Communication tools such as email facilitate communication and collaboration between speakers of different languages, who use two primary strategies—English as a common language and machine translation (MT) tools—to help them overcome language barriers. However, each of these communication strategies creates its own challenges for cross-lingual communication. In this paper, we compare how people’s interpretations of an email sender’s social intention, and their evaluation of the email and the senders, differ when using a common language versus MT in email communication. We conducted an online experiment in which monolingual native English speakers read and rated request emails written by native English speakers, emails written by bilingual Chinese speakers in English, and emails written in Chinese then machine-translated into English. We found that participants interpreted the social intentions of the email sender less accurately for machine-translated emails than for emails written by non-native speakers in English. Participants also rated the senders and emails less positively overall for machine-translated emails compared to emails written by non-native speakers in English. Based on these findings, we suggest design possibilities that could better aid multilingual communication.
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fauci-email: a json digest of Anthony Fauci's released emails
We provide a processed JSON version of the 3234 page PDF document of Anthony Fauci's emails that were released in 2021 to provide a better understanding of the United States government response to the COVID-19 pandemic. The main JSON file contains a collection of 1289 email threads with 2761 emails among the threads, which includes 101 duplicate emails. For each email, we provide information about the sender, recipients, CC-list, subject, email body text, and email time stamp (when available). We also provide a number of derived datasets stored in individual JSON files: 5 different types of derived email networks, 1 email hypergraph, 1 temporal graph, and 3 tensors. Details for the data conversion process, the construction of the derived datasets, and subsequent analyses can all be found in an online technical report at https://arxiv.org/abs/2108.01239. Updated code for processing and analyzing the data can be found at https://github.com/nveldt/fauci-email.
Research additionally supported by ARO Award W911NF-19-1-0057, ARO MURI, and NSF CAREER Award IIS-2045555, as well as NSF awards CCF-1909528, IIS-2007481, and the Sloan Foundation. more » « less- NSF-PAR ID:
- 10351793
- Publisher / Repository:
- Zenodo
- Date Published:
- Subject(s) / Keyword(s):
- Anthony Fauci email COVID-19
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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