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

Outline Introduction Behavior Trust Twitter data Experiment Results Conclusion

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

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

Evaluate Trust in Social Network Whether we trust others? Our own predisposition to trust. Relationship with others. Our opinions towards others. Tip: Add your own speaker notes here.

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.

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., 𝐴, 𝐵. Edges’ weights: strength of trust, e.g., 𝑇(𝐴,𝐵).

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

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

Conversations grouping The set of messages exchanged between A and B is: . Average time between messages is: Rule: two consecutive messages 𝑡 𝑖 , 𝑡 𝑖+1 are in the same conversation if 𝑡 𝑖+1 − 𝑡 𝑖 <S∙𝜏. 𝑡 5 −𝑡 4 > S∙𝜏 𝑡 3 −𝑡 2 < S∙𝜏 𝑡 1 𝑡 2 𝑡 3 𝑡 4 𝑡 5 𝑡 6 𝑡 7 𝑡 4 −𝑡 3 < S∙𝜏

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: 𝑝 : the fraction sent by one actor; 1−𝑝: the fraction sent by the other actor.

Propagation Trust 𝐴 details ? Given communication statistics alone, we cannot definitely determine which messages from B are propagations from A. So we turn to counting “potential propagations”. Tip: Add your own speaker notes here.

Potential Propagations Counting Potential Propagations must satisfy the following constraint: Matching “incoming to B” messages with “outgoing from B” messages: 𝑠 1 − 𝑡 1 < 𝜏 𝑚𝑖𝑛 𝜏 𝑚𝑖𝑛 < 𝑠 2 − 𝑡 1 < 𝜏 𝑚𝑎𝑥 𝜏 𝑚𝑖𝑛 <𝑠 3 − 𝑡 3 < 𝜏 𝑚𝑎𝑥 𝑠 3 − 𝑡 2 > 𝜏 𝑚𝑎𝑥 No cross

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

Twitter Data Data Volume: Data format: 2M users (1.9M senders). 230K tweets per day. Data format: (sender, receiver, time). Ground Truth Label of Trust: retweeting Directed Broadcast Tip: Add your own speaker notes here.

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

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

Computing Conversation & Propagation Graphs (continued) To achieve comparison between conversation and propagation graphs: treat the undirected edge as two directed ones. Tip: Add your own speaker notes here.

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.

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: 𝐵→𝐴. Directed retweet is more determinative than broadcast retweet in indicating trust.

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

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

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.

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.

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