Cybercrime and Authorship Detection in Very Short Texts A Quantitative Morpho-lexical Approach

Abstract

The present study proposes an integrated framework that considers letterpair frequencies/combinations along with the lexical features of documents. Drawing on a quantitative morpho-lexical approach, the study ‫لسنة‬ ‫العشرون‬ ‫العدد‬ ‫اآلداب‬ ‫فى‬ ‫العلمى‬ ‫البحث‬ ‫مجلة‬ 9102 ‫األول‬ ‫الجزء‬ tests the hypothesis that letter information or mapping carries unique stylistic features; and therefore detecting stable word combinations and morphological patterns can be used to enhance the authorship performance in relation to very short texts. The data used for analysis is a corpus of 12240 tweets derived from 87 Twitter accounts. Self-organizing maps (SOMs) model is used for classifying the input patterns that share common features together as a clue that tweets grouped under one class membership are written by the same author. Results indicate that the classification accuracy based on the proposed system is around 76%. Up to 22% of this accuracy was lost, however, when only distinctive words were used, and 26% was lost when the classification performance was based on letter combinations and morphological patterns only. The integration of letter-pairs and morphological patterns had the advantage of improving the accuracy of determining the author of a given tweet. This indicates that the integration of different linguistic variables into an integrated system leads to a better classification performance of very short texts. It is also clear that the use of the self-organizing map (SOM) led to better clustering performance for its capacity to integrate two different linguistic levels of each author profile together.

An Evaluation of the Automatic Detection of Hate Speech in Social Media Networks

“…In the face of this issue, researchers have sought to develop automatic detection systems and algorithms with the capability of identifying hate speech in content so that such posts can be removed [1,17]. Studies in this tradition are usually multidisciplinary.…”