  Query expansion is the process of reformulating the original query by adding
relevant words. Choosing which terms to add in order to improve the performance
of the query expansion methods or to enhance the quality of the retrieved
results is an important aspect of any information retrieval system. Adding
words that can positively impact the quality of the search query or are
informative enough play an important role in returning or gathering relevant
documents that cover a certain topic can result in improving the efficiency of
the information retrieval system. Typically, query expansion techniques are
used to add or substitute words to a given search query to collect relevant
data. In this paper, we design and implement a pipeline of automated query
expansion. We outline several tools using different methods to expand the
query. Our methods depend on targeting emergent events in streaming data over
time and finding the hidden topics from targeted documents using probabilistic
topic models. We employ Dynamic Eigenvector Centrality to trigger the emergent
events, and the Latent Dirichlet Allocation to discover the topics. Also, we
use an external data source as a secondary stream to supplement the primary
stream with relevant words and expand the query using the words from both
primary and secondary streams. An experimental study is performed on Twitter
data (primary stream) related to the events that happened during protests in
Baltimore in 2015. The quality of the retrieved results was measured using a
quality indicator of the streaming data: tweets count, hashtag count, and
hashtag clustering.
