TY - JOUR
T1 - Gender classification for web forums
AU - Zhang, Yulei
AU - Dang, Yan
AU - Chen, Hsinchun
N1 - Funding Information:
Manuscript received July 7, 2009; revised February 16, 2010; accepted June 18, 2010. Date of publication March 3, 2011; date of current version June 21, 2011. This work is supported in part by the National Science Foundation’s Computer and Network Systems (CNS) Program under Grant CNS-0709338. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This paper was recommended by Associate Editor C. Yang.
PY - 2011/7
Y1 - 2011/7
N2 - More and more women are participating in and exchanging opinions through community-based online social media. Questions concerning gender differences in the new media have been raised. This paper proposes a feature-based text classification framework to examine online gender differences between Web forum posters by analyzing writing styles and topics of interest. Our experiment on an Islamic women's political forum shows that feature sets containing both content-free and content-specific features perform significantly better than those consisting of only content-free features, feature selection can improve the classification results significantly, and female and male participants have significantly different topics of interest.
AB - More and more women are participating in and exchanging opinions through community-based online social media. Questions concerning gender differences in the new media have been raised. This paper proposes a feature-based text classification framework to examine online gender differences between Web forum posters by analyzing writing styles and topics of interest. Our experiment on an Islamic women's political forum shows that feature sets containing both content-free and content-specific features perform significantly better than those consisting of only content-free features, feature selection can improve the classification results significantly, and female and male participants have significantly different topics of interest.
KW - Gender classification
KW - online gender differences
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U2 - 10.1109/TSMCA.2010.2093886
DO - 10.1109/TSMCA.2010.2093886
M3 - Article
AN - SCOPUS:79959627244
SN - 1083-4427
VL - 41
SP - 668
EP - 677
JO - IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
JF - IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
IS - 4
M1 - 5723017
ER -