Authorship verification of opinion articles in online newspapers using the idiolect of author: a comparative study
Mahmoud A. Al‐Khatib, Juman K. Al-qaoud · Information Communication & Society · 2020
Many approaches have been introduced to solve the authorship verification problem, including the use of machine learning techniques. These techniques proved to be effective in detecting a person’s distinctive way of speaking or writing. The main aim of this study was to show that every writer has an idiolect which is presented through the use of several types of stylometric features unique to individual authors. For this purpose, 120 online opinion articles written by non-native speakers of English were chosen from four newspapers published in the Arab world, while 145 articles written by native speakers of English were taken from other four newspapers. All of these articles were classified and compared using the SMO and MLP algorithms via a tool called ‘JStylo’. The proposed framework achieved a competitive performance with an accuracy of 80% using the SMO classifier. The results of the study indicate that each author has an individual style of writing (idiolect), and that idiolect is not shown by one group of writers better than the other, namely the native and non-native authors.