English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Paper

Cross-Domain Learning for Classifying Propaganda in Online Contents

MPS-Authors
/persons/resource/persons231512

Wang,  Liqiang
Databases and Information Systems, MPI for Informatics, Max Planck Society;

/persons/resource/persons227243

Shen,  Xiaoyu
Databases and Information Systems, MPI for Informatics, Max Planck Society;

/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (public)

arXiv:2011.06844.pdf
(Preprint), 469KB

Supplementary Material (public)
There is no public supplementary material available
Citation

Wang, L., Shen, X., de Melo, G., & Weikum, G. (2020). Cross-Domain Learning for Classifying Propaganda in Online Contents. Retrieved from https://arxiv.org/abs/2011.06844.


Cite as: http://hdl.handle.net/21.11116/0000-0007-FEBF-5
Abstract
As news and social media exhibit an increasing amount of manipulative polarized content, detecting such propaganda has received attention as a new task for content analysis. Prior work has focused on supervised learning with training data from the same domain. However, as propaganda can be subtle and keeps evolving, manual identification and proper labeling are very demanding. As a consequence, training data is a major bottleneck. In this paper, we tackle this bottleneck and present an approach to leverage cross-domain learning, based on labeled documents and sentences from news and tweets, as well as political speeches with a clear difference in their degrees of being propagandistic. We devise informative features and build various classifiers for propaganda labeling, using cross-domain learning. Our experiments demonstrate the usefulness of this approach, and identify difficulties and limitations in various configurations of sources and targets for the transfer step. We further analyze the influence of various features, and characterize salient indicators of propaganda.