The Speech of Social Media Influencers in Najd: Introducing a New Source of Sociolinguistic Data
DOI:
https://doi.org/10.17507/tpls.1308.28Keywords:
indicator, marker, production data, stereotypeAbstract
This study explores using the speech of Najdi social media influencers in Snapchat and TikTok as a source of sociolinguistic production data. Saudis in general, Najdis in particular, post their daily life vlogs on these apps building a huge volume of recorded speech in the local dialects. This type of content has been examined to introduce the possibility of using it as a source of production data alongside/instead of the sociolinguistic interview. The speech of 18 social media influencers, representing three sub-dialects of Najd, has been transcribed and analyzed to test whether it is natural and not mostly formal. The variables have been ranked according to Labov’s (1972) classification of speakers’ awareness: stereotype, marker and indicator. It was found in this study that, similar to data elicited from the interview, social media influencers tend to use their local variants which are classified as markers and/or indicators, and they avoid using stereotypes. In some social settings, the social media influencers were even more spontaneous than they would be in an interview. The study suggests that the speech of social media influencers in Najd is an easily accessible, larger, and better-quality source of sociolinguistic production data.
References
Aboh, S. C., & Ezeudo, O. C. (2020). Interactions on Facebook and Twitter: A communicative action perspective. Theory and Practice in Language Studies, 10(11), 1351-1358. https://doi.org/10.17507/tpls.1011.02
Abu-Haidar, F. (2006). Baghdad Arabic. In K. Versteegh, M. Woidich, M. Eid, A. Elgibali, &Z. Andrzej (Eds.), Encyclopedia of Arabic language and linguistics (Vol. 1, pp. 222- 231). Leiden: Brill.
Ad-Darsoni, S. (2013). Mu’jam allahajaat almahkiyyah fi almamlakah al’arabiyah assi’udiyah. Jazan University. Jazan, Saudi Arabia.
Alajmi, N. (2019). The Bedouin-Sedentary Dichotomy in Najd: A Sociolinguistic Study [Unpublished doctoral dissertation]. University of York, UK.
Alajmi, N. M., & Alghannam, A. G. (2022). The sociolinguistic salience of linguistic variables in Najdi Arabic. World Journal of English Language, 12(6), 114-128. https://doi.org/10.5430/wjel.v12n6p114
Almeman, K., & Lee, M. (2013), Automatic building of Arabic multi dialect text corpora by bootstrapping dialect words, In 1st International Conference Communications, Signal Processing, and their Applications (ICCSPA), Sharjah, UAE, IEEE.
Alshutayri, A., & Atwell, Eric. (2021). Classifying Arabic dialect text in the Social Media Arabic Dialect Corpus (SMADC). Proc. of the 3rd Workshop on Arabic Corpus Linguistics, pp. 51-59.
Al-Wer, E. (2013). Sociolinguistics. In J. Owens (ed.), The Oxford Handbook of Arabic Linguistics. Oxford: Oxford University Press, pp. 241-263
Androutsopoulous, J., & Ziegler, E. (2004). Exploring language variation on the Internet: Regional speech in a chat community. In Gunnarsson, B.-L. et al. (eds.), Papers from ICLaVE 2 Uppsala Papers from the Second International Conference on Language Variation in Europe, ICLaVE 2. Uppsala, Sweden: Department of Scandinavian Languages, Uppsala University
Bazarova, N. N., Taft, J. G., Choi, Y. H., & Cosley, D. (2012). Managing impressions and relationships on Facebook. Journal of Language and Social Psychology, 32(2), 121-141. https://doi.org/10.1177/0261927x12456384
Biel, J., & Gatica-Perez, D. (2010). Voices of vlogging, in ICWSM 2010 - Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, pp. 211-214.
Carr, C. T., Schrock, D. B., & Dauterman, P. (2012). Speech acts within Facebook status messages. Journal of Language and Social Psychology, 31(2), 176-196. https://doi.org/10.1177/0261927x12438535
Ceron, A., & D’Adda, G. (2016). E-campaigning on Twitter: The effectiveness of distributive promises and negative campaign in the 2013 Italian election. New Media & Society, 18(9), 1935-1955. https://doi.org/10.1177/1461444815571915
Christiansen, M. S. (2019). “Listisimo para los #XVdeRubi:” constructing a chronotope as a shared imagined experience in Twitter to enact Mexicanness outside of Mexico. Lingua, 225, 1-15. https://doi.org/10.1016/j.lingua.2019.05.002
Concha, S. (2019). Analysis of Strategic and Sociolinguistic Competences Elicited Through Online Chat Activities. Praxis Pedagógica, 19(24), 41-59. http://dx.doi.org/10.26620/uniminuto. praxis.19.24.2019.41-59
Doyle, G. (2014). Mapping dialectal variation by querying social media. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics. https://doi.org/10.3115/v1/e14-1011
Eisenstein, J. (2015). Systematic patterning in phonologically-motivated orthographic variation. Journal of Sociolinguistics, 19(2), 161-188. https://doi.org/10.1111/josl.12119
Helmond, A., & Van der Vlist, F. N. (2019). Social media and platform historiography: Challenges and opportunities. TMG Journal for Media History, 22(1), 6-34. https://doi.org/10.18146/tmg.434
Ingham, B. (1994). Najdi Arabic: Central Arabian. Amsterdam: John Benjamins Publishing Company.
Kemp, S. (2022). Digital 2022: Egypt. Datareportal. Retrieved March 10, 2023, from https://datareportal.com/reports/digitalegypt.
Labov, W. (1972). Sociolinguistic Patterns. Philadelphia, PA: University of Pennsylvania Press.
Labov, W. (1984). Field methods of the Project on Linguistic Change and Variation. In J. Baugh, & J. Sherzer (Eds.), Language in use: Readings in sociolinguistics (pp. 28-53). Englewood Cliffs: Prentice Hall.
McDonnell, A. (2020). Clinton stated, Trump exclaimed! Gendered language on Twitter during the 2016 presidential debates. Journal of Language and Politics, 19(1), 71-88. https://doi.org/10.1075/jlp.19085.mcd
Mubarak, H., & Darwish, K. (2014), Using Twitter to collect a multi-dialectal corpus of Arabic, In Proceedings of the EMNLP, Workshop on Arabic Natural Language Processing (ANLP), Doha, Qatar, Association for Computational Linguistics, pp.1-7.
Palva, H. (2006). Dialects: Classification. In K. Versteegh, M. Woidich, M. Eid, A. Elgibali, & Z. Andrzej (Eds.), Encyclopedia of Arabic language and linguistics (Vol. 1, pp. 604-613). Leiden: Brill.
Saudi Arabia Social Media Statistics 2022: Most popular platforms. The Global Statistics - The Data Experts | Statistical Data Reports. (2022, August 30). Retrieved September 10, 2022, from https://www.theglobalstatistics.com/saudi-arabia-social-media-users/
Sun, Y., Wang, G., & Feng, H. (2021). Linguistic studies on social media: A bibliometric analysis. SAGE Open, 11(3), 1-12 https://doi.org/10.1177/21582440211047572.
Sutrisno, B., & Ariesta, Y. (2019). Beyond the use of code mixing by social media influencers in Instagram. Advances in Language and Literary Studies, 10(6), 143-151. https://doi.org/10.7575/aiac.alls.v.10n.6p.143
Tankosić, A., & Dovchin, S. (2021). The impact of social media in the sociolinguistic practices of the peripheral post-socialist contexts. International Journal of Multilingualism, 1-22. https://doi.org/10.1080/14790718.2021.1917582
Versteegh, K. (2001). The Arabic language. Edinburgh, UK: Edinburgh University Press.
Watson, J. (2002). The phonology and morphology of Arabic. Oxford: Oxford University Press.