Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Online Social Network Post Classification : A Multiclass approach. / Oliseenko, Valerii D.; Tulupyeva, Tatiana V.; Abramov, Maxim V.
Proceedings of the 5th International Scientific Conference “Intelligent Information Technologies for Industry”, IITI 2021. ed. / Sergey Kovalev; Valery Tarassov; Vaclav Snasel; Andrey Sukhanov. Springer Nature, 2022. p. 207-215 (Lecture Notes in Networks and Systems; Vol. 330 LNNS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
}
TY - GEN
T1 - Online Social Network Post Classification
T2 - 5th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2021
AU - Oliseenko, Valerii D.
AU - Tulupyeva, Tatiana V.
AU - Abramov, Maxim V.
N1 - Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - This paper presents the results of automating the process of social network posts’ two-level hierarchical (ensemble) classification. The research aims to build a second-level model that allows to classify three main classes into subclasses using methods of multi-class classification (SVM, naive Bayesian classifier, random forest and multilayer perceptron). This work’s theoretical and practical significance is determined by the fact that the resulting model will partially automate the process of assessing the severity of users’ psychological characteristics on their text posts in social networks, as well as create the potential to refine the estimates of the protection of users from social engineering attacks, and the development of recommendation systems offering measures to improve users’ protection. The novelty of the work comes from the complementing of the previously developed two-level classification of social network posts with a second-level model based on known multiclass classification’s methods.
AB - This paper presents the results of automating the process of social network posts’ two-level hierarchical (ensemble) classification. The research aims to build a second-level model that allows to classify three main classes into subclasses using methods of multi-class classification (SVM, naive Bayesian classifier, random forest and multilayer perceptron). This work’s theoretical and practical significance is determined by the fact that the resulting model will partially automate the process of assessing the severity of users’ psychological characteristics on their text posts in social networks, as well as create the potential to refine the estimates of the protection of users from social engineering attacks, and the development of recommendation systems offering measures to improve users’ protection. The novelty of the work comes from the complementing of the previously developed two-level classification of social network posts with a second-level model based on known multiclass classification’s methods.
KW - Artificial intelligence
KW - Data science
KW - Information security
KW - Multiclass text classification
KW - Social engineering attacks
KW - Social graph
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=85115840906&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/399a571e-0120-342a-9029-1ade184e43bd/
U2 - 10.1007/978-3-030-87178-9_21
DO - 10.1007/978-3-030-87178-9_21
M3 - Conference contribution
AN - SCOPUS:85115840906
SN - 9783030871772
T3 - Lecture Notes in Networks and Systems
SP - 207
EP - 215
BT - Proceedings of the 5th International Scientific Conference “Intelligent Information Technologies for Industry”, IITI 2021
A2 - Kovalev, Sergey
A2 - Tarassov, Valery
A2 - Snasel, Vaclav
A2 - Sukhanov, Andrey
PB - Springer Nature
Y2 - 30 September 2021 through 4 October 2021
ER -
ID: 86309645