Network-Based Pooling for Topic Modeling on Microblog Content

Topic modeling with tweets is difficult due to the short and informal nature of the texts. Tweet-pooling (aggregation of tweets into longer documents prior to training) has been shown to improve model outputs, but performance varies depending on the pooling scheme and data set used. Here we investigate a new tweet-pooling method based on network structures associated with Twitter content. Using a standard formulation of the well-known Latent Dirichlet Allocation (LDA) topic model, we trained various models using different tweet-pooling schemes on three diverse Twitter datasets. Tweet-pooling schemes were created based on mention/reply relationships between tweets and Twitter users, with several (non-networked) established methods also tested as a comparison. Results show that pooling tweets using network information gives better topic coherence and clustering performance than other pooling schemes, on the majority of datasets tested. Our findings contribute to an improved methodology for topic modeling with Twitter content.