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    Automatic Detection of Everyday Social Behaviours and Environments from Verbatim Transcripts of Daily Conversations

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    Name:
    draf-Automatic_Detection_of_So ...
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    Description:
    Final Accepted Manuscript
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    Author
    Yordanova, Kristina Y.
    Demiray, Burcu
    Mehl, Matthias R.
    Martin, Mike
    Affiliation
    Univ Arizona, Dept Psychol
    Issue Date
    2019-03
    Keywords
    social behaviour analysis
    natural language processing
    automated coding
    
    Metadata
    Show full item record
    Publisher
    IEEE
    Citation
    K. Y. Yordanova, B. Demiray, M. R. Mehl and M. Martin, "Automatic Detection of Everyday Social Behaviours and Environments from Verbatim Transcripts of Daily Conversations," 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom, Kyoto, Japan, 2019, pp. 1-10. doi: 10.1109/PERCOM.2019.8767403
    Journal
    2019 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS (PERCOM)
    Rights
    ©2019 IEEE
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Coding in social sciences is a process that involves the categorisation of qualitative or quantitative data in order to facilitate further analysis. Coding is usually a manual process that involves a lot of effort and time to produce codes with high validity and interrater reliability. Although automated methods for quantitative data analysis are largely used in social sciences, there are only a few attempts at automatically or semi-automatically coding the data collected in qualitative studies. To address this problem, in this work we propose an approach for automated coding of social behaviours and environments based on verbatim transcriptions of everyday conversations. To evaluate the approach, we analysed the transcripts from three datasets containing recordings of everyday conversations from: (1) young healthy adults (German transcriptions), (2) elderly healthy adults (German transcriptions), and (3) young healthy adults (English transcriptions). The results show that it is possible to automatically code the social behaviours and environments based on verbatim transcripts of the recorded conversations. This could reduce the time and effort researchers need to assign accurate codes to transcribed conversations.
    ISSN
    2474-2503
    DOI
    10.1109/percom.2019.8767403
    Version
    Final accepted manuscript
    Sponsors
    German Research FoundationGerman Research Foundation (DFG) [YO 226/1-1]; University of Zurich's Digital Society Initiative in the context of the DSI Fellowships and Collegium Helveticum
    ae974a485f413a2113503eed53cd6c53
    10.1109/percom.2019.8767403
    Scopus Count
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    UA Faculty Publications

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