The Eyes of the Beholder: Gender Prediction Using Images Posted in Online Social Networks

Identifying user attributes from their social media activities has been an active research topic. The ability to predict user attributes such as age, gender, and interests from their social media activities is essential for personalization and recommender systems. Most of the techniques proposed for this purpose utilize the textual content created by a user, while multimedia content has gained popularity in social networks. In this paper, we propose a novel algorithm to infer a user's gender by using the images posted by the user on different social networks.

[1]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[2]  David Bamman,et al.  Gender identity and lexical variation in social media , 2012, 1210.4567.

[3]  Faiyaz Al Zamal,et al.  Using Social Media to Infer Gender Composition of Commuter Populations , 2012, Proceedings of the International AAAI Conference on Web and Social Media.

[4]  Alessandro Perina,et al.  Unveiling the multimedia unconscious: implicit cognitive processes and multimedia content analysis , 2013, ACM Multimedia.

[5]  Nicu Sebe,et al.  Faved! Biometrics: Tell Me Which Image You Like and I'll Tell You Who You Are , 2014, IEEE Transactions on Information Forensics and Security.

[6]  Philip S. Yu,et al.  Empirical Evaluation of Profile Characteristics for Gender Classification on Twitter , 2013, 2013 12th International Conference on Machine Learning and Applications.

[7]  Ming-Hsuan Yang,et al.  Gender classification with support vector machines , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[8]  Nicu Sebe,et al.  We like it! Mapping image preferences on the counting grid , 2013, 2013 IEEE International Conference on Image Processing.

[9]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[10]  Nicu Sebe,et al.  Tell Me What You Like and I'll Tell You What You Are: Discriminating Visual Preferences on Flickr Data , 2012, ACCV.

[11]  Margaret L. Kern,et al.  Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach , 2013, PloS one.

[12]  R. Manmatha,et al.  Predicting retweet count using visual cues , 2013, CIKM.

[13]  David Yarowsky,et al.  Classifying latent user attributes in twitter , 2010, SMUC '10.

[14]  Sudeshna Sarkar,et al.  Stylometric Analysis of Bloggers' Age and Gender , 2009, ICWSM.

[15]  Wendy Liu,et al.  Homophily and Latent Attribute Inference: Inferring Latent Attributes of Twitter Users from Neighbors , 2012, ICWSM.

[16]  Shlomo Argamon,et al.  Effects of Age and Gender on Blogging , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[17]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[18]  John D. Burger,et al.  Discriminating Gender on Twitter , 2011, EMNLP.

[19]  Xiang Yan,et al.  Gender Classification of Weblog Authors , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[20]  Roope Raisamo,et al.  Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Sushil J. Louis,et al.  Genetic feature subset selection for gender classification: a comparison study , 2002, Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings..