Lexic-Grammatical Markers of the Manipulativeness of Mass Media Discourse (based on the Material of the Body of Russian-Language Media Texts in Republic of Kazakhstan)

The article is devoted to the phenomenon of manipulative mass media discourse. The results of the analysis of Kazakhstani media with verbally expressed manipulative discursive practice are presented. The research material was the Russian-language texts of opposition newspapers and information and analytical portals of the Republic of Kazakhstan. The choice of the source of factual material is due to the fact that it is in such a mass media discourse that language markers of the manipulability of the text are most pronounced. Given the generalization of the results of modern research, the concept of the manipulative nature of mass media discourse is defined. As a result of the analysis of texts from the position of cognitive-discursive and linguistic approaches, the main lexical, morphological and syntactic markers of manipulativeness are identified. For the first time, syntactic tools are described and formalized in which predicates in collocation with lexical and morphological tools form lexical and grammatical markers - statistically valid indicators of computer diagnostics of manipulativeness as a factor of a certain information threat to society. The results of the study can be theoretically and practically significant in the fields of sociology, political science, pragmatic linguistics, journalism, and information technology

The use of machine learning “black boxes” explanation systems to improve the quality of school education

The paper describes development of a multi-criteria decision support system (MCDSS) to improve the quality of school education. It is proposed to apply interpretable machine learning models for making decisions on improving the quality of education in secondary schools. Existing DSS are based on the expert judgement, which can be subjective. In addition, the large amount of data and features makes manual analysis difficult. Our approach is referred to as MCDSS with “black boxes” explainer, it consists of three stages. First, we develop the target indicators that measure the quality of education. A set of four features of quality of education (Q-Edu) has been developed. Secondly, we build regression models that link the data of the national educational database (NEDB) with target indicators. Thirdly, we use machine learning model interpreters to develop recommendations. The disadvantage associated with the difficulties of interpreting the results of models is overcome by SHAP (SHapley Additive exPlanations), which is used as a basis for developing recommendations for what features of educational institution could be altered in order to improve quality indicators. Using the described process, we, in particular, revealed the positive impact of the location of the school, ratio of experienced teachers, sports, technical and art studios on Q-Edu indicators. The ratio of experienced teachers and, at the same time, young teachers younger than 25 year positively affects the number of significant student achievements. The proposed universal approach reduces the subjectivity and laboriousness of parameter significance determination in MCDSS