Standard
Toxic Messages Classification in Social Media. / Dolgushin, Mikhail; Бидуля, Юлия.
Science and Global Challenges of the 21st Century - Science and Technology - Proceedings of the International Perm Forum “Science and Global Challenges of the 21st Century”: Proceedings of the International Perm Forum “Science and Global Challenges of the 21st Century”. ed. / Alvaro Rocha; Ekaterina Isaeva. Springer. ed. Cham : Springer Nature, 2022. p. 60-65 (Lecture Notes in Networks and Systems; Vol. 342 LNNS).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Harvard
Dolgushin, M & Бидуля, Ю 2022,
Toxic Messages Classification in Social Media. in A Rocha & E Isaeva (eds),
Science and Global Challenges of the 21st Century - Science and Technology - Proceedings of the International Perm Forum “Science and Global Challenges of the 21st Century”: Proceedings of the International Perm Forum “Science and Global Challenges of the 21st Century”. Springer edn, Lecture Notes in Networks and Systems, vol. 342 LNNS, Springer Nature, Cham, pp. 60-65, International Perm Forum on Science and Global Challenges of the 21st Century, 2021, Perm, Russian Federation,
18/10/21.
https://doi.org/10.1007/978-3-030-89477-1_7
APA
Dolgushin, M., & Бидуля, Ю. (2022).
Toxic Messages Classification in Social Media. In A. Rocha, & E. Isaeva (Eds.),
Science and Global Challenges of the 21st Century - Science and Technology - Proceedings of the International Perm Forum “Science and Global Challenges of the 21st Century”: Proceedings of the International Perm Forum “Science and Global Challenges of the 21st Century” (Springer ed., pp. 60-65). (Lecture Notes in Networks and Systems; Vol. 342 LNNS). Springer Nature.
https://doi.org/10.1007/978-3-030-89477-1_7
Vancouver
Dolgushin M, Бидуля Ю.
Toxic Messages Classification in Social Media. In Rocha A, Isaeva E, editors, Science and Global Challenges of the 21st Century - Science and Technology - Proceedings of the International Perm Forum “Science and Global Challenges of the 21st Century”: Proceedings of the International Perm Forum “Science and Global Challenges of the 21st Century”. Springer ed. Cham: Springer Nature. 2022. p. 60-65. (Lecture Notes in Networks and Systems).
https://doi.org/10.1007/978-3-030-89477-1_7
Author
Dolgushin, Mikhail ; Бидуля, Юлия. /
Toxic Messages Classification in Social Media. Science and Global Challenges of the 21st Century - Science and Technology - Proceedings of the International Perm Forum “Science and Global Challenges of the 21st Century”: Proceedings of the International Perm Forum “Science and Global Challenges of the 21st Century”. editor / Alvaro Rocha ; Ekaterina Isaeva. Springer. ed. Cham : Springer Nature, 2022. pp. 60-65 (Lecture Notes in Networks and Systems).
BibTeX
@inproceedings{e1555cdb4f5847beba220b91f2b183e5,
title = "Toxic Messages Classification in Social Media",
abstract = "The aim of our work is to develop a software that could detect toxicity in the Russian segment of social media. In this paper, we investigated the problem of toxic detection in messages in Russian language. We implemented a set of features using selected vector models, trained some classifiers on the dataset about fourteen thousand annotated messages and compare results. Experiments were conducted with a calculation of accuracy, precision, and recall values. F1 measure reached the value 0.91, accuracy value is 0.87.",
keywords = "обработка естественного языка, социальные сети, машинное обучение, Natural Language Processing (NLP), Social network analysis, Machine learning, Toxic detection, Feature extraction, Social media analysis",
author = "Mikhail Dolgushin and Юлия Бидуля",
note = "Dolgushin, M., Bidulya, Y. (2022). Toxic Messages Classification in Social Media. In: Rocha, A., Isaeva, E. (eds) Science and Global Challenges of the 21st Century - Science and Technology. Perm Forum 2021. Lecture Notes in Networks and Systems, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-030-89477-1_7; International Perm Forum on Science and Global Challenges of the 21st Century, 2021 ; Conference date: 18-10-2021 Through 23-10-2021",
year = "2022",
doi = "10.1007/978-3-030-89477-1_7",
language = "English",
isbn = "978-3-030-89476-4",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Nature",
pages = "60--65",
editor = "Alvaro Rocha and Ekaterina Isaeva",
booktitle = "Science and Global Challenges of the 21st Century - Science and Technology - Proceedings of the International Perm Forum “Science and Global Challenges of the 21st Century”",
address = "Germany",
edition = "Springer",
}
RIS
TY - GEN
T1 - Toxic Messages Classification in Social Media
AU - Dolgushin, Mikhail
AU - Бидуля, Юлия
N1 - Dolgushin, M., Bidulya, Y. (2022). Toxic Messages Classification in Social Media. In: Rocha, A., Isaeva, E. (eds) Science and Global Challenges of the 21st Century - Science and Technology. Perm Forum 2021. Lecture Notes in Networks and Systems, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-030-89477-1_7
PY - 2022
Y1 - 2022
N2 - The aim of our work is to develop a software that could detect toxicity in the Russian segment of social media. In this paper, we investigated the problem of toxic detection in messages in Russian language. We implemented a set of features using selected vector models, trained some classifiers on the dataset about fourteen thousand annotated messages and compare results. Experiments were conducted with a calculation of accuracy, precision, and recall values. F1 measure reached the value 0.91, accuracy value is 0.87.
AB - The aim of our work is to develop a software that could detect toxicity in the Russian segment of social media. In this paper, we investigated the problem of toxic detection in messages in Russian language. We implemented a set of features using selected vector models, trained some classifiers on the dataset about fourteen thousand annotated messages and compare results. Experiments were conducted with a calculation of accuracy, precision, and recall values. F1 measure reached the value 0.91, accuracy value is 0.87.
KW - обработка естественного языка
KW - социальные сети
KW - машинное обучение
KW - Natural Language Processing (NLP)
KW - Social network analysis
KW - Machine learning
KW - Toxic detection
KW - Feature extraction
KW - Social media analysis
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85118118626&partnerID=MN8TOARS
UR - http://www.scopus.com/inward/record.url?scp=85118118626&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/f1299183-627f-31f6-8b11-8cda023272c7/
U2 - 10.1007/978-3-030-89477-1_7
DO - 10.1007/978-3-030-89477-1_7
M3 - Conference contribution
SN - 978-3-030-89476-4
T3 - Lecture Notes in Networks and Systems
SP - 60
EP - 65
BT - Science and Global Challenges of the 21st Century - Science and Technology - Proceedings of the International Perm Forum “Science and Global Challenges of the 21st Century”
A2 - Rocha, Alvaro
A2 - Isaeva, Ekaterina
PB - Springer Nature
CY - Cham
T2 - International Perm Forum on Science and Global Challenges of the 21st Century, 2021
Y2 - 18 October 2021 through 23 October 2021
ER -
