Determining user similarity in healthcare social media using content similarity and structural similarity

Abstract

More and more health consumers discuss healthcare topics with peers in online health social websites. These health social websites empower consumers to actively participate in their own healthcare and promotes communication between people. However, it is difficult for consumers to find information efficiently from hundreds of thousands of discussion threads. Finding similar users for consumers enables them to see what their peers are doing or experiencing thus enables automated selection of “relevant” information. In this work, we proposed two different methods for computing user similarity in healthcare social media using content and structural information respectively. Experiment results showed that the method using structural information from a heterogeneous healthcare information network performed better than content similarity in finding active similar users. However, when the users are not as active or contributing relatively fewer messages in social media, content similarity performed better in identifying these users.