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Social media has greatly enabled people to participate in online activities at an unprecedented rate.
In this dissertation, we analyse the state-of-the-art in pair-wise trust prediction models in OSNs.
Current studies have made significant contributions to predicting fake news with less focus on exploiting the relationship (similarity) between the textual and visual information in news articles.
This is a paper for exploring various different models aiming at developing fake news detection models and we had used certain machine learning algorithms and we had used pretrained algorithms such as TFIDF and CV and W2V as features for processing textual data.
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.