Colloquiality analysis on social networks

A case from Twitter

Authors

  • Andre Luiz França Batista Federal Institute of Triangulo Mineiro https://orcid.org/0000-0003-3225-8359
  • Carlos Henrique da Silveira Campos Federal Institute of Triangulo Mineiro
  • Daniel Ramos Pimentel Federal Institute of Triangulo Mineiro

DOI:

https://doi.org/10.31686/ijier.vol8.iss4.2295

Keywords:

social network sites, Twitter, natural language, linguistics

Abstract

Social network sites are present in a constant way in society. The manner people write in social network sites has their dynamism because of the speed information are replicated, the reach publications may have, network sites’ peculiarities and the seek for fame. This generates linguistic constructions that are not in accord with the standard norm of the Portuguese language. This quantitative work aims to relate the use of colloquial constructions on Twitter with user's popularity, posts popularity and other specific factors of this social network. This analysis was made using regular expressions, dictionaries, and frequency distribution to identify colloquial constructions. A support system was developed to perform analysis, management, and mining.

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References

Boyd, D.; Golder. S; Lotan, G. Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter. In HICSS-43. IEEE: Kauai, HI, January 6. 2010.
Davies, M.; Preto-Bay, A. A Frequency Dictionary of Portuguese (Routledge Frequency Dictionaries). 1. ed.: Taylor Francis Ltd. 2008.
Go, A.; Bhayani, R.; Huang, L. Twitter Sentiment Classification using Distant Supervision. In CS224N Project Report, Stanford. 2009.
Gouws, S.; Metzler, D.; Cai, C.; Hovy, E. Contextual Bearing on Linguistic Variation in Social Media. Proceedings of the Workshop on Language in Social Media (LSM 2011). 2011.
Hagen, L.; Uzuner, O.; Harrison, T. M.; Katragaddda, S. E-petition popularity: Do linguistic and semantic factors matter?. Government Information Quarterly. 2016.
Internet Live Stats, Twitter Usage Statistics. Retrieved from: <http://www.internetlivestats.com/twitter-statistics/>, accessed 30 January 2020.
Liu, Kun-Lin and Li, Wu-Jun and Guo, Minyi, Emoticon Smoothed Language Models for Twitter Sentiment Analysis., AAAI, 2012.
Nguyen, D.; Gravel, R.; Trieschnigg, D.; Meder, T. How Old Do You Think I Am? A Study of Language and Age in Twitter. ICWSM. 2013.
Perez-Sabater, C.; The linguistics of social networking: A study of writing conventions on Facebook. Linguistik online, 56(6/12):81-93. 2012.
Souza, L. P.; Deps, V. L. A linguagem utilizada nas redes sociais e sua interferência na escrita tradicional: um estudo com adolescentes brasileiros. Proceedings II Congresso Internacional TIC e Educação, Portugal. 2012.
Statista, Statistics and facts about social media usage. Retrieved from: <https://www.statista.com/topics/1164/social-networks/>, accessed 30 January 2020.
Statista, Statistics and facts about Twitter. Retrieved from: <https://www.statista.com/topics/737/twitter/>, accessed 30 January 2020.
Statista. Social media usage worldwide. Retrieved from <https://www.statista.com/study/12393/social-networks-statista-dossier/>, accessed 30 January 2020.
Twitter. Retrieved from: <https://twitter.com/>, accessed 30 January 2020.
UNFPA. State of World Population 2019. New York: UNFPA Press; 2019. Retrieved from <https://www.unfpa.org/swop-2019>, accessed 30 January 2020.
Yagui, M. M., Maia, L. F. M. P., Oliveira, J., & Vivacqua, A. S. Data mining of social manifestations in Twitter: analysis and aspects of the social movement “bela, recatada e do lar” (beautiful, demure and housewife). Journal of Computer Science, 17(1), 23-37. 2018.

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Published

01-04-2020

How to Cite

França Batista, A. L., da Silveira Campos, C. H., & Ramos Pimentel, D. . (2020). Colloquiality analysis on social networks: A case from Twitter. International Journal for Innovation Education and Research, 8(4), 369–390. https://doi.org/10.31686/ijier.vol8.iss4.2295