DocumentCode :
119520
Title :
Emoticons and linguistic alignment: How visual analytics can elicit storytelling
Author :
Nan-Chen Chen ; Feldman, Laurie Beth ; Kroll, Judith F. ; Aragon, Cecilia R.
Author_Institution :
Univ. of Washington, Seattle, WA, USA
fYear :
2014
fDate :
25-31 Oct. 2014
Firstpage :
237
Lastpage :
238
Abstract :
Socio-emotional communication is a critical determining factor in the cohesiveness of international work teams. In recent years, online text communication (e.g., chat, forums, email) has been widely used in cross-cultural collaborations, and emoticons are often viewed as socio-emotional cues in this type of communication. Therefore, it is important to know how emoticons work in online text communication. One way to investigate this topic is to leverage theories in sociolinguistics to find potential mappings between emoticon use and face-to-face language use. In the present work, we propose a visual analytics tool to explore emoticon use among different groups over time and show how visual analytics can elicit storytelling in studying linguistic alignment of emoticons in a chat log dataset from a four-year scientific collaboration between France and the United States.
Keywords :
data analysis; data visualisation; linguistics; France; United States; chat log dataset; cross-cultural collaboration; emoticon use; face-to-face language use; linguistic alignment; online text communication; socio-emotional communication; socio-emotional cue; sociolinguistics; storytelling; visual analytics; Collaboration; Educational institutions; Nose; Pragmatics; Prototypes; Testing; Visual analytics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Visual Analytics Science and Technology (VAST), 2014 IEEE Conference on
Conference_Location :
Paris
Type :
conf
DOI :
10.1109/VAST.2014.7042508
Filename :
7042508
Link To Document :
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