Keywords
How to Cite
Abstract
The full-scale invasion of Ukraine by the russian federation had a number of geopolitical and domestic consequences. One of the manifestations of such consequences on both of the above levels was the change in the modern information environment under the influence of the actualization of numerous russian narratives. Naturally, the consequence of such false information should be the weakening and vulnerability of Ukrainians to manipulation, disinformation, etc. The latter is represented in the peculiarities of updating the above-mentioned data with false, exaggerated, distorted, etc. information. The above, in turn, determines the relevance and urgency of the problem of their existence in the context of russian disinformation, misinformation propaganda, and fakes (as well as diplomatic fakes), as well as the development of tools for their analysis to prevent their destructive impact. We are talking about the nature of the language poly system and Natural Language Processing (hereinafter – NLP) as tools for processing language data for further use in the process of Machine Learning (hereinafter – ML) and analysis using an artificial neural network. These include vectorization, tokenization, lemmatization, speech interface (Google Assistant and others), automatic translation (Google Translate, Reverso, DeepL, etc.), and other areas. Naturally, the aforementioned study is of particular relevance in the context of the hybrid nature of the Russian-Ukrainian war, in which the media space (online discourse) is home to a whole bunch of fake, distorted, actually false, and other data. That is why in our research we will analyze the main issues related to the problem of methodological features of neural network modeling of the processes of recognizing linguistic markers of the categories of sense and absurdity, paying attention to their nature, mutual influence, and interdependence, priority and socio-cultural significance for Ukrainian society. In addition, we will consider the specifics of the above process in the context of working with false data (disinformation), as well as the impact of the latter on its course.
References
Arnfield, D. (2023). Enhanced Content-Based Fake News Detection Methods with Context-Labeled News Sources.
Chung, S., Moon, S., Kim, J., Kim, J., Lim, S., & Chi, S. (2023). Comparing natural language processing (NLP) applications in construction and computer science using preferred reporting items for systematic reviews (PRISMA). Automation in Construction, 154, 105020.
Derbentsev, V. D., Bezkorovainyi, V. S., Matviychuk, A. V., Pomazun, O. M., Hrabariev, A. V., & Hostryk, A. M. (2023). A comparative study of deep learning models for sentiment analysis of social media texts. In CEUR Workshop Proceedings (pp. 168-188).
Fazil, M., Khan, S., Albahlal, B. M., Alotaibi, R. M., Siddiqui,T., & Shah, M. A. (2023). Attentional multi-channel convolution with bidirectional LSTM cell toward hate speech prediction. IEEE Access, 11, 16801-16811.
Guo, F. (2023). Revisiting Item Semantics in Measurement: A New Perspective Using Modern Natural Language Processing Embedding Techniques (Doctoral dissertation, Bowling Green State University).
Heimann, M., & Hübener, A. F. (2023). Circling the Void: Using Heidegger and Lacan to think about Large Language Models.
Kar, P., & Debbarma, S. (2023). Multilingual hate speech detection sentimental analysis on social media platforms using optimal feature extraction and hybrid diagonal gated recurrent neural network. The Journal of Supercomputing, 1-32.
Nikula, O. (2023). Linguistic Feature Analysis of Real and Fake News: Human-written vs. Grover-written.
Park, E. H., & Storey, V. C. (2023). Emotion Ontology Studies: A Framework for Expressing Feelings Digitally and its Application to Sentiment Analysis. ACM Computing Surveys, 55(9), 1-38.
Rastogi, S., & Bansal, D. (2023). A review on fake news detection 3T’s: Typology, time of detection, taxonomies. International Journal of Information Security, 22(1), 177-212.
Repede, Ș. E. (2023). Researching disinformation using artificial intelligence techniques: challenges. Bulletin of "Carol I" National Defence University, 12(2), 69-85.