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  Cross-Domain Learning for Classifying Propaganda in Online Contents

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.

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 Creators:
Wang, Liqiang1, Author              
Shen, Xiaoyu1, Author              
de Melo, Gerard2, Author              
Weikum, Gerhard1, Author              
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              
2External Organizations, ou_persistent22              

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Free keywords: Computer Science, Computation and Language, cs.CL
 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.

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Language(s): eng - English
 Dates: 2020-11-132020-11-222020
 Publication Status: Published online
 Pages: 11 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 2011.06844
URI: https://arxiv.org/abs/2011.06844
BibTex Citekey: Wang_2011.06844
 Degree: -

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