The growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009-2020.

IF 3 2区 计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS EPJ Data Science Pub Date : 2021-01-01 DOI:10.1140/epjds/s13688-021-00271-0
Thayer Alshaabi, David Rushing Dewhurst, Joshua R Minot, Michael V Arnold, Jane L Adams, Christopher M Danforth, Peter Sheridan Dodds
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引用次数: 34

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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社交媒体的不断扩大:测量2009-2020年Twitter上150多种语言的时间和社会传染动态。
从2009年初到2019年底的1180亿条消息数据集中,我们确定并探索了推特上150多种语言的相对日常使用情况。我们发现8种语言占所有推文的80%,其中英语、日语、西班牙语、阿拉伯语和葡萄牙语占主导地位。为了量化每种语言在一段时间内的社会传播,我们计算了“传染比率”:转发与有机信息的平衡。我们发现,对于Twitter上最常见的语言,尽管不是普遍的,但越来越多的人转发而不是分享新内容。截至2019年底,包括英语和西班牙语在内的前30种语言中,有一半的传染率超过了1,即幼稚传染阈值。2019年,平均每日使用频率最高的前5种语言依次是泰语(7.3)、印地语、泰米尔语、乌尔都语和加泰罗尼亚语,而最低的5种语言是俄语、瑞典语、世界语、宿雾语和芬兰语(0.26)。此外,我们表明,随着时间的推移,大多数常见语言的传染率比稀有语言的传染率增长得更强劲。
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来源期刊
EPJ Data Science
EPJ Data Science MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -
CiteScore
6.10
自引率
5.60%
发文量
53
审稿时长
13 weeks
期刊介绍: EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.
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