@inproceedings{a54802527cac437391c22e8aea2d8dc5,
title = "Automatic political profiling in heterogeneous corpora",
abstract = "In this paper we consider automatic political tendency recognition in a variety of genres. To this end, four different types of texts in Hebrew with varying levels of political content (manifestly political, semipolitical, non-political) are examined. It is found that in each case, training and testing in the same genre yields strong results. More significantly, training on political texts yields classifiers sufficiently strong to classify non-political personal Facebook pages with fair accuracy. This suggests that individuals' political tendencies can be identified without recourse to any tagged personal data.",
keywords = "Automatic profiling, Facebook, Machine learning, Politics classifying, Text classification",
author = "Hodaya Uzan and Esther David and Moshe Koppel and Maayan Geffet-Zhitomirsky",
year = "2015",
doi = "10.5220/0005270104760481",
language = "אנגלית",
series = "ICAART 2015 - 7th International Conference on Agents and Artificial Intelligence, Proceedings",
publisher = "SciTePress",
pages = "476--481",
editor = "Stephane Loiseau and Joaquim Filipe and Joaquim Filipe and Beatrice Duval and {van den Herik}, Jaap",
booktitle = "ICAART 2015 - 7th International Conference on Agents and Artificial Intelligence, Proceedings",
note = "7th International Conference on Agents and Artificial Intelligence, ICAART 2015 ; Conference date: 10-01-2015 Through 12-01-2015",
}