用跨语言文本特征预测推特用户性别，发现男女写作风格本质不同
A Deep Learning Approach to Language-independent Gender Prediction on Twitter
- 基于多语言推文提取无语言依赖特征，用逻辑回归与神经网络建模
- 数据量大时神经网络显著优于传统模型，小数据下则相反
- 证实性别差异超越语言，写作风格可作为独立预测依据
本研究通过在六种语言（葡萄牙语、法语、荷兰语、英语、德语、意大利语）的TwiSty数据集上进行实验，基于用户推文中的语言无关特征预测其性别。采用逻辑回归（LR）和带反向传播的前馈神经网络（FFNN）在两种设置下建模：跨语言（CL）与同语言（IL）。IL设置中，训练与测试在同一语言内进行；CL设置中，意大利语与德语仅作测试集，其余语言合并用于训练与开发。IL场景下，逻辑回归表现最佳；而CL场景中，含三层隐藏层的FFNN取得最高准确率。结果表明，当训练数据量较小时，神经网络性能不如传统模型；但数据充足时，神经网络显著超越传统方法。特征分析进一步证实，男女写作风格差异独立于语言存在。
原文摘要 · Abstract (English)
This work presents a set of experiments conducted to predict the gender of Twitter users based on language-independent features extracted from the text of the users' tweets. The experiments were performed on a version of TwiSty dataset including tweets written by the users of six different languages: Portuguese, French, Dutch, English, German, and Italian. Logistic regression (LR), and feed-forward neural networks (FFNN) with back-propagation were used to build models in two different settings: Inter-Lingual (IL) and Cross-Lingual (CL). In the IL setting, the training and testing were performed on the same language whereas in the CL, Italian and German datasets were set aside and only used as test sets and the rest were combined to compose training and development sets. In the IL, the highest accuracy score belongs to LR whereas in the CL, FFNN with three hidden layers yields the highest score. The results show that neural network based models underperform traditional models when the size of the training set is small; however, they beat traditional models by a non-trivial margin, when they are fed with large enough data. Finally, the feature analysis confirms that men and women have different writing styles independent of their language.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。