Deep learning approach for author verification problem on twitter
Murat Yilmaz, Begüm Mutlu, Anıl Utku, Muhammet Ali Akcayol · 2018
In social media, there are a large number of parody accounts belong to well known people. It is highly important to distinguish a sharing posted by these kind of accounts. In literature, this problem, referred to as author verification, has been investigated by basing traditional machine learning methods. However, the behavior of deep learning methods on social media sharing, which is composed of limited text length, has not been studied to solve this problem. In this study, a deep learning approach based on multilayer perceptron has been presented for author verification problem on Twitter data. As a result of experiments, high classification success (accuracy: 92.083%, precision: 77.894%, recall: 88.58%, ROC-AUC: 95.95%) has been obtained.