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Leveraging Emotional Signals for Credibility Detection
P. 877–880.
Giachanou A., Россо П., Crestani F.
The spread of false information on the Web is one of the main problems of our society. Automatic detection of fake news posts is a hard task since they are intentionally written to mislead the readers and to trigger intense emotions to them in an attempt to be disseminated in the social networks. Even though recent studies have explored different linguistic patterns of false claims, the role of emotional signals has not yet been explored. In this paper, we study the role of emotional signals in fake news detection. In particular, we propose an LSTM model that incorporates emotional signals extracted from the text of the claims to differentiate between credible and non-credible ones. Experiments on real world datasets show the importance of emotional signals for credibility assessment.
In book
NY: Association for Computing Machinery (ACM), 2019.
Nazarova V., Lodiagin B., Круглов Ф. А. et al., AlterEconomics (ранее - Журнал экономической теории) 2025 № 22(3) С. 482–502
This paper examines methods for forecasting oil prices, comparing traditional autoregressive mo dels (ARIMA, SARIMAX) with machine learning approaches (LSTM). The target variable is the price of WTI crude oil. The dataset covers 2015–2019 and includes both WTI price data and a set of exogenous varia bles: the Wilshire 5000, Dow Jones, and DXY indices; ...
Added: October 5, 2025
Surkov A., Zakharov V., Sergei Koltcov et al., , in: Smart Technologies, Systems and Applications: 4th International Conference, SmartTech-IC 2024, Quito, Ecuador, December 2–4, 2024, Revised Selected Papers, Part IIVol. 2: Revised Selected Papers, Part II.: Springer, 2025. P. 239–252.
Currently, large language models are actively developing and beginning to be used to solve some mathematical problems. With the emergence of xLSTM model, which demonstrates the results comparable with transformer-based models, there has been a surge of interest in recurrent neural networks. This paper considers the application of baseline recurrent models such as LSTM and ...
Added: September 11, 2025
Natalia Sizykh, Said Dandamaev, Dmitry Sizykh, , in: 16th International Conference Management of large-scale system development (MLSD).: IEEE, 2023. P. 1–5.
Forecasting data and research on cryptocurrency price forecasting methods are increasing in importance. So far, methods based on LSTM deep learning architecture have shown the best results in forecasting cryptocurrency prices. In order to improve the accuracy of forecasting data, this paper investigates the application of a multivariate multistep forecasting method based on the LSTM ...
Added: December 22, 2023
I. K. Kusakin, Fedorets O. V., A. Y. Romanov, Scientific and Technical Information Processing 2023 Vol. 50 No. 3 P. 176–183
This paper discusses modern approaches to natural language processing and the application of machine learning models to the task of classifying short scientific texts in Russian. This study is devoted to the analysis of methods for vectorization of textual information, selection of a model for scientific paper clas- sification, and training of linguistic model BERT ...
Added: November 4, 2023
Кусакин И. К., Федорец О. В., Romanov A., Научно-техническая информация. Серия 2: Информационные процессы и системы 2022 Т. 12 С. 6–9
This paper discusses modern approaches to natural language processing and appliance of artificial intelligence technologies in the task of classifying scientific texts in Russian. The report contains an analysis of implementations of text vectorization methods, a description of experiments with training various classifier models: from classical machine learning algorithms to neural network transformer architectures. ...
Added: January 31, 2023
Vukovic D., Romanyuk K., Ivashchenko S. et al., Expert Systems with Applications 2022 Vol. 194 No. May 2022 Article 116553
This paper investigates the forecasting performance for credit default swap (CDS) spreads by Support Vector
Machines (SVM), Group Method of Data Handling (GMDH), Long Short-Term Memory (LSTM) and Markov
switching autoregression (MSA) for daily CDS spreads of the 513 leading US companies, in the period
2009–2020. The goal of this study is to test the forecasting performance of ...
Added: February 4, 2022
Bryanov K., Vziatysheva V., Plos One 2021 Vol. 16 No. 6 Article e0253717
Background
Proliferation of misinformation in digital news environments can harm society in a number of ways, but its dangers are most acute when citizens believe that false news is factually accurate. A recent wave of empirical research focuses on factors that explain why people fall for the so-called fake news. In this scoping review, we summarize ...
Added: June 25, 2021
Lukankin Alexander, Slastnikov Sergey, Journal of Physics: Conference Series 2021 Vol. 1740 P. 1–6
Paper is devoted to the predictive models for metrological indicators on the real estate engineering infrastructure. The solution is in demand among many enterprises both in terms of security and economic considerations. The key task is to build a mathematical model performing predictions on the real data samples. We study both classical predictive models (ARIMA, ...
Added: February 2, 2021
Попова А. С., Рассадин А. Г., Пономаренко А. А., В кн.: Материалы XXIV международной научно-технической конференции «Информационные системы и технологии-2018.: [б.и.], 2018. С. 1083–1089.
Рассматривается задача автоматической классификации эмоций в цифровом аудио сигнале. В работе рассматривается и верифицируется подход, в котором классификация звукового фрагмента производится с помощью рекуррентной нейронной сети c долговременно-кратковременной памятью. В качестве признаков использовались мел-кепстральные коэффициенты. Произведен численный эксперимент на открытом наборе данных Ravdess, включающий 8 различных эмоций: “нейтральный”, “спокойный”, “счастливый”, “грустный”, “злой”, “испуганный”, “отвращение”, “удивление” ...
Added: October 21, 2018
Slastnikov S., Лупанов В. Э., Journal of Physics: Conference Series 2019 Vol. 1163 No. 12048 P. 1–6
A lot of files and data, in general, are transferred throughout the networks. But the data may be corrupted by intrusions or package loss so, the executable files may be marked as non-executable and violate the local network policy. Thus, it’s necessary to detect such files. In this paper, we present a novel method for ...
Added: October 19, 2018
Grachev A., Ignatov D. I., Savchenko A., , in: Pattern Recognition and Machine Intelligence. 7th International Conference, PReMI 2017, Kolkata, India, December 5-8, 2017, Proceedings. Lecture Notes in Computer Science book series (LNCS, volume 10597).: Springer, 2017. P. 351–357.
In this paper, we consider several compression techniques for the language modeling problem based on recurrent neural networks (RNNs). It is known that conventional RNNs, e.g., LSTM-based networks in language modeling, are characterized with either high space complexity or substantial inference time. This problem is especially crucial
for mobile applications, in which the constant interaction with ...
Added: October 14, 2018