The growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009–2020
Vermont Complex Systems Center, University of Vermont, 05405, Burlington, VT, USA
2 Computational Story Lab, University of Vermont, 05405, Burlington, VT, USA
3 Department of Computer Science, University of Vermont, 05405, Burlington, VT, USA
4 Charles River Analytics, 02138, Cambridge, MA, USA
5 Department of Mathematics & Statistics, University of Vermont, 05405, Burlington, VT, USA
Accepted: 17 March 2021
Published online: 31 March 2021
Working from a dataset of 118 billion messages running from the start of 2009 to the end of 2019, we identify and explore the relative daily use of over 150 languages on Twitter. We find that eight languages comprise 80% of all tweets, with English, Japanese, Spanish, Arabic, and Portuguese being the most dominant. To quantify social spreading in each language over time, we compute the ‘contagion ratio’: The balance of retweets to organic messages. We find that for the most common languages on Twitter there is a growing tendency, though not universal, to retweet rather than share new content. By the end of 2019, the contagion ratios for half of the top 30 languages, including English and Spanish, had reached above 1—the naive contagion threshold. In 2019, the top 5 languages with the highest average daily ratios were, in order, Thai (7.3), Hindi, Tamil, Urdu, and Catalan, while the bottom 5 were Russian, Swedish, Esperanto, Cebuano, and Finnish (0.26). Further, we show that over time, the contagion ratios for most common languages are growing more strongly than those of rare languages.
Key words: NLP / Sociolinguistics / Social contagion / Twitter / Signal processing
© The Author(s) 2021
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