Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification

Vaibhav Vaibhav, Raghuram Mandyam, Eduard Hovy


Abstract
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.
Anthology ID:
D19-5316
Volume:
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)
Month:
November
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | TextGraphs | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
134–139
Language:
URL:
https://aclanthology.org/D19-5316
DOI:
10.18653/v1/D19-5316
Bibkey:
Cite (ACL):
Vaibhav Vaibhav, Raghuram Mandyam, and Eduard Hovy. 2019. Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification. In Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13), pages 134–139, Hong Kong. Association for Computational Linguistics.
Cite (Informal):
Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification (Vaibhav et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-5316.pdf
Attachment:
 D19-5316.Attachment.pdf
Code
 MysteryVaibhav/fake_news_semantics