Measuring Behavioral Trust in Social Networks

1. Measuring Behavioral Trust in Social Networks SibelAdali, et al. IEEE International Conference on Intelligence and Security Informatics Presented by: Liang Zhao Northern Virginia Center

2. Outline • Introduction • Behavior Trust • Twitter data • Experiment Results • Conclusion

3. Introduction • Trust vs. Social Network • Evaluate Trust in Social Network • Assumptions • Purpose of this paper

4. Trust vs. Social Network • Trust → Social Network (SN) • Forms coalitions • Identifies influential nodes in SN • Depicts the flow of information • Social Network → Trust • Communities induce greater trust • Information flow enhances trust

5. Evaluate Trust in Social Network • Our own predisposition to trust. • Relationship with others. • Our opinions towards others. Whether we trust others?

6. Assumptions • Does not consider semantic information. • Only consider social ties • Trust is a social tie between a trustor and trustee. • Social ties can be observed by communication behaviors. • Degree of Trust can change. Behavior Trust: Measure of trust is based on social behavior. Social behaviors can conversely enhance or reduce the trust.

7. Purpose of this paper • Measure trust based on the communi-cation behavior of the actors in SN. • Input: • Communication Stream of Social Network: • {<sender, receiver, time>,…,<sender, receiver, time>} • Output: • Behavior trust graph • Nodes: actors in SN, e.g., . • Edges’ weights: strength of trust, e.g., .

8. Behavior Trust • Conversations & Propagations • Conversations behavior based • Conversations grouping • Conversation Trust Computation • Propagation behavior based • Propagation Trust • Potential Propagations Counting • Propagation Trust Computation

9. Conversations & Propagations • This paper considers two kinds of behavior: • Conversations: Two nodes converse means they are more likely to trust each other. • Propagations: Apropagates info from B indicates A trust B. directed undirected

10. Conversations grouping • The set of messages exchanged between A and B is: . • Average time between messages is: • Rule: two consecutive messages , are in the same conversation if .

11. Conversation Trust Computation • Rules: • Longer Conversations imply more trust. • More Conversations imply more trust. • Balanced participation between two actors imply more trust. • Trust (namely Edge’s weight in trust graph): Entropy function: : the fraction sent by one actor; the fraction sent by the other actor.

12. Propagation Trust • Given communication statistics alone, we cannot definitely determine which messages from B are propagations from A. • So we turn to counting “potential propagations”. details ?

13. Potential Propagations Counting • Potential Propagations must satisfy the following constraint: • Matching “incoming to B” messages with “outgoing from B” messages: No cross

14. Propagation Trust Computation • Notations: • the number of propagations by B. • the number of potential propagations. • the number of messages A sent to B. • Strategy 1: • Strategy 2: The fraction of A’s messages worthy to be propagated by B. The fraction of B’s energy spent on propagating A’ messages.

15. Twitter Data • Data Volume: • 2M users (1.9M senders). • 230K tweets per day. • Data format: • (sender, receiver, time). • Ground Truth Label of Trust: retweeting • Directed • Broadcast

16. Experiment • Compute Conversation & Propagation Graphs. • Overlaps between Conversation & Propagation Graphs. • Validate Conversation & Propagation Graphs using retweets.

17. Computing Conversation & Propagation Graphs • Data: • 15M Directed tweets for conversation graph. • 34M broadcast tweets for propagation graph. • Settings:

18. Computing Conversation & Propagation Graphs (continued) • To achieve comparison between conversation and propagation graphs: treat the undirected edge as two directed ones.

19. Overlaps between Conversation & Propagation Graphs • Cluster these two graphs based on the weighted edges to discover communities: • Overlaps evaluation: Random set of clusters with same size distribution; repeat 1000 times.

20. Graph validation using retweets. • Assumption: • A retweet is a propagation. • When a user propagates information from some other user, there must be some element of trust between them. • indicates directed trust: . • Directed retweet is more determinative than broadcast retweet in indicating trust.

21. Graph validation using retweets (contd.) • Conversational Trust Graph Validation: • Nodes: 20% are also presented in retweets graph. • Edges: as follows. : Random graph, which consists of randomly selected nodes. The edges are communications between the nodes. : Prominence graph, which consists of most active nodes. The edges are communications between the nodes.

22. Graph validation using retweets (contd.) • Propagation Trust Graph Validation: • Nodes: 20% are also presented in retweets graph. • Edges: as follows.

23. Conclusion • Method advantages: • Propose a measurable behavior trust metric. • Does not need semantic information. • Can be applied to dynamic network. • The proposed metric reasonably correlate with retweets. • Can be applied to general social networks other than Twitter. • Good scalability due to low computational cost on statistical communication data.

24. Future Works • Verify the potentially casual relationship between conversation and propagation behavior. • The intersection of conversation and propagation graphs would be a more stringent measure of trust. • Improve the purity of trust measurement by considering semantics of messages. • Trust should be dependent on context (e.g., we trust a doctor in medical science, but not necessarily in finance analysis. • Improve the trust measurement by considering the quality and value of messages.

25. Thank you