ArticleEPJ data science2021
The growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009-2020.
Article in EPJ data science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Acute respiratory infections prediction through social media data and machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Traditional, Complementary, and Integrative Medicine on X (Formerly Twitter) From 2015 to 2024 Across English, Spanish, and French: Retrospective Observational Study.Journal of medical Internet research · 2026Observational
- Ousiometrics: The essence of meaning aligns with a power-danger-structure framework instead of valence-arousal-dominance.Science advances · 2026Article
- Analysing lithium, quetiapine and valproic acid on social media: an infodemiology study.International journal of bipolar disorders · 2025Article
- Unveiling Social Media Content Related to ADHD Treatment: Machine Learning Study Using X's Posts over 15 Years.Healthcare (Basel, Switzerland) · 2025Article
- A perspective on friction interventions to curb the spread of misinformation.Npj complexity · 2025Review
- Quantifying the vulnerabilities of the online public square to adversarial manipulation tactics.PNAS nexus · 2024Article
- A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM.Scientific reports · 2024Article
- Acupuncture Topics on Twitter (Currently X) in English and Japanese: Co-occurrence Network Analysis.Cureus · 2024Article
- Sentiment analysis of medical record notes for lung cancer patients at the Department of Veterans Affairs.PloS one · 2023Article
- Article
- Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study.JMIR mental health · 2022Article
- Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter.Science advances · 2021Article
- "COVID19 is_": The Perpetuation of Coronavirus Conspiracy Theories via Google Autocomplete.Proceedings of the Association for Information Science and Technology. Association for Information Science and Technology · 2021Article
- How the world's collective attention is being paid to a pandemic: COVID-19 related n-gram time series for 24 languages on Twitter.PloS one · 2021Article
- Article
- Hurricanes and hashtags: Characterizing online collective attention for natural disasters.PloS one · 2021Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.