Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification
It addresses the problem of detecting fake news online, which is an incremental improvement by focusing on sentence-level interactions not previously modeled.
The paper tackles fake news classification by modeling sentence interactions within documents, proposing a graph neural network that eliminates feature engineering and achieves state-of-the-art accuracy on existing datasets.
The rising growth of fake news and misleading information through online media outlets demands an automatic method for detecting such news articles. Of the few limited works which differentiate between trusted vs other types of news article (satire, propaganda, hoax), none of them model sentence interactions within a document. We observe an interesting pattern in the way sentences interact with each other across different kind of news articles. To capture this kind of information for long news articles, we propose a graph neural network-based model which does away with the need of feature engineering for fine grained fake news classification. Through experiments, we show that our proposed method beats strong neural baselines and achieves state-of-the-art accuracy on existing datasets. Moreover, we establish the generalizability of our model by evaluating its performance in out-of-domain scenarios. Code is available at https://github.com/MysteryVaibhav/fake_news_semantics