  Social networks play a fundamental role in propagation of information and
news. Characterizing the content of the messages becomes vital for different
tasks, like breaking news detection, personalized message recommendation, fake
users detection, information flow characterization and others. However, Twitter
posts are short and often less coherent than other text documents, which makes
it challenging to apply text mining algorithms to these datasets efficiently.
Tweet-pooling (aggregating tweets into longer documents) has been shown to
improve automatic topic decomposition, but the performance achieved in this
task varies depending on the pooling method.
  In this paper, we propose a new pooling scheme for topic modeling in Twitter,
which groups tweets whose authors belong to the same community (group of users
who mainly interact with each other but not with other groups) on a user
interaction graph. We present a complete evaluation of this methodology, state
of the art schemes and previous pooling models in terms of the cluster quality,
document retrieval tasks performance and supervised machine learning
classification score. Results show that our Community polling method
outperformed other methods on the majority of metrics in two heterogeneous
datasets, while also reducing the running time. This is useful when dealing
with big amounts of noisy and short user-generated social media texts. Overall,
our findings contribute to an improved methodology for identifying the latent
topics in a Twitter dataset, without the need of modifying the basic machinery
of a topic decomposition model.
