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
Link To Document :
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