дезінформація, пропаганда

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 describes a study of identification of fake news based on natural language processing, big data analysis and deep learning technology. The developed system automatically checks the news for signs of fake news, such as the use of manipulative language, unverified sources and unreliable information. Data visualization is implemented on the basis of a friendly user interface that displays the results of news analysis in a convenient and understandable format.