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Trust and Influence in the Complex Network of Social Media

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William Rand, University of Maryland, presents at the 2012 Big Analytics Roadshow.

The dramatic feature of social media is that it gives everyone a voice; anyone can speak out and express their opinion to a crowd of followers with little or no cost or effort, which creates a loud and potentially overwhelming marketplace of ideas. The good news is that the organizations have more data than ever about what their consumers are saying about their brand. The bad news is that this huge amount of data is difficult to sift through. We will look at developing methods that can help sift through this torrent of data and examine important questions, such as who do users trust to provide them with the information and the recommendations that they want? Which tastemakers have the greatest influence on social media users? Using agent-based modeling, machine learning and network analysis we begin to examine and shed light on these questions and develop a deeper understanding of the complex system of social media.

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Trust and Influence in the Complex Network of Social Media

  1. 1. TRUST AND INFLUENCE in the Complex Network of Social MediaBill RandDirector, Center for Complexity in BusinessAsst. Professor of Marketing and Computer ScienceRobert H. Smith School of BusinessUniversity of Maryland
  2. 2. Connecting the CMO to the CIO... • Organizations have more data than ever before... • Computational power and storage is cheaper than ever before... • This enables analytics that can be used, for example, to: 1. Gain new customers / stop old customers from churning 2. Find out additional information to increase share of customer 3. Analyze word-of-mouth and ROI for media events
  3. 3. Social Media Analytics
  4. 4. Teasers • Who are the most influential individuals in social media? • It may not just be those who are the most popular... • How is trust earned in social media? • We can design new social network mechanisms that increase trust in social networks....
  5. 5. Influencejoint work with Forrest Stonedahl and Uri WilenskySupported by NSF Award IIS-0713619
  6. 6. Who are the most influentialindividuals in social networks?•How does network structure affectinfluence?•What is the value of an individual in anetwork?•If we can simulate a diffusion process at themicro-level then we can answer thesequestions.
  7. 7. Who should you seed?•Which individuals will allow you to reach the widestaudience as soon as possible?•Standard Rule-of-Thumb is to seed those with thehighest number of connections•Alternative Strategies •Seed the people whose friends do not talk to each other, spread the message widely (low clustering coefficient) •Seed the people who are the closest to everyone else in the network, centralize your message (low average path length)
  8. 8. How many to Seed?•Seeding more people means themessage spreads quicker, but•Seeding more people costs more, and•At a certain point you start seedingpeople who would have adopted anywaybecause of their friends•So how many people should we seed?
  9. 9. Best Primary Strategies
  10. 10. Optimal Twitter Seeds
  11. 11. Influence• People with lots of friends know other people with lots of friends which constrains social contagion.• The most influential people have lots of friends but their friends don’t know each other.• But this assumes that all individuals trust each other equally, what happens when trust varies over a network?
  12. 12. Trustjoint work with Hossam Sharara and Lise GetoorSupported by NSF Award IIS-0746930 and IIS-1018361
  13. 13. Motivation Ann Janet JohnBob and Mary will definitely be interested. However, I think Mary Ann is not WOW… I’ll interested in send it over movies to everyone MovieRental.com Bob Book Store (Refer a friend and get (Invite a friend and get 10% $10 off your next rental) off your next purchase)
  14. 14. Dataset Social Network (user-user following links) • 11,942 users • 1.3M follow edges Digg Network (user-story digging links) • 48,554 news stories • 1.9M digg edges • 6 months (Jul 2010 – Dec 2010)
  15. 15. The Model• Our model takes two factors in to account: 1. People have different preferences for different product categories 2. Trust between individuals in recommendations changes in time• We can then use this model to predict who is likely to accept recommendations in the future.
  16. 16. Results The Adaptive model, taking both the diffusion dynamics and the users heterogeneity into account, yields better performance
  17. 17. A New Viral MarketingMarketing Mechanism:Adaptive Rewards Successful recommendations are awarded (α x r) units, while failed ones are penalized ((1-α) x r) units • α conservation parameter Most existing viral marketing strategies assume α=1  (no reason for the user to be selective) The penalty term helps maintain the average overall confidence level between different peers
  18. 18. Experimental Results• Allowing agents to learn the preferences accounts for both the product preference as well as the confidence level
  19. 19. Trust• We can make better predictions about adoption if we take in to account heterogeneous preferences and dynamic trust.• We can create better mechanisms that encourage more trust within social networks.
  20. 20. Any Questions?wrand@umd.eduwww.rhsmith.umd.edu/ccb/bit.ly/ccbssrnDigital Marketing Analytics Roundtable on June 21stMS in Marketing Analytics