Title: A Hybrid Model for Role-related User Classification on Twitter,
Abstract: To aid a variety of research studies, we propose TWIROLE, a hybrid model for
role-related user classification on Twitter, which detects male-related,
female-related, and brand-related (i.e., organization or institution) users.
TWIROLE leverages features from tweet contents, user profiles, and profile
images, and then applies our hybrid model to identify a user's role. To
evaluate it, we used two existing large datasets about Twitter users, and
conducted both intra- and inter-comparison experiments. TWIROLE outperforms
existing methods and obtains more balanced results over the several roles. We
also confirm that user names and profile images are good indicators for this
task. Our research extends prior work that does not consider brand-related
users, and is an aid to future evaluation efforts relative to investigations
that rely upon self-labeled datasets.