CORPUS LINGUISTIC STUDY OF TWEETS USING #CHARLIEHEBDO HASHTAG

Intan Siti Nugraha, Eva Tuckyta S. Sujatna, Sutiono Mahdi

Abstract


Hashtags of #CharlieHebdo becomes trending in Twitter when a knife attack incident happened in front of former Charlie Hebdo magazine’s office on 25 September 2020. If #JeSuisCharlie was used to show empathy and support for the victim and freedom of speech value, #CharlieHebdo still remains question on what topics around the Twitter discussion using the hashtag. Thus, using corpus linguistic analysis method, which are keyword and concordance analysis, this study aims to investigate the significant topic of corpus containing #CharlieHebdo. The tweet corpus which contains 8.604 tweets and retweets and words in total are 177.352 tokens (words) was constructed from the tweets scrapped by the researcher using Python and Twitter API. The result of analysis shows that there are at least 13 categories of keywords which indicate significant topics of the tweet corpus. They are place, attacker, act, weapon, religion/belief, motive, victims, figures, emotion evoked, law enforcement and other topics.

Keywords


Corpus Linguistics, Keyword Analysis, Concordance analysis, Topics, #CharlieHebdo, #ParisAttack

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References


Al Fajri, M. S. (2019). The discursive portrayals of Indonesian Muslims and Islam in the American press: A corpus-assisted discourse analysis. Indonesian Journal of Applied Linguistics, 9(1), 167-176.

Anthony, L. (2019). AntConc (Version 3.5. 8) (Computer Software), Waseda University, Tokyo.

Anthony, L., & Baker, P. (2015). ProtAnt: A tool for analysing the prototypicality of texts. International Journal of Corpus Linguistics, 20(3), 273-292.

Baker, P. (2004). Querying keywords: Questions of difference, frequency, and sense in keywords analysis. Journal of English Linguistics, 32(4), 346-359.

Baker, P. (2006). Using corpora in discourse analysis. A&C Black.

Baker, P. & McEnery, T. (2015). Who Benefiits When Discourse Get Democratised? Analyzing a Twitter Corpus around the British Benefits Street Debate.

Bondi, M., & Scott, M. (Eds.). (2010). Keyness in texts (Vol. 41). John Benjamins Publishing

Biber, D., & Reppen, R. (Eds.). (2015). The Cambridge handbook of English corpus linguistics. Cambridge University Press.

Chiluwa, I., & Ifukor, P. (2015). ‘War against our Children’: Stance and evaluation in #BringBackOurGirls campaign discourse on Twitter and Facebook. Discourse & Society, 26(3), 267-296.

Collins, L. C. (2019). Corpus linguistics for online communication: A guide for research. Routledge.

De Cock, B., & Pedraza, A. P. (2018). From expressing solidarity to mocking on Twitter: Pragmatic functions of hashtags starting with# jesuis across languages. Language in society, 47(2), 197.

Gabrielatos, C., & Baker, P. (2008). Fleeing, sneaking, flooding: A corpus analysis of discursive constructions of refugees and asylum seekers in the UK press, 1996-2005. Journal of English linguistics, 36(1), 5-38.

Giglietto, F., & Lee, Y. (2017). A hashtag worth a thousand words: Discursive strategies around# JeNeSuisPasCharlie after the 2015 Charlie Hebdo shooting. Social Media+ Society, 3(1), 2056305116686992.

Gimenes, M., & New, B. (2015). Worldlex: Twitter and blog word frequencies for 66 languages. Behavior research methods, 48(3), 963-972.

Kundnani, A. (2017). Extremism, Theirs and Ours: Britain’s Generational Struggle’. After Charlie Hebdo: Terror, Racism and Free Speech. London: Zed Books. A ‘Muslim’response, 193.

Mondon, A., & Winter, A. (2017). Charlie Hebdo, Republican Secularism and Islamophobia

Paquot, M., & Bestgen, Y. (2009). Distinctive words in academic writing: A comparison of three statistical tests for keyword extraction. In Corpora: Pragmatics and discourse (pp. 247-269). Brill Rodopi.

Partington, A., Duguid, A., & Taylor, C. (2013). Patterns and meanings in discourse: Theory and practice in corpus-assisted discourse studies (CADS) (Vol. 55). John Benjamins Publishing.

Reinhardt, W., Ebner, M., Beham, G., & Costa, C. (2009). How people are using Twitter during conferences. Creativity and Innovation Competencies on the Web. Proceedings of the 5th EduMedia, 145-156.

Ross, A. S., & Caldwell, D. (2020). “Going negative”: An Appraisal analysis of the rhetoric of Donald Trump on Twitter. Language & Communication. doi:10.1016/j.langcom.2019.09.003

Wendland, J., Ehnis, C., Clarke, R. J. & Bunker, D. (2018). Sydney siege, December 2014: A visualisation of a semantic social media sentiment analysis. In K. Boersma & B. Tomaszewski (Eds.), Proceedings of the 15th ISCRAM Conference (pp. 493-506).

Yuliawati, S., Dienaputra, R. D., Sujatna, E. T. S., Suryadimulya, A. S., & Lukman, F. (2019). Looking into “Awewe” and “lalaki” in the Sundanese Magazine Mangle: Local Wisdom and a Corpus Analysis of the Linguistic Construction of Gender. International Journal of Advanced Science and Technology, 28, 549-559.

Zappavigna, M. (2011). Ambient affiliation: A linguistic perspective on Twitter. New media & society, 13(5), 788-806.

Zappavigna, M. (2012). The Discourse of Twitter and Social Media (Continuum Discourse Series). Continuum International Publishing Group Limited.

Zappavigna, M. (2016). Searchable talk: The linguistic functions of hashtags in tweets about Schapelle Corby. Global Media Journal. Australian Edition, (10).

Zubiaga, A. (2018). A longitudinal assessment of the persistence of twitter datasets. Journal of the Association for Information Science and Technology, 69(8), 974-984.




DOI: http://dx.doi.org/10.25157/jall.v5i1.4965

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