13, 14)]. Modeling efforts in the wake of the H1N1 and other global COMPUTER SCIENCES social networks | human dynamics pandemics also have provided a valuable insight into time-sensi- tive human dynamics on a large scale, via spatial simulation (15) or he Internet and online social media are now credited with the network-based diffusion (16, 17). In common with these efforts, Tunprecedented ability to coordinate the mobilization of large we model the interaction and connection of large numbers of masses of people to achieve incredible feats that require cover- agents; however, we consider the propagation of a message that SCIENCES may be transmitted without direct physical proximity and generally POLITICAL age of large geographical and informational landscapes in very “ ” limited time. Social media have been used to mobilize volunteers shorter incubation times, leading to faster spreading. Thus, the mechanism of “infection” (recruitment) is independent of human to map natural disasters in real time (1) and to conduct large- – scale search-and-rescue missions (2). Online social networks also mobility patterns (18 20), which, in our case, contribute only to have been an important tool in the coordination of mass political the area searched. In this work, we build on recent results on social network rallies (3, 4). structure, information diffusion, and urban economics to eluci- Endeavors such as the Defense Advanced Research Projects date the constraints they pose on social mobilization. In partic- Agency (DARPA) Network Challenge (5) aimed to test the ular, we conduct high-resolution simulations of the DARPA power of the Internet and social media in time-critical social Network Challenge. We obtain statistical characterizations of the mobilization to its absolute limits. The Network Challenge re- population recruited, geography covered, and time to locate the quired competing teams to locate and submit the coordinates of 10 balloons, together with their dependencies on the instrumental 10 tethered weather balloons dispersed at random locations all variables. over the continental United States. The winning team, based at Our results demonstrate, surprisingly, that the DARPA Net- Massachusetts Institute of Technology (MIT), won the challenge work Challenge outcome is plausible and thus not simply a fluke by locating all balloons in less than 9 h. The MIT team used an that can be explained only by the role of mass media. Therefore, incentive scheme to kick start an information and recruitment cascade that resulted in 4,400 sign-ups to the team’s Web site within 48 h. Analysis of the diffusion revealed that the recursive Author contributions: A.R., M.C., E.M., A.P., and I.R. designed research; A.R., S.D., and I.R. incentive scheme may have played an important role in maxi- performed research; A.R., M.C., E.M., A.P., and I.R. contributed new reagents/analytic mizing the speed and branching of the diffusion to limits above tools; A.R. analyzed data; and A.R., M.C., E.M., A.P., and I.R. wrote the paper. what normally are observed in viral propagation schemes (6–8). The authors declare no conflict of interest. More recently, the State Department’s Tag Challenge required *This Direct Submission article had a prearranged editor. competing teams to locate and photograph five target “thieves” 1To whom correspondence should be addressed. E-mail: [email protected]. fi (actors) in ve different cities in the United States and Europe, This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. based only on a mug shot released at 8:00 AM local time (9). The 1073/pnas.1216338110/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1216338110 PNAS | April 16, 2013 | vol. 110 | no. 16 | 6281–6286 Downloaded by guest on September 29, 2021 the challenge lies at the limit of what time-critical social mobili- 1 zation can achieve. Mobilization requires highly connected, highly Pij ∝ P ; [1] pk active individuals to be motivated to propagate the message k:rik<rij to many friends and to mobilize people in distant locations, P overcoming the local trapping of diffusion in highly dense areas. where ij is the probability of friendship between agents in two distinct grid cells i and j, pk is the population in cell k, and rik is Moreover, even under highly favorable conditions, the risk of the distance from cell i to cell k. The quantity on the right-hand mobilization failure remains significant. These findings have side is the rank of an agent in i with respect to agent in j;itis implications for the design of better incentive schemes for social a measure of the number of people located between i and j. mobilization. They also call for caution in estimating the re- Thus, the spatial distribution of a person’s friends is now strongly liability of this capability. dependent on the local population density, with the effect that two people separated by a given large distance are more likely to Simulation Model be friends in a rural region than in a dense, urban environment. In seeking to understand social mobilization, we must consider the It also was noted that friends could be classified into two distinct many different dynamics that underpin such a process, particularly types. One group comprises rank-based friends chosen because the branching dynamics of recruitment, the temporal dynamics of of geographical proximity (e.g., a shared workplace) in accor- message propagation, the geographical spread of social networks, dance with rank scaling. The authors also observed a “back- and the scales and aspects of human mobility. A full accountability ground” probability that an agent might be friends with any other of each process will render the simulation and its understanding randomly chosen agent from across the country; in this case, impossible, and thus we concentrate on the main ingredients that friendships are independent of geography. Further, these two explain the observed behavior in each of the processes. types of friends were found to exist in a ratio of 2.5 distance- independent friendships to 5.5 rank-based friendships among the Recruitment user average of 8 friends.
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