Discover Your Social Identity from What You Tweet: a Content Based Approach

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.

This is a preprint of a chapter published in Disinformation, Misinformation, and Fake News in Social Media: Emerging Research Challenges and Opportunities, edited by Kai, S., Suhang, W., Dongwon, L., Huan, L, 2020, Springer reproduced with permission of Springer Nature Switzerland AG. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-42699-6

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