12,279 research outputs found
A Survey on Computational Propaganda Detection
Propaganda campaigns aim at influencing people's mindset with the purpose of
advancing a specific agenda. They exploit the anonymity of the Internet, the
micro-profiling ability of social networks, and the ease of automatically
creating and managing coordinated networks of accounts, to reach millions of
social network users with persuasive messages, specifically targeted to topics
each individual user is sensitive to, and ultimately influencing the outcome on
a targeted issue. In this survey, we review the state of the art on
computational propaganda detection from the perspective of Natural Language
Processing and Network Analysis, arguing about the need for combined efforts
between these communities. We further discuss current challenges and future
research directions.Comment: propaganda detection, disinformation, misinformation, fake news,
media bia
Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection
In this paper, we describe our submission to SemEval-2019 Task 4 on
Hyperpartisan News Detection. Our system relies on a variety of engineered
features originally used to detect propaganda. This is based on the assumption
that biased messages are propagandistic in the sense that they promote a
particular political cause or viewpoint. We trained a logistic regression model
with features ranging from simple bag-of-words to vocabulary richness and text
readability features. Our system achieved 72.9% accuracy on the test data that
is annotated manually and 60.8% on the test data that is annotated with distant
supervision. Additional experiments showed that significant performance
improvements can be achieved with better feature pre-processing.Comment: Hyperpartisanship, propaganda, news media, fake news, SemEval-201
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