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News articles along with other related components like news creators and news subjects can be modeled as a heterogeneous information network (HIN for short).
In this work, we propose a mechanism to detect fake news through a classifier based on weighted causal graphs.
In addition, we build a shared CNN to extract the low level features on both labeled data and unlabeled data to feed them into these two paths.
Nowadays, Internet is a primary source of attaining health information.
Thus, our results motivate the need for designing training techniques that are robust to unintended feature learning, specifically for transfer learned models.
Defined as the intentional or unintentionalspread of false information (K et al., 2019)through context and/or content manipulation, fake news has become one of the most seriousproblems associated with online information(Waldrop, 2017).
Recently, neural networks based on multi-task learning have achieved promising performance on fake news detection, which focuses on learning shared features among tasks as complementarity features to serve different tasks.