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Social Norm Creation Support in Social Networks

Sajadi, Hadi | 2018

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  1. Type of Document: Ph.D. Dissertation
  2. Language: English
  3. Document No: 51414 (52)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Habibi, Jafar
  7. Abstract:
  8. The topic of influencing and regulating the behavior of communities is one which has always gathered attention by various governments, civic institutions, organizations and individuals. Research in this topic has recently been accelerated with the expansion of large scale online social networks. The massive moment of user data which exists in such settings has led to various researchers investigating methods to create new social behaviors such as habits, values and norms, with the core of these methods being the flow of ideas between social actors in order to create such social habits and behaviors.In this context, social norms are a core concept in social sciences and play a critical role in regulating a society’s behaviors as it is a less costly alternative to establishing new laws and regulations. Social networks are an important and effective infrastructure in which social norms can evolve.Therefore there is a need for theoretical models in order to study the spread of social norms in social networks. In this thesis, by using the intrinsic properties of norms, we redefine and tune the Rescorla-Wagner conditioning model in order to obtain an affective model for the spread of social norms. We extend this model for a network of people as a row-stochastic Matrix-based process. The potential structures of steady states of this process are studied. Then, we formulate the problem of maximizing the adoption of social norms in a social network by finding the best set of initial norm adopters. Finally, we propose an algorithm for solving this problem which runs in polynomial time and experiment it on different networks. Our experiments show that our algorithm has superior performance over other methods
  9. Keywords:
  10. Social Networks ; Spread ; Algorithm ; Conditioning ; Social Norm ; Rescorla-Wagner

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