Entity grouping for accessing social streams via word clouds

Word clouds have been proven as an effective tool for information access in different domains. As social media is a main driver of large increase in available user generated content, means for accessing information in such content are needed. We study word clouds as a means for information access in social media. Currently-used clouds that are generated from social media data include redundant and misranked entries, harming their utility.We propose a method for generating improved word clouds over social streams. In this method, named entities are detected, disambiguated and aggregated into clusters, which in turn inform cloud construction. We show that word clouds using named entity clusters attain broader coverage and decreased content duplication. Further, an extrinsic evaluation shows improved access to data, with word clouds having grouped named entities being rated more relevant and diverse. Additionally we find word clouds with higher Mean Average Precision (MAP) tend to be more relevant to underlying concepts. Critically, this supports MAP as a tool for predicting cloud quality without needing a human.