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How character limit affects language usage in tweets

Author

Listed:
  • Arnout B. Boot

    (Erasmus University Rotterdam)

  • Erik Tjong Kim Sang

    (Netherlands eScience Center)

  • Katinka Dijkstra

    (Erasmus University Rotterdam)

  • Rolf A. Zwaan

    (Erasmus University Rotterdam)

Abstract

In November 2017 Twitter doubled the available character space from 140 to 280 characters. This provided an opportunity for researchers to investigate the linguistic effects of length constraints in online communication. We asked whether the character limit change (CLC) affected language usage in Dutch tweets and hypothesized that there would be a reduction in the need for character-conserving writing styles. Pre-CLC tweets were compared with post-CLC tweets. Three separate analyses were performed: (I) general analysis: the number of characters, words, and sentences per tweet, as well as the average word and sentence length. (II) Token analysis: the relative frequency of tokens and bigrams; (III) part-of-speech analysis: the grammatical structure of the sentences in tweets (i.e., adjectives, adverbs, articles, conjunctives, interjections, nouns, prepositions, pronouns, and verbs); pre-CLC tweets showed relatively more textisms, which are used to abbreviate and conserve character space. Consequently, they represent more informal language usage (e.g., internet slang); in turn, post-CLC tweets contained relatively more articles, conjunctions, and prepositions. The results show that online language producers adapt their texts to overcome limit constraints.

Suggested Citation

  • Arnout B. Boot & Erik Tjong Kim Sang & Katinka Dijkstra & Rolf A. Zwaan, 2019. "How character limit affects language usage in tweets," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-13, December.
    • Handle: RePEc:pal:palcom:v:5:y:2019:i:1:d:10.1057_s41599-019-0280-3
    DOI: 10.1057/s41599-019-0280-3
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    References listed on IDEAS

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    1. Grolemund, Garrett & Wickham, Hadley, 2011. "Dates and Times Made Easy with lubridate," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i03).
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    1. Puklavec, Žiga & Kogler, Christoph & Stavrova, Olga & Zeelenberg, Marcel, 2023. "What we tweet about when we tweet about taxes: A topic modelling approach," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 1242-1254.
    2. Michal Ptaszynski & Agata Pieciukiewicz & Pawel Dybala & Pawel Skrzek & Kamil Soliwoda & Marcin Fortuna & Gniewosz Leliwa & Michal Wroczynski, 2023. "Expert-Annotated Dataset to Study Cyberbullying in Polish Language," Data, MDPI, vol. 9(1), pages 1-26, December.
    3. Puklavec, Žiga & Stavrova, Olga & Kogler, Christoph & Zeelenberg, Marcel, 2024. "Diffusion of tax-related communication on social media," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 110(C).
    4. Amal Marzouki & Arbi Chouikh & Sehl Mellouli & Rim Haddad, 2021. "From Sustainable Development Goals to Sustainable Cities: A Social Media Analysis for Policy-Making Decision," Sustainability, MDPI, vol. 13(15), pages 1-21, July.
    5. Saad Nor Hasliza Md & San Chin Wei & Yaacob Zulnaidi, 2023. "Twitter Sentiment Analysis of the Low-Cost Airline Services After COVID-19 Outbreak: The Case of AirAsia," Business Systems Research, Sciendo, vol. 14(2), pages 1-23, December.
    6. Jiping Cao & Hartwig H. Hochmair & Fisal Basheeh, 2022. "The Effect of Twitter App Policy Changes on the Sharing of Spatial Information through Twitter Users," Geographies, MDPI, vol. 2(3), pages 1-14, September.
    7. Ebaa Fayyoumi & Sahar Idwan, 2021. "Semantic Partitioning and Machine Learning in Sentiment Analysis," Data, MDPI, vol. 6(6), pages 1-17, June.

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