The Case for Latent Variable Vs Deep Learning Methods in Misinformation Detection: An Application to COVID-19
24th International Conference on Discovery Science, DS 2021
; 12986 LNAI:422-432, 2021.
Article
in English
| Scopus | ID: covidwho-1499373
ABSTRACT
The detection and removal of misinformation from social media during high impact events, e.g., COVID-19 pandemic, is a sensitive application since the agency in charge of this process must ensure that no unwarranted actions are taken. This suggests that any automated system used for this process must display both high prediction accuracy as well as high explainability. Although Deep Learning methods have shown remarkable prediction accuracy, accessing the contextual information that Deep Learning-based representations carry is a significant challenge. In this paper, we propose a data-driven solution that is based on a popular latent variable model called Independent Component Analysis (ICA), where a slight loss in accuracy with respect to a BERT model is compensated by interpretable contextual representations. Our proposed solution provides direct interpretability without affecting the computational complexity of the model and without designing a separate system. We carry this study on a novel labeled COVID-19 Twitter dataset that is based on socio-linguistic criteria and show that our model’s explanations highly correlate with humans’ reasoning. © 2021, Springer Nature Switzerland AG.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
24th International Conference on Discovery Science, DS 2021
Year:
2021
Document Type:
Article
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