ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2022-22-4-769-778ntv-222Research ArticleКОМПЬЮТЕРНЫЕ СИСТЕМЫ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИCOMPUTER SCIENCEОблегченная система рекомендаций для анализа социальных сетей с использованием гибридного алгоритма классификатора BERT-SVM (на англ. яз.)Light weight recommendation system for social networking analysis using a hybrid BERT-SVM classifier algorithmhttps://orcid.org/0000-0001-6601-1341КирутикаН. С.KiruthikaN. S.

Кирутика Налличери Субраманиан — научный сотрудник

sc 55420781200

Ченнаи, шт. Тамил Наду, 600117

Nallichery Subramanian Kiruthika — Research Scholar

sc 55420781200

Chennai, Tamil Nadu, 600117

sathishpoojaa5@gmail.comhttps://orcid.org/0000-0002-0043-2415ТайламбалГ.ThailambalG.

Тайламбал Ганапати — доцент

sc 57189250428

Ченнаи, шт. Тамил Наду, 600117

Ganapathy Thailambal — Associate Professor, Department of Computer science, School of Computing Sciences

sc 57189250428

Chennai, Tamil Nadu, 600117

thaila.scs@velsuniv.ac.inУниверситет ВелсаИндияVels UniversityIndiaИнститут науки, технологий и перспективных исследований ВелсаИндияVels Institute of Science, Technology and Advanced Studies (VISTAS)India202215122024224769778Copyright © Кирутика Н.С., Тайламбал Г., 20242024Кирутика Н.С., Тайламбал Г.Kiruthika N.S., Thailambal G.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/222

Платформы социальных сетей, такие как Twitter, Instagram и Facebook, способствуют массовому общению и установлению связей. Развитие и продвижение социальных платформ приводит к увеличению распространения фейковых новостей. В настоящее время проведено большое количество исследований для обнаружения фейковых новостей с помощью алгоритмов машинного обучения. Существующие методы определения фейков имеют ряд трудностей: быстрое распространение фейков; различные методы доступа и незначительный выбор признаков, приводящие к невысокой точности классификации текста. Для преодоления данных трудностей предложена гибридная модель представления двунаправленного кодировщика трансформаторов – метод опорных векторов (BERT-SVM) с системой рекомендаций, которая используется для прогнозирования, является ли информация поддельной или реальной. Предложенная модель включает в себя три этапа: предварительная обработка, выбор признаков и классификация. Набор данных собран из социальных сетей Twitter, связанных с данными о COVID-19 в режиме реального времени. Этап предварительной обработки включает в себя разделение, удаление стоп-слов, лемматизацию и исправление орфографии. Преобразователь обратной частоты документа (TF-IDF) использован для извлечения признаков и преобразования текста в двоичные векторы. Гибридная модель классификации BERT-SVM применена для прогнозирования данных, которые сопоставлены с предварительно обработанными данными. Представленная модель реализована в программном пакете MATLAB. Рассчитанные показатели точности продемонстрировали следующие результаты: доля правильных ответов 98 %, ошибка 2 %, точность 99 %, специфичность 99 %, чувствительность 98 %. Полученные результаты показали эффективность предложенной модели по сравнению с существующими подходами. Возможность анализа социальных сетей обеспечивает эффективное предсказание фейковых новостей, которое можно использовать для идентификации комментариев в Twitter, как настоящих, так и поддельных.

Social media platforms, such as Twitter, Instagram, and Facebook, have facilitated mass communication and connection. Due to the development as well as the advancement of social platforms, the spreading of fake news has increased. Many studies have been performed for detecting fake news with machine learning algorithms; but these existing methods had several difficulties, such as rapid propagation, access method and insignificant selection of features, and low accuracy of the text classification. Therefore, to overcome these issues, this paper proposed a hybrid Bidirectional Encoder Representations from Transformers — Support Vector Machine (BERT-SVM) model with a recommendation system that used to predict whether the information is fake or real. The proposed model consists of three phases: preprocessing, feature selection and classification. The dataset is gathered from Twitter social media related to COVID-19 real-time data. Preprocessing stage comprises Splitting, Stop word removal, Lemmatization and Spell correction. Term Frequency Inverse Document Frequency (TF-IDF) converter is utilized to extract the features and convert text to binary vectors. A hybrid BERT-SVM classification model is used to predict the data. Finally, the predicted data is compared with the preprocessed data. The proposed model is implemented in MATLAB software with several performance metrics carried out, and these parameters attained better performance: accuracy is 98 %, the error is 2 %, precision is 99 %, specificity is 99 %, and sensitivity is 98 %. Therefore the better effectiveness of the proposed model than existing approaches is shown. The proposed social networking analysis model provides effective fake news prediction that can be used to identify the Twitter comments, either real or fake.

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The authors declare that there are no conflicts of interest present.