论文标题
从您的推文中发现您的社会身份：基于内容的方法
Discover Your Social Identity from What You Tweet: a Content Based Approach
论文作者
论文摘要
身份表示个人或一个群体在高度差异化的当代社会中所扮演的角色。在本文中，我们的目标是根据其角色身份对Twitter用户进行分类。我们首先自动收集一个粗粒的公共人物数据集，然后手动标记更细粒度的身份数据集。我们提出了一个用于Twitter用户角色身份分类的分层自我发项神经网络。我们的实验表明，所提出的模型显着胜过多个基线。我们进一步提出了一种转移学习计划，该方案通过很大的利润来提高模型的性能。这种转移学习还大大减少了对大量人类标记数据的需求。
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.
