Using Spectral Clustering of Hashtag Adoptions to Find Interest-Based Communities

We investigate the use of spectral clustering of hashtag adoptions in Nigerian Twitter users between October 013 and November 2014. This period is of interest due to the online campaign centered around the #BringBackOurGirls BBOG) hashtag, which relates to the kidnapping of 276 Nigerian schoolgirls. We examine the adoption of hashtags during the six months before, the month after, and the six months following the kidnapping to test the informational value of behavior-based clusters discovered with unsupervised methods for predicting future hashtag usage behaviors. We demonstrate an efficient spectral clustering approach, that leverages power iteration on symmetric adjacency matrices, to group users based on hashtag adoptions prior to the kidnapping. Unlike follow network-based clusters, these adoption-based clusters reveal groups of users with similar interests and prove to be more predictive of interest in future topics. We compare this unsupervised spectral clustering to spectral clustering based on symmetrized follow network relations as well as clusters induced by latent Dirichlet allocation (LDA) topics. We find that hashtag adoption-based clusters perform similarly to the more computationally expensive LDA approach at identifying interest groups that are more likely to adopt future topical tags. We also benchmark the spectral clustering approach against the popular Louvain clustering approach on a synthetic dataset, finding the faster spectral clustering algorithm produces more balanced clusters with a higher similarity to the true interest groupings used to synthesize adoption data.