marin stamov cs 765 oct 26 2011. social network importance of the information spread in social...
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Information spread in social networks
Marin StamovCS 765Oct 26 2011
OutlineSocial networkImportance of the information spread in social networksInformation typesAdvertising in social networks Information spread models
Sequential social learningRational word-of-mouth learningInformation aggregation modelBinary agreement modelDiffusion model of actionable information
My contribution
Social network
A social structure made up of individuals connected by one or more specific types of interdependency
Nodes -> individuals Edges -> friendship
• Email networks• Cell phone call networks• Real-world interactions• Online networks
• Age distribution of Facebook profiles
Importance of the information spread in social networks
Knowledge is power
Misinformation and rumors can be mistaken for useful information
Individual’s beliefs can be changed based on interactions with their friends
Types of information
NewsFactsOpinions RumorsBehaviorFashionObesity
Advertising in social networks
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
Agent based models
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
Sequential social learning model 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
Model of rational word-of-mouth learning
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
Information aggregation modelEach scalar in the model represents a belief that a certain individual holds Agents meet pair-wiseTwo 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
social influence: Binary agreement model
Opinions can be:ABUndecided AB
At each step:A random speaker is chosenA random neighbor of the speaker is chosen
Different from epidemic like modelsA “converted” individual can revert backsymmetric in both opinions
social influence: Binary agreement model
If spoken opinion not on listener’s list he adds itIf it is on the list both keep only spoken opinion
consensus state can be reached
A A B
A
Speaker Listener
social influence: Binary agreement model
A committed set of minority opinion holders on a network, can reverse the majority
Applications:Influencing public opinionReducing hostile opinion
Diffusion model of actionable information
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 informationAbort message can be broadcasted after the action messageNodes combine action and abort information
Disbelieved nodes spread abort information
My contribution
Analyze what factors affect the information spread in social networks
Betweenness, Closeness, and DegreeNode influenceEdges weights (trust)Information importance and it’s representation
Try to identify misinformation and limit its spreadCreate new information spread model or improve an existing one
References[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)
Questions ?Thank you!