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Visualizing and Verifying Directed Social Queries

We present a novel visualization system that automatically classifies social network data in order to support a user’s directed social queries and, furthermore, that allows the user to quickly verify the accuracy of the classifications. We model a user’s friends’ interests in particular topics through the creation of a crowd-sourced knowledge base comprised of terms related to user-specified semantic categories. Modeling friends in terms of these topics enables precise and efficient social querying to effectively fulfill a user’s information needs. That is, our system makes it possible to quickly identify friends who have a high probability of being able to answer particular questions or of having a shared interested in particular topics. Our initial investigations indicate that our model is effective at correlating friends to these topics even without sentiment or other lexical analyses. However, even the most robust system may produce results that have false positives or false negatives due to inaccurate classifications stemming from incorrect, polysemous, or sparse data. To mitigate these errors, and to allow for more fine-grained control over the selection of friends for directed social queries, an interactive visualization exposes the results of our model and enables a human-in-the-loop approach for result analysis and verification. A qualitative analysis of our verification system indicates that the transparent representation of our shared-interest modeling algorithm leads to an increased effectiveness of the model.