• 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