Socio-Political Events and Language of Twitter: The Representation of Events in Ukraine in Russian Twitter

Tuesday, 12 July 2016: 16:00
Location: Hörsaal 24 (Main Building)
Oral Presentation
Tatiana NIKULINA, St.-Petersburg State University, Russia
Elena YAGUNOVA, St.-Petersburg State University, Russia
Vladislav KOTOV, St.-Petersburg State University, Russia
Today social networks undoubtedly play an important role: it is a source of information, a mean of mobilization and an environment for the public discussion and reflection. Nowadays social reflections research takes new turns using language technologies. We propose the simple approach to the interpretation of social networks’ language.

The topic of the following research is concerned with many neologisms appearing in the period of popular socio-political events. Unlike Mass media language, language in social networks is more uninhibited and as far as Twitter is online platform users are free to use new words. The emergence of new emotionally charged words that directly reflect actual events is more likely in Twitter. Users create new words that express new concepts or new ideas or include the most popular emotional evaluation.  E.g. “майдаун” (Майдан [Maidan] +даун [daun] = [maidaun]) expresses negative attitude as it’s ending sounds like Russian insulting word “даун” (~mentally retarded).

Currently much research is being done on the analysis of neologisms. For example, BBC Magazine published an article “Twitter spawns twitterverse of new words” (5 September 2011). Lots of works are focused on detecting neologisms on Facebook. However, the majority of these studies do not pay attention to the reason of appearing of certain words, i.e. neologisms are studied in general, without any communicative focus. We focus particularly on factors leading to the creation of new words and/or changing the words meaning (key words, neologisms, memes), pointing out inseparability of society and language. The other task is to match the words and the communicative roles (information, manipulation, self-expression, etc.). 

Our research is actual and reliable as it based on representative corpus of tweets and the survey of 110 informants. The primary set was obtained by the statistical analysis to extract the most significant keywords (February-April 2014).