Liuqing Li, Ziqian Song, Edward Fox

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.

People

Ziqian Song


Edward Fox


Liuqing Li


Publication Details

Date of publication:
November 26, 2018
Journal:
Cornell University
Publication note:

Liuqing Li, Ziqian Song, Xuan Zhang, Edward A. Fox: A Hybrid Model for Role-related User Classification on Twitter. CoRR abs/1811.10202 (2018)