Quality biased thread retrieval using the voting model

Abstract

Thread retrieval is an essential tool in knowledge-based forums. However, forum content quality varies from excellent to mediocre and spam; thus, search methods should find not only relevant threads but also those with high quality content. Some studies have shown that leveraging quality indicators improves thread search. However, these studies ignored the hierarchical and the conversational structures of threads in estimating topical relevance and content quality. In that regard, this paper introduces leveraging message quality indicators in ranking threads. To achieve this, we first use the Voting Model to convert message level quality features into thread level features. We then train a learning to rank method to combine these thread level features. Preliminary results with some features reveal that representing threads as collections of messages is superior to treating them as concatenations of their messages. The results show also the utility of leveraging message content quality as compared to non quality-based methods.