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Confluence: Conformity Influence in Large Social Networks

Confluence: Conformity Influence in Large Social Networks. Authors: Tang, J., Wu, S., & Sun, J. Presented by John Kerr. Background. Conformity : the act of matching attitudes, beliefs, and behaviors to group norms.

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Confluence: Conformity Influence in Large Social Networks

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  1. Confluence: Conformity Influence in Large Social Networks Authors: Tang, J., Wu, S., & Sun, J.Presented by John Kerr

  2. Background • Conformity: the act of matching attitudes, beliefs, and behaviors to group norms. • Influenced by various factors: individual status, peer influence, group pressure. • Conformity can be an excellent measurement of social interactions in groups, but is hard to quantify. • Difficult to measure on large scales.

  3. Social Networks • Can provide an easily quantifiable source of information on group and individual actions, and therefore conformity. • Example 1: Political mobilization messages delivered to 61 million Facebook users. If an individual is aware that friends have made political votes, their likelihood to vote increases. (Bond et al.) • Example 2: If an individual is aware that their Facebook friends have clicked a certain ad, they will be more likely to click that same ad. (Bakshy et al.) • But these two studies only look at one type of conformity each.

  4. Challenges • How to define and differentiate between types of conformity? • 1 on 1 influence isn’t the same as group influence. • How to construct a computational model to learn the different conformity factors? • How to check the model for accuracy?

  5. Notation

  6. Individual Conformity • Ratio b/w the number of actions for which user v1conforms to a friend, v2, over the total number of actions performed by v1. • Also represents how likely v’s behavior is influenced by one particular friend, v2.

  7. Peer Conformity • The ratio b/w the number of actions performed by v1that conform to those by v2, over the total number of actions performed by v2.

  8. Group Conformity • The ratio b/w the number of actions performed by v to those performed by the group over total number of group actions. • Group action defined by the the percentage of users in the group performing that action.

  9. How to Model? • Confluence: describes what terms in a system can be rewritten in more than one way to yield the same result. • Simple example: Wikipedia

  10. Confluence Model • Can distinguish and quantify the effects of different types of conformities. • Attempts to maximize the probability of user actions given their corresponding attributes.

  11. Cont. • Different factors quantify how different levels of conformity determine the actions of user v. • Likelihood function comes from integrating all the factors together. • Use learned parameters that maximize the likelihood function to infer user’s future actions.

  12. Prediction:

  13. Experiment • Flickr: a photo sharing network. Action is defined as adding a comment to a photo. Action space is all photos on Flickr. Photos with < 5 comments are ignored. • Gowalla: location sharing network. Action is checking in to a location. Action space is number of available locations. • Weibo: Chinese site similar to Twitter. Action is whether a user posts on a specific topic (indicated w/ hashtags). Authors examine the 10 most popular topics of 2012. • Co-Author: a network of authors. Action is whether an author will publish a paper in a specific category.

  14. Experiment • Test prediction accuracy and scalability performance. • Test machine: 1.87 GHz Intel Xeon 16 core 64 bit CPU w/ 192GB of RAM running Windows Server 2008. • Algorithm converges after 100 iterations, on average.

  15. Conformity is easiest to predict w/ Co-Author. Why?

  16. A Simple Case Study User A joins 3 groups. He adds a Comment to pictures 1 and 2 on 3/10/2012. The model suggests that User A has a strong conformity To user B and Group 2. Looking at the users/groups Qualitatively, the authors find that Users in Group 1 are loosely Connected while users in Group 2 Know each other. Conformity is likely.

  17. Conclusions • The authors define 3 types of conformity to measure different levels of conformity. • Propose a Confluence Model to model user behavior. • This model significantly outperforms alternative methods, none of which consider the impact of group conformity. • Future: make a connection between conformity and other social theories such as social status. • Use a Game Theory model instead of a Confluence model?

  18. Questions?

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