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