Using Gossips to Spread Information: Theory and Evidence from Two Randomized Controlled Trials

Can we identify highly central individuals in a network without collecting network data, simply by asking community members? Can seeding information via such nominated individuals lead to significantly wider diffusion than via randomly chosen people, or even respected ones? In two separate large field experiments in India, we answer both questions in the affirmative. In particular, in 521 villages in Haryana, we provided information on monthly immunization camps to either randomly selected individuals (in some villages) or to individuals nominated by villagers as people who would be good at transmitting information (in other villages). We find that the number of children vaccinated every month is 22% higher in villages in which nominees received the information. We show that people’s knowledge of who are highly central individuals and good seeds can be explained by a model in which community members simply track how often they hear gossip about others. Indeed, we find in a third data set that nominated seeds are central in a network sense, and are not just those with many friends or in powerful positions.

[1]  Emily Breza,et al.  Changes in Social Network Structure in Response to Exposure to Formal Credit Markets , 2018, SSRN Electronic Journal.

[2]  Ariel BenYishay,et al.  Can Network Theory-Based Targeting Increase Technology Adoption? , 2018, American Economic Review.

[3]  Amin Saberi,et al.  Diffusion, Seeding, and the Value of Network Information , 2018, EC.

[4]  Emily Breza,et al.  When Less is More: Experimental Evidence on Information Delivery During India&Apos;S Demonetization , 2018, Review of Economic Studies.

[5]  Gabrielle Demange,et al.  Rumors and Social Networks , 2018 .

[6]  Alireza Tahbaz-Salehi,et al.  Seeing the Forest for the Trees? An Investigation of Network Knowledge , 2018, 1802.08194.

[7]  Matthew O. Jackson,et al.  A typology of social capital and associated network measures , 2017, Social Choice and Welfare.

[8]  Matthew O. Jackson,et al.  Behavioral Communities and the Atomic Structure of Networks , 2017, ArXiv.

[9]  Matthew O. Jackson,et al.  The Friendship Paradox and Systematic Biases in Perceptions and Social Norms , 2016, Journal of Political Economy.

[10]  E. Paluck,et al.  Changing climates of conflict: A social network experiment in 56 schools , 2016, Proceedings of the National Academy of Sciences.

[11]  Matthew O. Jackson,et al.  Pricing and Referrals in Diffusion on Networks , 2015, Games Econ. Behav..

[12]  David Krackhardt,et al.  A Preliminary Look at Accuracy in Egonets , 2014 .

[13]  G. Lawyer Understanding the spreading power of all nodes in a network: a continuous-time perspective , 2014, ArXiv.

[14]  Michele Benzi,et al.  A matrix analysis of different centrality measures , 2013, ArXiv.

[15]  Arun Sundararajan,et al.  Engineering social contagions: Optimal network seeding in the presence of homophily , 2013, Network Science.

[16]  Arun G. Chandrasekhar,et al.  Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia , 2012 .

[17]  T. Valente Network Interventions , 2012, Science.

[18]  Arun G. Chandrasekhar,et al.  The Diffusion of Microfinance , 2012, Science.

[19]  Damon Centola An Experimental Study of Homophily in the Adoption of Health Behavior , 2011, Science.

[20]  Jan U. Becker,et al.  Seeding Strategies for Viral Marketing: An Empirical Comparison , 2011 .

[21]  M. Sarvary,et al.  Network Effects and Personal Influences: The Diffusion of an Online Social Network , 2011 .

[22]  Leeat Yariv,et al.  Diffusion, Strategic Interaction, and Social Structure , 2011, Handbook of Social Economics.

[23]  Thomas W. Valente,et al.  Opinion Leadership and Social Contagion in New Product Diffusion , 2011, Mark. Sci..

[24]  A. Galeotti,et al.  The Law of the Few , 2010 .

[25]  Thomas W. Valente,et al.  Social Networks and Health: Models, Methods, and Applications , 2010 .

[26]  Thomas W. Valente,et al.  Social Networks and Health , 2010 .

[27]  Attila Ambrus,et al.  Consumption Risk-Sharing in Social Networks , 2010 .

[28]  Matthew O. Jackson,et al.  Naïve Learning in Social Networks and the Wisdom of Crowds , 2010 .

[29]  A. Belloni,et al.  Least Squares After Model Selection in High-Dimensional Sparse Models , 2009, 1001.0188.

[30]  Matthew O. Jackson,et al.  Average Distance, Diameter, and Clustering in Social Networks with Homophily , 2008, WINE.

[31]  Marcel Fafchamps,et al.  The formation of risk sharing networks , 2007 .

[32]  T. Valente,et al.  Identifying Opinion Leaders to Promote Behavior Change , 2007, Health education & behavior : the official publication of the Society for Public Health Education.

[33]  Stephen P. Borgatti,et al.  Identifying sets of key players in a social network , 2006, Comput. Math. Organ. Theory.

[34]  Antoni Calvó-Armengol,et al.  Centre De Referència En Economia Analítica Barcelona Economics Working Paper Series Working Paper Nº 178 Who's Who in Networks. Wanted: the Key Player Who's Who in Networks. Wanted: the Key Player Barcelona Economics Wp Nº 178 , 2022 .

[35]  Éva Tardos,et al.  Influential Nodes in a Diffusion Model for Social Networks , 2005, ICALP.

[36]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[37]  Douglas P. Woodward,et al.  A Tractable Approach to the Firm Location Decision Problem , 2003, Review of Economics and Statistics.

[38]  Béla Bollobás,et al.  Random Graphs, Second Edition , 2001, Cambridge Studies in Advanced Mathematics.

[39]  Tiziana Casciaro,et al.  Seeing things clearly: social structure, personality, and accuracy in social network perception , 1998 .

[40]  R. Evenson,et al.  The impact of T&V extension in Africa : the experience of Kenya and Burkina Faso , 1997 .

[41]  D. Iacobucci Networks in Marketing , 1996 .

[42]  S. Baker The Multinomial‐Poisson Transformation , 1994 .

[43]  S. Feld Why Your Friends Have More Friends Than You Do , 1991, American Journal of Sociology.

[44]  David Krackhardt,et al.  Cognitive social structures , 1987 .

[45]  P. Bonacich Power and Centrality: A Family of Measures , 1987, American Journal of Sociology.

[46]  Béla Bollobás,et al.  Random Graphs , 1985 .

[47]  Noah E. Friedkin,et al.  Horizons of Observability and Limits of Informal Control in Organizations , 1983 .

[48]  Juni Palmgren,et al.  The Fisher information matrix for log linear models arguing conditionally on observed explanatory variable , 1981 .

[49]  J. Coleman,et al.  Medical Innovation: A Diffusion Study. , 1967 .

[50]  E. Rogers,et al.  Diffusion of Innovations , 1964 .

[51]  P. Lazarsfeld,et al.  Personal Influence: The Part Played by People in the Flow of Mass Communications , 1956 .

[52]  Asuman Ozdaglar,et al.  A Simple Model of Cascades in Networks ∗ , 2016 .

[53]  Jens Woelke,et al.  Personal Influence. The Part Played by the People in the Flow of Mass Communication , 2016 .

[54]  Jess Benhabib,et al.  Handbook of Social Economics , 2011 .

[55]  Cynthia M. Lakon,et al.  How Correlated Are Network Centrality Measures? , 2008, Connections.

[56]  Stephen P. Borgatti,et al.  Centrality and network flow , 2005, Soc. Networks.

[57]  Béla Bollobás,et al.  Random Graphs: Notation , 2001 .

[58]  Joseph B. Lang,et al.  On the comparison of multinomial and Poisson log-linear models , 1996 .

[59]  D. Krackhardt Structural Leverage in Marketing , 1996 .