Learning Invariant Representations of Social Media Users
Table 4: Clustering performance on Reddit episodes using embeddings obtained with different methods.
['[EMPTY]', '[BOLD] NMI', '[BOLD] H', '[BOLD] C']
Procedure. Using the pre-trained Reddit IUR model, we embed five episodes of length 16 by 5000 users selected uniformly at random, all drawn from the held-out 2016-09 split. The embeddings are clustered using affinity propagation, hiding both the identities of the users as well as the true number of users from the algorithm Frey and Dueck Ideally the algorithm will arrive at 5000 clusters, each containing exactly five episodes by same author. Clustering performance is evaluated using mutual information (NMI), homogeneity (H), and completeness (C) Rosenberg and Hirschberg NMI involves a ratio of the mutual information of the clustering and ground truth. Homogeneity is a measure of cluster purity. Completeness measures the extent to which data points by the same author are elements of the same cluster. All three measures lie in interval [0,1] where 1 is best.