NDLI: Predicting age and gender in online social networks

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Predicting age and gender in online social networks

Content Provider	ACM Digital Library
Author	Peersman, Claudia Van Vaerenbergh, Leona Daelemans, Walter
Abstract	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.
Starting Page	37
Ending Page	44
Page Count	8
File Format	PDF
ISBN	9781450309493
DOI	10.1145/2065023.2065035
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-10-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Stylometry Age and gender prediction Text categorization Social media Cyber-pedophilia detection
Content Type	Text
Resource Type	Article

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