ANALYSIS OF STATISTICAL CHARACTERISTICS OF ARTIFICIALLY GENERATED TEXTS
Annotation
A new trend is considered, namely, the formation of content using artificial intelligence tools and technologies. Active implementation of artificial intelligence technologies for data generation leads to an increase in the share of artificially generated data that must be identified automatically to prevent errors (unreliability, misleading). Approaches to identifying text data created using neural network technologies are proposed, including heuristic rules based on the criterion of dependence of the abstract volume on the abstracting threshold, which allows for automatic evaluation of text documents in monitoring and search systems when processing large volumes of unstructured data. The obtained results lay the technological basis for the implementation of a wide range of practical solutions to ensure intellectual support for the collective behavior of participants in human-machine communities through the development of theoretical and technological foundations for processing unstructured data.
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