Situation and Text: Representation of Migrants Whilst the Escalation of Refugee Crisis in Great Britain as Compared to Russia

Document Type: Research Article


International Law Faculty, Academy of Sciences, MGIMO, Russia.



Increasing migration is a vital concern for a globalizing sociocultural environment in today’s world. The UK and developed European countries have become an attractive destination for asylum seekers (labelled as “migrants”) in the past decade. The rapid rise in the number of asylum seekers, which was labelled “migration crisis” (Ruz, 2015), made this topic an integral part of scientific discussion on sociocultural environment. There are different factors underlying the perception of migrants by local population. The given study is devoted to the review of the usage of different linguistic means and its strategies by mass media whilst the escalation of refugee crisis in Great Britain as compared to Russia. We implement situational approach to the non-fiction factual texts, in particular, texts representing a concrete (actual) situation. Today due to the development of neural network technology we can easily define thematic structure of the text, specify the central lexis, the near peripheral lexis and the far peripheral lexis and reveal implicit information. The data of British National Corpus and Russian National Corpus is analyzed using text mining software- TextAnalyst 2.01. The relationships and patterns among key words and their associations are cluster-analyzed and contextual interpretation of semantic core is provided. Associative structures enable us to define the lexical experience of its speakers (White & Abrams, 2004) while the analysis of associative network helps to study public opinion on different social issues (File, Keczer, Vancsó et al., 2018). The data for the text mine is gathered from the website (data set 1) and British National Corpus (data set 2). The thematic network depicts the structure of the text and the most pertinent information in the data. The semantic network constructs a list of topics and their semantic weights. It is a tree structure of the concepts of the text and identifies the relationships in the text. Just like in data set 1 public attitudes in data set 2 towards refugees and migrants are complex. Representation of migrants and hence public opinion are complicated by predisposition to favor those using legal means to enter a country with national culture, traditional customs, laws and ability to speak the language of the host country being the key factor in integrating migrants.