User identification based on short text using recurrent deep learning

Huda Hallawi (University of Kerbala)
Huda Ragheb Kadhim (University of Kerbala)
Zahraa Najm Abdullah (University of Kerbala)
Noor D. AL-Shakarchy (University of Kerbala)
Dhamyaa A. Nasrawi (University of Kerbala)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 12, No 4: December 2023

Technological development is a revolutionary process by this time, it ismainly depending on electronic applications in our daily routines like(business management, banking, financial transfers, health, and other essentialtraits of life). Identification or approving identity is one of the complicatedissues within online electronic applications. Person’s writing style can beemployed as an identifying biological characteristic in order to recognize theidentity. This paper presents a new way for identifying a person in a socialmedia group using comments and based on the Deep Neural Network. Thetext samples are short text comments collected from Telegram group in Arabiclanguage (Iraqi dialect). The proposed model is able to extract the person'swriting style features in group comments based on pre-saved dataset. Theanalysis of this information and features forms the identification decision.This model exhibits a range of prolific and favorable results, the accuracy thatcomes with the proposed system reach to 92.88% (+/- 0.16%).