The impact of language and systemic factors on tweeted countries of the world

Abstract

This study is intended to unveil the difference of social mediated world via major languages and investigates the volume of tweets individual countries received during 2015–2016 in nine languages –Arabic, Chinese, English, French, German, Japanese, Portuguese, Russian, and Spanish. Shared language, country attributes, economic power, and communication resources were used in predicting country mention. The salient countries on Twitter overall are vastly diverse and vary from language to language. Based on cluster analysis, English and Japanese tweets distinguish themselves from other languages; yet the result from rank-order correlation test shows Arabic and French tweets treat countries differently from the rest. Core nations are still covered more in English- and French-language tweets. Shared language factor is found to predict well for tweets in Chinese, Arabic, Spanish, French, and German but not in English and Portuguese.