Suchergebnisse

The article solves the problem of identifying markers of positive or negative sentiment in the network discourse that is formed in social networks in relation to a particular politician. The theoretical and methodological foundations of the study were the basics of network linguistics, the network approach, Big Data. To conduct an empirical study using the method of continuous sampling for the keyword "Boris Johnson", data from the social network Twitter was uploaded from May 15 to July 15, 2021 through the Twitter API service. The received dataset amounted to 1 million 900 thousand messages which were divided into a dataset of messages with a positive sentiment and a dataset of messages with a negative sentiment. In each dataset, frequently used fragments are identified and subjected to linguistic discursive analysis. As a result of their analysis, markers of the positive and negative sentiment of the online discourse that is emerging in the Internet space in relation to British Prime Minister Boris Johnson have been identified. They reflect public opinion, the level of trust in a politician, the pole of evaluation of his activities. Considering such markers when developing strategies for working with public opinion will allow changes in the image and reputational potential of public figures and organizations both online and offline.

В статье рассматриваются этапы трансформации модели "Говорящий - Слушающий" с точки зрения способов генерирования, передачи, распространения контента и охвата аудитории. ; Through the prism of the scientific paradigm of "linguistic turn", the article describes 5 stages of transfor-mation of the model "Speaker - Listener" from the point of view of the methods of generation, transmission, and dissemination of content, audience attraction, as well as in terms of assessment of the result and the reaction of users / readers, consisting in the feedback and actional response (online / offline

An asynchronous multimodal discursive field formed by political content is a reflection of the activities of a network community from a small community of residents of any locality to network communities operating within the sub-jects of the Russian Federation and in the whole country. Political content is able to transform socio-political reality by pro-ducing events - a message or a series of messages that can change the balance of power or interests in society. Regional political discourse, political content and the asynchronous multimodal discursive fields produced by it accumulate the poten-tial of social action in the online space which can lead to both constructive and destructive actions in the offline space. To analyze the regional political discourse, a political content management model was applied and a research methodology was tested. ; В статье рассматриваются особенности формирования и развития регионального политического дискурса в онлайн-пространстве современных субъектов Российской Федерации.

The specificity of modern socio-political and linguistic changes is most clearly reflected in the function-ing of linguistic models of socio-political communication between citizens / consumers in relation to institutionalized and non-institutionalized political actors. Modern social reality is largely shaped by the influence of the Internet and social me-dia. The functional segmentation of social media is an effect of the coronavirus era. The social networking website Twitter has become a source of political content worldwide in 2019-2020. The empirical base of the study includes two sets of net-work data: the first data set (March 2020, 50,000 messages) and the second data set (June 2020, 50,000 messages) consist of network data obtained by the method of continuous unloading of messages published by Twitter users and containing the keyword "trump" through the Twitter API. ; В статье рассматривается смена позитивного дискурса в отношении Д. Трампа на отрицательный, в котором превалирующими стали обсуждение протестов и резкая критика со стороны всех групп населения.