Using Trust and Provenance for Content Filtering on the Semantic Web   By Jen Golbeck & Aaron Mannes Maryland Information ...
What are social networks <ul><li>Connections between people </li></ul><ul><li>Can be  </li></ul><ul><ul><li>Explicit (peop...
Web-Based Social Networks (WBSNs) <ul><li>Websites and interfaces that let people maintain browsable lists of friends </li...
Trust in WBSNs <ul><li>People annotate their relationships with information about how much they trust their friends </li><...
Inferring Trust The Goal: Select two individuals - the  source  (node A) and  sink  (node C) - and recommend to the source...
Trust Algorithm <ul><li>If the source does not know the sink, the source asks all of its friends how much to trust the sin...
Film Trust <ul><li>Working example of this can be found at - FilmTrust available at  http://trust. mindswap .org/ FilmTrus...
Applications of Trust <ul><li>With direct knowledge or a recommendation about how much to trust people, this value can be ...
Trust Networks & Intelligence <ul><li>Intelligence agencies no longer face hierarchies, now they face networks </li></ul><...
Use Case Scenarios <ul><li>Help individual analyst sort through mass of material by identifying reliable sources </li></ul...
Uses for Meta-Data <ul><li>Analyzing patterns of trust ratings could help break organizational barriers </li></ul><ul><li>...
References <ul><li>Papers and software available at  http://trust.mindswap.org </li></ul><ul><li>FilmTrust available at  h...
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presentation29

  1. 1. Using Trust and Provenance for Content Filtering on the Semantic Web By Jen Golbeck & Aaron Mannes Maryland Information Network Dynamic Lab University of Maryland, College Park
  2. 2. What are social networks <ul><li>Connections between people </li></ul><ul><li>Can be </li></ul><ul><ul><li>Explicit (people say who they know) </li></ul></ul><ul><ul><li>Derived (e.g. from email archives) </li></ul></ul><ul><ul><li>Simulated </li></ul></ul>
  3. 3. Web-Based Social Networks (WBSNs) <ul><li>Websites and interfaces that let people maintain browsable lists of friends </li></ul><ul><li>Last count </li></ul><ul><ul><li>142 social networking websites </li></ul></ul><ul><ul><li>Over 200,000,000 accounts </li></ul></ul><ul><ul><li>Full list at http://trust.mindswap.org </li></ul></ul><ul><li>Over 10,000,000 accounts are represented in FOAF, an OWL ontology </li></ul>
  4. 4. Trust in WBSNs <ul><li>People annotate their relationships with information about how much they trust their friends </li></ul><ul><li>Trust can be binary (trust or don’t trust) or on some scale </li></ul><ul><ul><li>This work uses a 1-10 scale where 1 is low trust and 10 is high trust </li></ul></ul><ul><li>At least 8 social networks have some mechanism for expressing trust </li></ul>
  5. 5. Inferring Trust The Goal: Select two individuals - the source (node A) and sink (node C) - and recommend to the source how much to trust the sink. A B C t AB t BC t AC
  6. 6. Trust Algorithm <ul><li>If the source does not know the sink, the source asks all of its friends how much to trust the sink, and computes a trust value by a weighted average </li></ul><ul><li>Neighbors repeat the process if they do not have a direct rating for the sink </li></ul>
  7. 7. Film Trust <ul><li>Working example of this can be found at - FilmTrust available at http://trust. mindswap .org/ FilmTrust </li></ul><ul><li>A movie recommendation site backed by a social network that uses trust values to generate predictive recommendations and sort reviews </li></ul>
  8. 8. Applications of Trust <ul><li>With direct knowledge or a recommendation about how much to trust people, this value can be used as a filter in many applications </li></ul><ul><li>Since social networks are so prominent on the web, it is a public, accessible data source for determining the quality of annotations and information </li></ul>
  9. 9. Trust Networks & Intelligence <ul><li>Intelligence agencies no longer face hierarchies, now they face networks </li></ul><ul><li>Several major intelligence failures due to lack of information-sharing or adequately questioning dominant assumptions </li></ul><ul><li>Sheer size of intelligence communities are often a barrier to information sharing </li></ul><ul><li>Trust networks could help intelligence agencies connect the dots </li></ul>
  10. 10. Use Case Scenarios <ul><li>Help individual analyst sort through mass of material by identifying reliable sources </li></ul><ul><li>Trust ratings would allow analysts to check veracity of information by seeing how sources are rated by other trusted analysts </li></ul><ul><li>Importance of outliers for red-teaming - a team comes to strong conclusions on an issue: policy-makers could use trust ratings to check with dissenters </li></ul>
  11. 11. Uses for Meta-Data <ul><li>Analyzing patterns of trust ratings could help break organizational barriers </li></ul><ul><li>While outliers are useful on a case by case basis they could also indicate an organizational dysfunction </li></ul><ul><li>A pattern of low trust ratings between units could indicate a conflict or lack of understanding </li></ul><ul><li>Alternately a pattern of particularly high ratings could indicate group think </li></ul>
  12. 12. References <ul><li>Papers and software available at http://trust.mindswap.org </li></ul><ul><li>FilmTrust available at http://trust.mindswap.org/ FilmTrust </li></ul><ul><li>Terrorism Analysis available at http://profilesinterror.mindswap.org/ </li></ul><ul><li>[email_address] </li></ul><ul><li>[email_address] </li></ul>

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