Age Inference Using A Hierarchical Attention Neural Network

While demographic attributes, such as age, gender, and location, have been extensively studied, most previous studies usually combine different sources of data, such as the user's biography, pictures, posts, and the user's network to obtain reasonable inference accuracies. However, it is not always practical to collect all those different forms of data. Therefore, in this paper, we consider methods for inferring age that only use Twitter posts (tweet text and emojis). We propose a hierarchical attention neural model that integrates independent linguistic knowledge gained from text and emojis when making a prediction. This hierarchical model is able to capture the intra-post relationship between these different post components, as well as the inter-post relationships of a user's posts. Our empirical evaluation using a data set generated from Wikidata demonstrates that our model achieves better performance than the state-of-the-art models, and still performs well when the number of posts per user is reduced in the training data set.