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  1. Proceedings of the 3rd international workshop on Search and mining user-generated contents (SMUC '11)
  2. Predicting age and gender in online social networks
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The challenge of understanding the flow of sentiments in social media documents
Trend-based and reputation-versed personalized news network
Improved answer ranking in social question-answering portals
Analysis of communities in social media
Predicting age and gender in online social networks
"I'm eating a sandwich in Glasgow": modeling locations with tweets
A comparative evaluation of personality estimation algorithms for the twin recommender system
Detection of near-duplicate user generated contents: the SMS spam collection
Characterizing Wikipedia pages using edit network motif profiles
Mining tweets for tag recommendation on social media
Mining tag similarity in folksonomies
ThemeCrowds: multiresolution summaries of twitter usage
On the generation of rich content metadata from social media

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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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