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. 2021 Mar 31;10(1):15. doi: 10.1140/epjds/s13688-021-00271-0

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Language identification uncertainty as a function of tweet-length for top 10 most used languages on Twitter. We display the number of messages that were classified differently by Twitter-LID model and FastText-LID for the top-10 prominent languages as a function of the number of characters in each message. Unlike Twitter, we count each character individually, which is why the length of each message may exceed the 280 character limit. The grey lines indicate the daily number of mismatches between 2020-01-01 and 2020-01-07 (approximately 32 million messages for each day for the top-10 used languages), whereas the black line shows an average of that whole week