• DocumentCode
    630128
  • Title

    Sociolect-based community detection

  • Author

    Reynolds, W.N. ; Salter, William J. ; Farber, Robert M. ; Corley, Courtney ; Dowling, Chase P. ; Beeman, William O. ; Smith-Lovin, Lynn ; Joon Nak Choih

  • Author_Institution
    Least Squares Software, Inc., Albuquerque, NM, USA
  • fYear
    2013
  • fDate
    4-7 June 2013
  • Firstpage
    221
  • Lastpage
    226
  • Abstract
    “Sociolects” are specialized vocabularies used by social subgroups defined by common interests or origins. We applied methods to retrieve large quantities of Twitter data based on expert-identified sociolects and then applied and developed network-analysis methods to relate sociolect use to network (sub-) structure. We show that novel methods including consideration of node populations, as well as edge counts, provide substantially enhanced performance compared to standard assortativity. We explain these methods, show their utility in analyzing large corpora of social media data, and d iscuss their further extensions and potential applications.
  • Keywords
    information retrieval; social networking (online); vocabulary; Sociolect-based community detection; Twitter data retrieval; edge counts; expert-identified sociolects; large social media data corpora; network structure; network substructure; network-analysis methods; node populations; social subgroups; vocabularies; Artificial neural networks; Communities; Media; Nickel; Sociology; Statistics; Twitter; assortativity; community detection; network analysis; social media analysis; sociolect;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligence and Security Informatics (ISI), 2013 IEEE International Conference on
  • Conference_Location
    Seattle, WA
  • Print_ISBN
    978-1-4673-6214-6
  • Type

    conf

  • DOI
    10.1109/ISI.2013.6578823
  • Filename
    6578823