English is the international standard of social research, but scholars are
increasingly conscious of their responsibility to meet the need for scholarly
insight into communication processes globally. This tension is as true in
computational methods as any other area, with revolutionary advances in the
tools for English language texts leaving most other languages far behind. In
this paper, we aim to leverage those very advances to demonstrate that
multi-language analysis is currently accessible to all computational scholars.
We show that English-trained measures computed after translation to English
have adequate-to-excellent accuracy compared to source-language measures
computed on original texts. We show this for three major analytics -- sentiment
analysis, topic analysis, and word embeddings -- over 16 languages, including
Spanish, Chinese, Hindi, and Arabic. We validate this claim by comparing
predictions on original language tweets and their backtranslations: double
translations from their source language to English and back to the source
language. Overall, our results suggest that Google Translate, a simple and
widely accessible tool, is effective in preserving semantic content across
languages and methods. Modern machine translation can thus help computational
scholars make more inclusive and general claims about human communication.

本文研究通过机器翻译将其他语言翻译成英文进行多语言文本分析，结果表明这种方法在情感分析，主题分析和词嵌入等分析方法上，与使用源语言进行分析的结果相当，机器翻译可以帮助计算机学者做出关于人类沟通的更具包容性和普适性的结论。