disinformation

The article considers the topical aspects of implementing fact-checking formats in the Ukrainian media landscape in the framework of Russian-Ukrainian war. The authors analyse external and internal challenges that complicate ensuring the reliability and timeliness of information verification. The main problems include the widespread dissemination of disinformation, the use of generative artificial intelligence to create fakes, limited access to official sources, financial and personnel constraints, and insufficient coordination between fact-checking resources and government agencies.

The article provides a theoretical analysis of the phenomenon of cognitive warfare as a new type of conflict within the system of contemporary information wars, in which the primary target is the human mind – its cognitive processes, emotional responses, and decision-making mechanisms.

The article examines false information in digital media as a sociotechnical phenomenon that is shaped at the intersection of content distortions, producer intent, and networked platform mechanisms of diffusion. Given the blurred nature of the everyday use of the terms fake and fake news, a conceptual delimitation of related phenomena is undertaken, and the tendency to universalise these concepts across different types of falsehood is critically analysed.

In the digital age, the significant increase in information generation and processing is accompanied by a growing threat of unauthorized access, illegal distribution, and use. One of the most promising strategies for protecting information from various cyber threats and malicious attacks is the use of Natural Language Processing (NLP) processors. This article focuses on the methodology of data protection in the context of utilizing Natural Language Processing for sentiment analysis and trend detection.

The article investigates the current scientific problem of automated detection of propaganda influence in short text comments of users of the TikTok social network, which operates in conditions of hybrid warfare and intensive disinformation campaigns. A hybrid model for detecting propaganda content has been developed, which integrates deep contextual representations of the text (transformer-based contextual representations) based on the BERT architecture with an additional vector of semiotic and structural features (number of emojis, repetition of symbols, use of caps lock).

The article is devoted to a comprehensive and interdisciplinary study of the “United News” telethon as one of the key sources of primary information in the Ukrainian media space and to the analysis of its influence on the formation of trust, patterns of information perception, and the level of media literacy of the population of Ukraine in the context of the full-scale Russian invasion.

Abstract. This article addresses the issue of combating deepfakes, which has recently gained significant relevance. With the emergence of publicly available artificial intelligence tools capable of generating highly convincing images, video clips and other types of content, as well as a favorable digital landscape for their dissemination, deepfake technology has become increasingly prevalent. Given the risks of deepfakes, reasonable expectations are placed on the law designed to protect our fundamental values, which are often

A method for detecting sources of disinformation and inauthentic behavior of chat users in social networks has been developed. The developed model is based on the analysis of text information using modern machine learning algorithms, in particular classifiers (SVM, Naive Bayes, decision trees, etc.) and clustering methods to identify structural relationships between news and users. Considerable attention is paid to the collection and balancing of datasets, as well as the visualization of networks to assess the spread of fake news.

The article considers an approach to detecting fake news in the digital environment through text analysis using machine learning and natural language processing methods. The proposed method is based on a hybrid text representation combining frequency features (TF-IDF) and contextual embeddings obtained using the IBM Granite model. A complete data processing cycle was developed, covering the stages of exploratory analysis (EDA), text preprocessing and tokenization, forming vector representations, training a logistic regression model, and obtaining key metrics.

The article provides a systematic analysis of threats to compliance with legal principles of elections in a globalized digital society.