Age Prediction of Spanish-speaking Twitter Users

Age prediction in Twitter is an interesting but challenging task, that arises as a way to improving online marketing and potentially helping with the detection of cyber-pedophiles who pretend to be younger users by using fake profiles. In this work, we focus the analysis on Twitter users writing in Spanish. As any author profiling task, age prediction greatly depends on the language used by the target group. In the case of Spanish, one of the biggest difficulties is the lack of a labeled corpus. Hence, we explore strategies to generate it and, as a result, we develop TweetLab, a software pipeline to extract and label Twitter and customize it for users in Spanish from Uruguay and part of Argentina. Another identified problem is the short nature of the tweets. Therefore, it is necessary to gather as many information as possible from them, even by inferring hidden relations or calculating lexical metrics. In order to do that, we study three types of features: user metadata, stylometric features from tweets text and Natural Language Processing features extracted from tweets as well as subscription lists, which contain information about the user’s interests. We also present a novel set of features that model the presence of other social networks profiles linked to the Twitter account. Those extracted features are used to build models which are used as input of Machine Learning algorithms, in order to predict the age of the users and classify them into the age groups defined. We run several experiments with different datasets and algorithms. The experimental results show that these features work well in detection of users age.