Marin Stamov CS 765 Oct

Social network Importance of the information spread in social networks Information types Advertising in social networks Information spread models Sequential social learning Rational word-of-mouth learning Information aggregation model Binary agreement model Diffusion model of actionable information My contribution

A social structure made up of individuals connected by one or more specific types of interdependency Nodes -> individuals Edges -> friendship

networks Cell phone call networks Real-world interactions Online networks

Age distribution of Facebook profiles

Knowledge is power Misinformation and rumors can be mistaken for useful information Individual’s beliefs can be changed based on interactions with their friends

News Facts Opinions Rumors Behavior Fashion Obesity

Products recommended by trustful sources are more likely to be considered Recommendations based on interests and preferences Individuals satisfied with a product, will advertise it to their friends even without gating paid to do so

A class of computational models for simulating the interactions of autonomous agents Each agent makes individual decisions based on the information that he has and a set of rules Agents may execute various behaviors appropriate for the system they represent

Agent’s actions and behavior can be influenced by other agents in observable world Agents decide what action to make when their turn comes Does a popularity of a choice indicate that this is a good choice? The agent involved in herd behavior act in specific way, not motivated by personal reasons

Generations of agents make choices between two alternatives. Each agent asks agents that have already made a choice, what did they chose and how satisfied they are If each agent samples two or more others, in the long run every agent will choose the same thing

Each scalar in the model represents a belief that a certain individual holds Agents meet pair-wise Two types of agents: regular and forceful Regular – regular --> updated beliefs are set to the average of their pre-meeting beliefs Forceful – regular --> updated beliefs are set to the beliefs of the forceful agent

Opinions can be: A B Undecided AB At each step: A random speaker is chosen A random neighbor of the speaker is chosen Different from epidemic like models A “converted” individual can revert back symmetric in both opinions

If spoken opinion not on listener’s list he adds it If it is on the list both keep only spoken opinion consensus state can be reached A A B A Speaker Listener

A committed set of minority opinion holders on a network, can reverse the majority Applications: Influencing public opinion Reducing hostile opinion

The information trust value is related to the social relationship between the sender and the receiver Believer Undecided Disbelieved Uninformed 1 0

The nodes process and act on the information Diffusion model with abort information Abort message can be broadcasted after the action message Nodes combine action and abort information Disbelieved nodes spread abort information

Analyze what factors affect the information spread in social networks Betweenness, Closeness, and Degree Node influence Edges weights (trust) Information importance and it’s representation Try to identify misinformation and limit its spread Create new information spread model or improve an existing one

[1] Daron Acemoglu,Asuman Ozdaglar, Spread of (Mis)Information in Social Networks Games and Economic Behavior 7 (2010) [2] D. Acemoglu, Munther Dahleh, Ilan Lobel, Bayesian learning in social networks, Preprint, (2008) [3] A. Banerjee and D. Fudenberg, Word-of-mouth learning, Games and Economic Behavior 46 (2004) [4] V. Bala and S. Goyal, Learning from neighbours, Review of Economic Studies 65(1998) [5] A. Banerjee, A simple model of herd behavior, Quarterly Journal of Economics 107(1992) [6] S. Sreenivasan, J. Xie, W. Zhang, Influencing with committed minorities, NetSci (2011) [7] Cindy Hui, Modeling the Spread of Actionable Information in Social Networks, (2011) [8] Lada Adamic, Co-evolution of network structure and content, NetSci (2011)