Analysis of information-psychological impact detection methods in social networks
Keywords:information-psychological impact, machine learning, neural networks, lexeme-oriented approach, sentiment analysis
Methods with the help of which it is possible to carry out automatic content analysis in social networks for the detection information-psychological impact are analyzed. Based on the performed research, the features of the virtual communities functioning in social networks were determined. Virtual communities have become objects and tools of external information management and the information confrontation arena at different levels. They have become an ideal tool for information-psychological impact on the national interests of the state, society in the information, and cyberspace, in general. To prevent and counteract shocks in society, it is necessary to constantly monitor the presence of negative informational-psychological impact in communities to be able to resist it. Methods for detecting information-psychological impact include methods based on the use of lexemes and machine learning with a teacher, namely: support vector machine, the naive Bayes classifier, decision trees, the method of maximum entropy, and neural networks. Each of the analyzed methods has its own advantages and disadvantages, features of use, which must be taken into account when choosing a method for detecting information-psychological impact in social networks. Among the methods considered for automatic content analysis, the most effective method is a machine learning based on the use of neural networks. This method does not involve pre-processing of text, there is no need to create dictionaries, can classify into several categories. This allows identifying different types of information-psychological impact by training the network with new information. So, updates of content in social networks are taken into account. It has been established that unlike neural networks, the decision tree for detecting information-psychological impact cannot be used in practice. This limitation is due to the difficulty of maintaining incremental training. You can take a large amount of data and build a decision tree for it. However, it is impossible to take into account new messages, because you will have to teach it from scratch every time.
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