In this paper, we extend existing work on latent attribute inference by leveraging the principle of homophily: we evaluate the inference accuracy gained by augmenting the user features with features derived from the Twitter profiles and postings of her friends. We consider three attributes which have varying degrees of assortativity: gender, age, and political affiliation. Our approach yields a significant and robust increase in accuracy for both age and political affiliation, indicating that our approach boosts performance for attributes with moderate to high assortativity. Furthermore, different neighborhood subsets yielded optimal performance for different attributes, suggesting that different subsamples of the user's neighborhood characterize different aspects of the user herself. Finally, inferences using only the features of a user's neighbors outperformed those based on the user's features alone. This suggests that the neighborhood context alone carries substantial information about the user.

https://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/download/4713/5013

Homophily and Latent Attribute Inference: Inferring Latent Attributes of Twitter Users from Neighbors

In this paper we focus on the connection between age and language use, exploring age prediction of Twitter users based on their tweets. We discuss the construction of a fine-grained annotation effort to assign ages and life stages to Twitter users. Using this dataset, we explore age prediction in three different ways: classifying users into age categories, by life stages, and predicting their exact age. We find that an automatic system achieves better performance than humans on these tasks and that both humans and the automatic systems have difficulties predicting the age of older people. Moreover, we present a detailed analysis of variables that change with age. We find strong patterns of change, and that most changes occur at young ages.

/pdf/how-old-do-you-think-i-am-a-study-of-language-and-age-in-4ntxd4z1zw.pdf

"How Old Do You Think I Am?" A Study of Language and Age in Twitter

The PAN task on author profiling has been organised in the
framework of the WIQ-EI IRSES project (Grant No. 269180) within the FP 7 Marie
Curie People Framework of the European Commission. We would like to thank Atribus
by Corex for sponsoring the award for the winner team. We thank Julio Gonzalo,
Jorge Carrillo and Damiano Spina from UNED for helping with the Twitter subcorpus.
The work of the first author was partially funded by Autoritas Consulting SA and
by Ministerio de Economia y Competitividad de Espana under grant ECOPORTUNITY
IPT-2012-1220-430000 and CSO2013-43054-R. The work of the second author was in
the framework the DIANA-APPLICATIONS-Finding Hidden Knowledge in Texts: Applications
(TIN2012-38603-C02-01) project, and the VLC/CAMPUS Microcluster on
Multimodal Interaction in Intelligent Systems.

/pdf/overview-of-the-2nd-author-profiling-task-at-pan-2014-8filhhi5zp.pdf

Overview of the 2nd Author Profiling Task at PAN 2014

A common characteristic of communication on online social networks is that it happens via short messages, often using non-standard language variations. These characteristics make this type of text a challenging text genre for natural language processing. Moreover, in these digital communities it is easy to provide a false name, age, gender and location in order to hide one's true identity, providing criminals such as pedophiles with new possibilities to groom their victims. It would therefore be useful if user profiles can be checked on the basis of text analysis, and false profiles flagged for monitoring. This paper presents an exploratory study in which we apply a text categorization approach for the prediction of age and gender on a corpus of chat texts, which we collected from the Belgian social networking site Netlog. We examine which types of features are most informative for a reliable prediction of age and gender on this difficult text type and perform experiments with different data set sizes in order to acquire more insight into the minimum data size requirements for this task.

/pdf/predicting-age-and-gender-in-online-social-networks-kqa2uttw4n.pdf

Predicting age and gender in online social networks

Demographic lexica have potential for widespread use in social science, economic, and business applications. We derive predictive lexica (words and weights) for age and gender using regression and classification models from word usage in Facebook, blog, and Twitter data with associated demographic labels. The lexica, made publicly available,1 achieved state-of-the-art accuracy in language based age and gender prediction over Facebook and Twitter, and were evaluated for generalization across social media genres as well as in limited message situations.

/pdf/developing-age-and-gender-predictive-lexica-over-social-2ppnu60bo1.pdf

Developing Age and Gender Predictive Lexica over Social Media

We report results of stylometric differences in blogging for gender and age group variation. The results are based on two mutually independent features. The first feature is the use of slang words which is a new concept proposed by us for Stylometric study of bloggers. Slang is a non-dictionary word that has evolved with time due to its frequent and popular usage. For the second feature, we have analysed the variation in average length of sentences across various age groups and gender. These two features are then augmented with previous study results reported in literature for stylometric analysis of age and gender. The combined feature list enhances the accuracy by a remarkable extent in predicting age and gender. These experiments were done on a 20,000 blog corpus. Experimental results show that these features work well in detection of bloggers’ demography. However, gender determination is more accurate than age group detection over a data spread across all ages but the accuracy of age prediction increases if we sample data with remarkable age difference.

Stylometric Analysis of Bloggers’ Age and Gender

We report results of stylistic differences in blogging for gender and age group variation. The results are based on two mutually independent features. The first feature is the use of slang words which is a new concept proposed by us for Stylistic study of bloggers. For the second feature, we have analyzed the variation in average length of sentences across various age groups and gender. These features are augmented with previous study results reported in literature for stylistic analysis. The combined feature list enhances the accuracy by a remarkable extent in predicting age and gender. These machine learning experiments were done on two separate demographically tagged blog corpus. Gender determination is more accurate than age group detection over the data spread across all ages but the accuracy of age prediction increases if we sample data with remarkable age difference.

/pdf/learning-age-and-gender-of-blogger-from-stylistic-variation-4hst911jmh.pdf

Mayur Rustagi

Papers

Stylometric Analysis of Bloggers’ Age and Gender

Learning Age and Gender of Blogger from Stylistic Variation