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Social-Media Users can be profiled by their Similarity with other Users

In this paper we describe the system we designed for participating at CLEF-PAN 2017. In this work, we addressed the Author Profiling (AP) task by exploiting the corpus as a knowledge base. The core idea is that profiles can be identified by exposing its relationship with other users. This strategy produces enriched representations, where fine grained user-document relationships are highlighted. For this, we use a non-sparse user-document representation, which captures distributional information of word-usage among user-documents. We compare the proposed approach with the Second Order Attributes, which have been a key component in the winning approaches of the previous PAN-AP editions. We also report experimental results with the traditional Bag-of-Terms and AvgWord2Vec representations. The experimental evaluation on the PAN17 corpora shows that the proposal outperforms all other methodologies, showing strong evidence of the usefulness of the representation to determine language variety and gender profiles. Furthermore, this representation can be seen as a natural extension to Second Order Attributes, which could be combined in future works in order to expose finer details about user relationships.