The purpose of this study is to examine how tweeters drawn from three different languages and cultural boundaries manage the lack of contextual cues through an analysis of Arabic, English and Korean tweets.
Data for this study is drawn from a corpus of tweets (n = 1,200) streamed using Python through Twitter API. Using the language information, the authors limited the number of tweets to 400 randomly selected tweets from each language, totaling 1,200 tweets. Final coding taxonomy was derived through interactive processes preceded by literature and a preliminary analysis based on a small subset (n = 150) by isolating nonverbal communication devices and emoticons.
The results of the study present that there is great commonality across these tweets in terms of strategies and creativity in compensating for the constraints imposed by the tweet platform. The language-specific characteristics are also shown in the form of different usage of devices.
Emoticon usage indicates that the communication mode influences online social interaction; the restriction of 140 maximum characters seems to engender a frequent usage of emoticons across tweets regardless of language differences. The results of the study bring forth implications into the design of social media technologies that reflect affective aspects of communication and language-/culture-specific traits and characteristics.
To the best of the authors’ knowledge, there are no qualitative studies examining paralinguistic nonverbal communication cues in the Twitter platform across language boundaries.
Park, J.R. and El Mimouni, H. (2020), "Emoticons and non-verbal communications across Arabic, English, and Korean Tweets", Global Knowledge, Memory and Communication, Vol. 69 No. 8/9, pp. 579-595. https://doi.org/10.1108/GKMC-02-2020-0021
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