Social Media in English and Russian Language Consciousness. Article 2. Corpus linguistics and modeling experience

Keywords: social media, social networks, psycholinguistics 2.0, corpus linguistics, English, Russian.

Abstract

Introduction. The article consists of two parts. The first part is devoted to the investigation of the structure and content of the concept “social media” in Russian and English linguistic consciousness according to the data of a serial psycholinguistic experiment. The second part provides an analysis of the concept in corpus linguistics. It also includes the field and the classificational cognitive models of the concept “social media”.

Methods and data for the study. The presence of the concept in the Russian National Corpus (RNC) and the Corpus of Contemporary American English (COCA) was investigated via corpus analysis. We analyzed 42 158 contexts with mentions of the concept “social media”. Using a method of semantic network analysis, we built a model of the concept in the shape of a semantic graph.

Results. We elicited that the concept «social media» in both Russian and English linguistic cultures is in the stage of formation. The non-specific characteristic of the concept in Russian and English languages is its identical structure (dissimilarities are noticeable only in peripheral zones). Socially and culturally specific characteristics are: (1) major formation of the concept in English linguistic culture (higher nominative density); (2) in English language concept’s nominative field represents personal contacts, while in Russian it represents disengaged and impersonal attitude to social media.

Conclusion. The research of a conceptual nature of social media has a perspective in studying its idioethnic specificity, its dynamic nature, and other cognitive formations (scenarios and frames), connected with social media, based on the data of different language families.

Downloads

Download data is not yet available.

References

Atteveldt, van Wouter. (2008). Semantic Network Analysis: Techniques for Extracting, Representing, and Querying Media Content. Retrieved from https://cs.vu.nl/en/Images/WH_van_Atteveldt_14-10-2008_tcm210-259647.pdf

Bell, A., & Quillian, M.R (1969). Capturing Concepts in a Semantic Net. Retrieved from https://apps.dtic.mil/dtic/tr/fulltext/u2/697035.pdf https://doi.org/10.21236/AD0697035

Brown, C.H., Holman, E.W., Wichmann, S., & Velupillai, V. (2008). Automated Classification of the World’s Languages: A Description of the Method and Preliminary Results. STUF – Language Typology and Universals, 61(4), 285–308. https://doi.org/10.1524/stuf.2008.0026

Drieger, Ph. (2013). Semantic Network Analysis as a Method for Visual Text Analytics. Procedia – Social and Behavioral Sciences, 79, 4–17. https://doi.org/10.1016/j.sbspro.2013.05.053

Elwert (Bochum), F., & Gerhards, S. (2017). Tracing Concepts – Semantic Network Analysis as a Heuristic Device for Classification. In T. Pommerening & W. Bisang (Eds.), Classification from Antiquity to Modern Times: Sources, Methods, and Theories from an Interdisciplinary Perspective (pp. 311–338). Berlin: De Gruyter. https://doi.org/10.1515/9783110538779-012

Goroshko, O. (2008). Psiholingvistika Internet-kommunikacij [Psycholinguistics of Internet Сommunications]. Voprosy psiholingvistiki – Journal of Psycholinguistics, 7, 5–12. [in Russian].

Holman, E.W., Brown, C.H., Wichmann, S. et al. (2011). Automated dating of the world’s language families based on lexical similarity. Current Anthropology, 52(6), 841–875. https://doi.org/10.1086/662127

Hunter, S. (2014). A Novel Method of Network Text Analysis. Open Journal of Modern Linguistics, 4, 350–366. https://doi.org/10.4236/ojml.2014.42028

Koponen, I.T., & Nousiainen, M. (2018). Modelling students’ knowledge organisation: Genealogical conceptual networks. Physica A: Statistical Mechanics and its Applications, 495(C), 405–417. https://doi.org/10.1016/j.physa.2017.12.105

Lima, T.S., Arruda, de H.F., Silva, F.N. et al. (2018). The Dynamics of Knowledge Acquisition via Self-Learning in Complex Networks. Chaos, 28, 083–106. https://doi.org/10.1063/1.5027007

List, J.M., Cysouw, M., & Forkel, R. (2016). Concepticon. A Resource for the Linking of Concept Lists. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016) (Portorož, Slovenia, May 23–28, 2016). Retrieved from http://www.lrec-conf.org/proceedings/lrec2016/summaries/127.html

Shlyakhova, S., & Klyuev, N. (2020). Socialnye media v anglijskom i russkom jazykovom soznanii. Statja 1. Psiholingvisticheskie jeksperimenty [Social Media in English and Russian Language Consciousness. Article 1. Psycholinguistic experiments]. Psiholingvistika – Psycholinguistics, 27(2), 385–416. https://doi.org/10.31470/2309-1797-2020-27-2-385-416 [in Russian].

Silva, F.N., Amancio, D.R., Bardosova, M. et al. (2016). Using network science and text analytics to produce surveys in a scientific topic. Journal of Informetr, 10(2), 487–502. https://doi.org/10.1016/j.joi.2016.03.008

Wichmann, S., Holman, W., & Brown, C.H. (Eds.). (2018). The ASJP Database (version 18). Retrieved from https://asjp.clld.org/


Abstract views: 415
PDF Downloads: 338
Published
2020-11-08
How to Cite
Shlyakhova , S., & Klyuev , N. (2020). Social Media in English and Russian Language Consciousness. Article 2. Corpus linguistics and modeling experience. PSYCHOLINGUISTICS, 28(2), 204-223. https://doi.org/10.31470/2309-1797-2020-28-2-204-223