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 BA

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!