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Using Trust and Provenance for Content Filtering on the

Semantic Web

By Jen Golbeck & Aaron MannesMaryland Information Network Dynamic Lab

University of Maryland, College Park

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What are social networks

• Connections between people• Can be

– Explicit (people say who they know)– Derived (e.g. from email archives)– Simulated

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Web-Based Social Networks (WBSNs)

• Websites and interfaces that let people maintain browsable lists of friends

• Last count– 142 social networking websites– Over 200,000,000 accounts– Full list at http://trust.mindswap.org

• Over 10,000,000 accounts are represented in FOAF, an OWL ontology

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Trust in WBSNs

• People annotate their relationships with information about how much they trust their friends

• Trust can be binary (trust or don’t trust) or on some scale– This work uses a 1-10 scale where 1 is low

trust and 10 is high trust

• At least 8 social networks have some mechanism for expressing trust

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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 CtAB tBC

tAC

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Trust Algorithm

• 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

• Neighbors repeat the process if they do not have a direct rating for the sink

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Film Trust

• Working example of this can be found at - FilmTrust available at http://trust.mindswap.org/FilmTrust

• A movie recommendation site backed by a social network that uses trust values to generate predictive recommendations and sort reviews

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Applications of Trust

• With direct knowledge or a recommendation about how much to trust people, this value can be used as a filter in many applications

• Since social networks are so prominent on the web, it is a public, accessible data source for determining the quality of annotations and information

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Trust Networks & Intelligence

• Intelligence agencies no longer face hierarchies, now they face networks

• Several major intelligence failures due to lack of information-sharing or adequately questioning dominant assumptions

• Sheer size of intelligence communities are often a barrier to information sharing

• Trust networks could help intelligence agencies connect the dots

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Use Case Scenarios

• Help individual analyst sort through mass of material by identifying reliable sources

• Trust ratings would allow analysts to check veracity of information by seeing how sources are rated by other trusted analysts

• 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

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Uses for Meta-Data

• Analyzing patterns of trust ratings could help break organizational barriers

• While outliers are useful on a case by case basis they could also indicate an organizational dysfunction

• A pattern of low trust ratings between units could indicate a conflict or lack of understanding

• Alternately a pattern of particularly high ratings could indicate group think

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References• Papers and software available at

http://trust.mindswap.org

• FilmTrust available at http://trust.mindswap.org/FilmTrust

• Terrorism Analysis available at http://profilesinterror.mindswap.org/

• golbeck@cs.umd.edu• awmannes@comcast.net