  Online media outlets adopt clickbait techniques to lure readers to click on
articles in a bid to expand their reach and subsequently increase revenue
through ad monetization. As the adverse effects of clickbait attract more and
more attention, researchers have started to explore machine learning techniques
to automatically detect clickbaits. Previous work on clickbait detection
assumes that all the training data is available locally during training. In
many real-world applications, however, training data is generally distributedly
stored by different parties (e.g., different parties maintain data with
different feature spaces), and the parties cannot share their data with each
other due to data privacy issues. It is challenging to build models of
high-quality federally for detecting clickbaits effectively without data
sharing. In this paper, we propose a federated training framework, which is
called federated hierarchical hybrid networks, to build clickbait detection
models, where the titles and contents are stored by different parties, whose
relationships must be exploited for clickbait detection. We empirically
demonstrate that our approach is effective by comparing our approach to the
state-of-the-art approaches using datasets from social media.
