Directed social queries with transparent user models

The friend list of many social network users can be very large. This creates challenges when users seek to direct their social interactions to friends that share a particular interest. We present a self-organizing online tool that by incorporating ideas from user modeling and data visualization allows a person to quickly identify which friends best match a social query, enabling precise and efficient directed social interactions. To cover the different modalities in which our tool might be used, we introduce two different interactive visualizations. One view enables a human-in-the-loop approach for result analysis and verification, and, in a second view, location, social affiliations and "personality" data is incorporated, allowing the user to quickly consider different social and spatial factors when directing social queries. We report on a qualitative analysis, which indicates that transparency leads to an increased effectiveness of the system. This work contributes a novel method for exploring online friends.