  In this paper, we propose a method that efficiently utilizes appearance
features and text vectors to accurately classify political posters from other
similar political images. The majority of this work focuses on political
posters that are designed to serve as a promotion of a certain political event,
and the automated identification of which can lead to the generation of
detailed statistics and meets the judgment needs in a variety of areas.
Starting with a comprehensive keyword list for politicians and political
events, we curate for the first time an effective and practical political
poster dataset containing 13K human-labeled political images, including 3K
political posters that explicitly support a movement or a campaign. Second, we
make a thorough case study for this dataset and analyze common patterns and
outliers of political posters. Finally, we propose a model that combines the
power of both appearance and text information to classify political posters
with significantly high accuracy.
