Abstract
In this paper, we investigate the importance of
social network information compared to content information in the prediction of a Twitter
user’s occupational class. We show that the
content information of a user’s tweets, the pro-
file descriptions of a user’s follower/following
community, and the user’s social network provide useful information for classifying a user’s
occupational group. In our study, we extend an existing dataset for this problem, and
we achieve significantly better performance by
using social network homophily that has not
been fully exploited in previous work. In our
analysis, we found that by using the graph
convolutional network to exploit social homophily, we can achieve competitive performance on this dataset with just a small fraction
of the training data.