Abstract
An identity denotes the role an individual or a group plays in highly differentiated contemporary societies. In this paper, our goal is to classify Twitter users based on their role identities. We first collect a coarse-grained public figure dataset automatically, then manually label a more fine-grained identity dataset. We propose a hierarchical self-attention neural network for Twitter user role identity classification. Our experiments demonstrate that the proposed model significantly outperforms multiple baselines. We further propose a transfer learning scheme that improves our model’s performance by a large margin. Such transfer learning also greatly reduces the need for a large amount of human labeled data.
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Huang, B., Carley, K.M. (2020). Discover Your Social Identity from What You Tweet: A Content Based Approach. In: Shu, K., Wang, S., Lee, D., Liu, H. (eds) Disinformation, Misinformation, and Fake News in Social Media. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-42699-6_2
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