<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peersman, Claudia</style></author><author><style face="normal" font="default" size="100%">Walter Daelemans</style></author><author><style face="normal" font="default" size="100%">Van Vaerenbergh, Leona</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Age and Gender Prediction on Netlog Data</style></title><secondary-title><style face="normal" font="default" size="100%">ATILA MEETING, Ostend (Belgium)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">23-09-2010</style></date></pub-dates></dates><abstract><style face="normal" font="default" size="100%">In recent years millions of people have started using social networking sites such as Netlog to support their personal and professional communications, creating digital communities. However, a common characteristic of these digital communities is that users can easily provide a false name, age, gender and location in order to hide their true identity. This way, social networking sites can be used by people with criminal intentions (e.g., paedophiles) to support their activities on-line. In the context of the DAPHNE project (Defending Against Paedophiles in Heterogeneous Network Environments), we present first results of a  machine learning approach for age and gender prediction on a corpus of posts on the social network site Netlog. We investigate which types of linguistic and stylistic features are effective for age and gender prediction, given the specific characteristics of (the Dutch) chat language and compare the effectiveness of different machine learning techniques for age and gender prediction on the Netlog data. We will conclude our presentation by discussing how these results will guide future research in the DAPHNE project.
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